Assessing the impact of historical operational data from complex assets on predictive maintenance models

Assessing the Impact of Historical Operational Data

from Complex Assets on Predictive Maintenance Models

by

Brian Gabriel Gaudio

B.S. Mechanical Engineering with an Additional Major in Engineering and Public Policy

Carnegie Mellon University, 2010

Submitted to the MIT Sloan School of Management and the Department of Mechanical

Engineering in Partial Fulfillment of the Requirements for the Degrees

of

Master of Business Administration and

Master of Science in Mechanical Engineering

in conjunction with the Leaders for Global Operations Program

at the

Massachusetts Institute of Technology

May 2020

©

Brian Gabriel Gaudio. All rights reserved.

Signature of Author ...

Brian Gabriel Gaudio

MIT Sloan School of Management

Department of Mechanical Engineering

May

8, 2020

Certified by ...

David E. Hardt, Thesis Supervisor

Professor of Mechanical Engineering; Ralph E. and Eloise F. Cross Professor in

Manufacturing

Certified by ...

Roy E. Welsch, Thesis Supervisor

Eastman Kodak Leaders for Global Operations Professor of Management

Professor of Statistics and Data Science

Accepted by ...

Nicolas Hadjiconstantinou

Chair, Mechanical Engineering Committee on Graduate Students

Accepted by ...

Maura Herson

Assistant Dean, MBA Program, MIT Sloan School of Management

Assessing the Impact of Historical Operational Data

from Complex Assets on Predictive Maintenance Models

by

Brian Gabriel Gaudio

Submitted to the Department of Mechanical Engineering and the MIT Sloan School of Management on May 8, 2020, in partial fulfillment of the requirements for the degrees of Master

of Business Administration and

Master of Science in Mechanical Engineering

Abstract

Over the past one hundred years, maintenance concepts have evolved from a simple “fix when broken” approach to advanced prognostic methods used today that leverage large

amounts of historical, operational, and primary sensor data to predict when and how failures will occur. For firms that produce complex assets, the ability to predict with accuracy when

maintenance overhauls should occur can provide both an operational and economic competitive advantage. This research evaluates the hypothesis that the accuracy of predictive maintenance models for complex assets can be improved with the addition of historical operational data and failure modes can be more clearly identified by examining primary sensor data.

This hypothesis is tested through data analysis on predictive maintenance models used by commercial turbofan jet engines. Because some engines have operated for decades, their entire operational records are not in the appropriate digital format and not utilized by current models. This research identifies alternate, available sources of this data.

The additional data sources were processed and incorporated into the existing predictive maintenance models. The addition of the operational data sources did not reduce the error in the model used to forecast the useful life of assets for preventative maintenance, which suggests that the current coverage provided by existing data is sufficient. The examination of primary sensor data isolated one component that displayed age-related degradation and maintenance costs.

Thesis Supervisor: David E. Hardt

Title: Professor of Mechanical Engineering; Ralph E. and Eloise F. Cross Professor in Manufacturing

Acknowledgements

First, I would like to thank all of my colleagues at Pratt & Whitney that helped to make my internship and this thesis such a great experience. In the years to come, I will look back fondly on the six months I spent in Connecticut. To Travis Gracewski, thank you so much for your guidance, support, and advice throughout the project. You made East Hartford such an easy place to work while introducing me to other parts of Pratt & Whitney (along with the

occasional boat ride on the Connecticut River). To my internship supervisors, Rohan Mehta and Jason Rhodes, a big thank you for your support, insight, and patience during my time at Pratt and for opening up the inner workings of the Engine Services team. Thanks also to Gene Holtsinger and John Harrington for letting me explore in-depth the other teams that are a part of Engine Services.

At MIT, I would like to thank my thesis advisors, Roy Welsch and Dave Hardt. Your wisdom and support throughout this project have been much appreciated. Thank you for keeping me sane for the duration of the internship and during the writing of this thesis. I would not have been able to do this without your guidance.

I would also like to thank my LGO 2020 classmates for making my two-years at MIT some of the most enjoyable in my life. I have learned so much from all of you, and the memories we have made will last a lifetime. Completing this internship and thesis would not have been possible without all of you. Extra thanks are in order for the other 2020 interns at Pratt & Whitney that made living and working in Connecticut so much fun.

To my past colleagues at Westinghouse and Toshiba, my journey to MIT would not have been possible without everything I have learned from all of you. Thank you again so much.

Finally, to my parents, Pam and Vince, and my brother, Greg, I would not be at MIT and LGO if were not for my family. Thank you for so much love over the years.

Table of Contents

1.1 Project Origin and Need ... 18

1.2 Problem Statement and Approach ... 19

1.3 Thesis Organization ... 20

2 Established Maintenance Concepts ... 21

2.1 Maintenance Overview and Nomenclature ... 21

2.2 First and Second-Generation Maintenance Concepts ... 24

2.3 Third Generation Maintenance Management Concepts ... 25

2.4 Reliability Centered Maintenance (RCM) ... 26

2.5 Failure Modes and Effects Analysis (FMEA) ... 33

2.6 Preventive and Predictive Maintenance (PPM) ... 37

2.6.1 Preventive Maintenance ... 37

2.6.2 Predictive Maintenance (PdM) and Condition Monitoring ... 40

3 Case Examples of Predictive Maintenance ... 46

3.1 Predictive Maintenance for Light Emitting Diodes ... 46

3.2 Predictive Maintenance for Railway Infrastructure ... 49

3.3 Current Maintenance Practices and Predictive Methods for Turbofan Engines ... 52

3.3.1 Critical Turbofan Components and Failure Modes ... 52

3.3.2 Current Turbofan Maintenance Practices ... 54

3.3.3 Engine Shop Visits Patterns and Shop Visit Rates ... 56

3.3.4 Managing Maintenance with Flight Hour Agreements ... 60

3.3.5 Exhaust Gas Temperature ... 63

3.4 Current Methods for Predicting Time on Wing and Shop Visit Costs ... 65

4 Current Data Collection Approaches ... 68

4.1 Shop Visit Cost Data ... 68

4.2 Engine Operational Data ... 69

4.3 Other Environmental Data ... 70

4.4 Operational Data Coverage at an FHA Provider ... 71

5 Filling in Gaps in Engine Flight History ... 74

5.1 Aircraft Registration Numbers ... 74

5.2 Commercial Third-Party Flight Data Providers ... 76

5.3 US Department of Transportation Bureau of Transportation Statistics Data Set ... 82

5.4 Structure ... 82

5.4.2 BTS Data Limitations and Discrepancies ... 86

6 Augmenting Existing Preventative Maintenance Models with Additional Flight History ... 87

6.1 Merging ADEM and BTS Datasets ... 87

6.2 Number of Flights Added ... 89

6.3 Results of Additional Flights on Existing Cost and Interval Models ... 94

7 Using EGT Margin for Condition Monitoring and Estimating Maintenance Costs ... 95

7.1 Examining Engines with Shared Flight History for Differences in Maintenance Costs . 95 7.2 Using EGT to Measure Asset Operation Time ... 100

8 Conclusions and Future Work ... 108

8.1 Future Work ... 108

Bibliography ... 110

Appendix A: Pratt & Whitney History ... 116

Appendix B: Physics of Commercial Jet Engines ... 118

List of Figures

Figure 2-1: The complex linking of technical and commercial issues in maintenance concepts 23

Figure 2-2: The strategic, tactical, and operational levels of maintenance management ... 24

Figure 2-3: The progression of maintenance expectations and techniques from 1940 through 2000 ... 26

Figure 2-4: A traditional view of when failures occur: a period of useful life leading to a distinct failure zone ... 29

Figure 2-5: Six common failure patterns of operating time versus conditional probability of failure ... 31

Figure 2-6: A process flow diagram of the risk assessment phase of FMEA ... 35

Figure 2-7: Setting of safe-life limit in relation to failure occurrence for scheduled discard tasks ... 39

Figure 2-8: The potential failure to failure curve demonstrates when predictive maintenance occurs ... 41

Figure 2-9: Inconsistent P-F intervals across the same assets, which leads to variability ... 42

Figure 3-1: Example of LED lumen maintenance life prediction comparing standard TM-21 method with PF method ... 48

Figure 3-2: A comparison of real, TM-21, and PF LED remaining useful life predictions ... 49

Figure 3-3: Process for rail wear prediction using track data, rolling stock data, and modeling . 51 Figure 3-4: Prediction of outer rail wear area for both new and worn rolling stock wheels ... 52

Figure 3-5: A process diagram of gas path analysis – monitor physical faults by observing changes in component performance ... 54

Figure 3-6: A comparison of flight profiles with respect to flight hours and flight cycles ... 58

Figure 3-7: Stabilization of shop visit rate over the lifecycle of a family of engines ... 59

Figure 3-9: FHA Policy optimized for minimum number of SVs ... 62

Figure 3-10: A visualization of EGT and EGT margin ... 64

Figure 3-11: The effects of deterioration on EGT margin over time ... 65

Figure 4-1: A survey of engine flight records not utilized by current models; most histories are not utilizing more than half of their flights ... 72

Figure 5-1: Aircraft registration number displayed on the tail of an airplane ... 75

Figure 5-2: Example FlightAware radar information for Boston Logan International Airport ... 78

Figure 5-3: Example FlightAware in-flight data for Flight 1423 on registered aircraft N900UW .. 79

Figure 5-4: Example FlightAware flight history for registered aircraft N537JT ... 80

Figure 5-5: Example FlightAware N900UW registration information ... 81

Figure 5-6: A sample of collected BTS flight data ... 84

Figure 6-1: Histogram of Extra Flights Added for Carrier A ... 91

Figure 6-2: Carrier A percent coverage after additional flights ... 91

Figure 6-3: Histogram of Extra Flights Added for Carrier B ... 92

Figure 6-4: Carrier B percent coverage after additional flights ... 92

Figure 6-5: Histogram of Extra Flights Added for Carrier C ... 93

Figure 6-6: Carrier B percent coverage after additional flights ... 93

Figure 7-1: Paired Engines –Difference in Total Shop Visit Cost Compared to Differences in EFCs ... 98

Figure 7-2: Paired Engines - Module Shop Visit Cost Compared to EFHs ... 99

Figure 7-3: Raw EGT Margin Data from a Pair of Engines on the Same Aircraft ... 102

Figure 7-4: Smoothed EGT Margin Data from a Pair of Engines on the Same Aircraft ... 103

Figure 7-5: Quantifying EGT margin consumption by measuring the area under the EGT margin curve ... 104

Figure B-1: A classification of types of gas turbine engines ... 119

Figure B-2: Sub-classifications of turbofan jet engines ... 120

Figure B-3: Jet Engine Brayton Cycle Schematic ... 121

Figure B-4: Ideal Brayton Cycle Diagrams ... 122

Figure B-5: Steps of the Brayton Cycle mapped to major turbofan components ... 123

List of Tables

Table 1: Occurrence failure rates and indices for FMEA risk assessment [6]... 35

Table 2: Severity criteria and indices for FMEA risk assessment [6] ... 36

Table 3: Detection criteria and indices for FMEA risk assessment [6] ... 36

Table 4: A comparison of SV costs per EFH for different FHA policies ... 62

Table 5: Summary of Added Flights for Carriers A, B, and C ... 90

Table 6: Summary of Interval Model Root Mean Squared Error with Additional Flights ... 94

List of Acronyms

AAC Airline administrative communications

ACARS Aircraft communications addressing and reporting system ADEM Advanced diagnostics and engine management

ADS-B Automatic dependent surveillance-broadcast AMM Aircraft maintenance manual

AOC Airline operational communications ASDI Aircraft Situation Display to Industry ATS Air traffic service

BTS Bureau of Transportation Statistics

CFR United States Code of Federal Regulations

COMB Combustor

CRS Central reservation system

CSV Comma separated value

EFC Engine flight cycle

EFH Engine flight hour

EGT Exhaust gas temperature

EHM Engine health monitoring FAA Federal Aviation Administration FADEC Full authority digital engine control

FHA Flight hour agreement

FMEA Failure modes and effects analysis

FOD Foreign object damage

GPA Gas path analysis

HPC High pressure compressor

HPT High pressure turbine

HS Hot Section

HSR Hot section repair

IATA International Air Transport Association ICAO International Civil Aviation Organization ISO International Organization for Standardization LED Light emitting diode

LLP Life limited part

LM Lumen maintenance life

LPC Low pressure compressor

LPT Low pressure turbine

MAR Missing at random

MCAR Missing completely at random

MISR Multi-angle imaging spectroradiometer MNAR Missing not at random

MRO Maintenance repair and overhaul

NASA National Aeronautics and Space Administration OAT Outside air temperature

OEM Original equipment manufacturer

PdM Predictive maintenance

PF Particle filtering

PM Preventive maintenance

PPM Preventive and predictive maintenance RCM Reliability centered maintenance

RMSE Root mean square error

RMT Role matrix technique

RPN Risk priority number

RUL Remaining useful life

SPC Statistical process control

SV Shop visit

SVR Shop visit rate

TBO Time between overhauls

TOW Time on wing

Chapter 1

1

Introduction

ISO 55000 defines an asset as, “an item, thing, or entity that has potential or actual value to an organization.” The same standard defines asset management as, “the coordinated activity of an organization to realize value from assets.” In fact, the integrated, risk-based management of all industrial infrastructure falls under the definition of asset management. In general across all sectors, the costs of maintaining physical assets represent 5%-12% of the total capital invested, up to 15% of the gross sales, and up to 10% of the production costs of an asset [1]. The ability to predict and forecast when maintenance will occur can be of great operational and economic benefit to certain organizations.

1.1

Project Origin and Need

Maintenance practices have developed over the last century from simple “fix when broken” heuristics to a formal field of engineering and science. New methods to implement and manage asset maintenance continue to be developed. Today, prognostics and condition-based maintenance leverage data and models to predict when and how assets will fail, which allows firms to predict when their assets should be maintained.

Over the same time period, assets have become more complex. Assets are no longer simple pieces of industrial equipment. Assets are collections of thousands of parts, components, and systems that must operate reliably for many years in harsh environments. The addition of sensors and instrumentation to their control systems has further increased their complexity, but

it has also resulted in the generation of large amounts of data related to the operation of these assets.

Today, firms sit on a trove of both historical and rapidly accumulating asset data that is just beginning to be utilized to its full potential. Often times, firms do not realize that this data is available, or the data has not been converted to or process in the appropriate digital format. These data sets are often incomplete and do not contain every record of all of the operating conditions of an asset.

Firms constantly face pressures to reduce costs, which includes maintenance costs. Firms that produce and maintain complex assets stand to benefit the most by harnessing their existing asset data to predict more accurately how assets will fail and when maintenance should be performed. Extending the analysis across thousands of similar assets would allow a firm to forecast their maintenance capacity and maintenance strategies accordingly.

1.2

Problem Statement and Approach

Firms must begin to fully utilize the vast amounts of asset data that are available for predictive maintenance activities. This thesis hypothesizes that the accuracy of predictive maintenance models of complex assets will improve with the addition of new historical operational data sources that are able to supply previously missing observations and that the inclusion and examination of primary sensor data in these models will reveal age-based failure modes and sources of maintenance cost.

This hypothesis will be tested first by documenting the current maintenance strategy, tasks, and data collection efforts for a fleet of complex assets, specifically commercial turbofan jet engines. Second, current predictive maintenance models will be evaluated for their function, accuracy, and use of existing data sources. Third, new data sources both internal to a firm and external, publicly available sources will be surveyed and evaluated for the types of data that they offer. Finally, data will be collected from these new sources, processed, and included in

existing predictive maintenance models to measure the accuracy with which they can forecast the useful life of the assets.

1.3

Thesis Organization

This thesis will begin by presenting major maintenance concepts in Chapter 2. Concepts will be presented chronologically, which will provide a review of the appropriate maintenance nomenclature and frameworks that can be used to evaluate various maintenance practices across a variety of assets. Current predictive and prognostic maintenance methods will also be included. In Chapter 3, applications of existing predictive maintenance methods will be

presented for a variety of assets including turbofan engines found on commercial aircraft. Chapter 4 will present the data that is currently collected for long term asset

maintenance contracts including how to address missing data. Chapter 5 focuses on finding new sources of operational asset data and how it can be included in the existing models. Chapter 6 will explain how supplemental data was processed and incorporated into the models.

Chapter 7 will present how asset sensor data can be used for condition monitoring of an asset. Chapter 8 will provide recommendations for continued improvements to predictive

maintenance models and identify areas of future work. Appendices provide a historical overview of Pratt & Whitney, an explanation of the basic physics of turbofan jet engines, and information on how to best implement predictive and prognostic maintenance models from an organizational perspective.

Chapter 2

2

Established Maintenance Concepts

For as long as parts, machines, and engineering systems have existed, there has been a need for them to be maintained and repaired. In its first generation, maintenance was straight forward: if a machine were broken, one simply fixed it. Little attention was given to the impact maintenance had on cost or when it should occur. However, as parts and machines became more complex, maintenance concepts began to mature at the onset of World War II. After the war, this led to the development of formal maintenance concepts during the second generation of maintenance in the 1950s. As computing became more accessible in the third generation of maintenance from the late 1970s and 1980s, data that resulted from these maintenance

concepts could be analyzed to make informed decisions and begin to predict when maintenance should be completed. Today, in the fourth generation of maintenance, an abundance of Internet connected sensors, machines, and parts can be monitored using real time prognostics.

This section will start with a brief overview of maintenance and establish a standard set of terms that will allow for the evaluation of individual maintenance concepts. Next, this section will trace the history of maintenance concepts in detail. As the concepts have matured, they have incorporated new technologies and borrowed from one another. As a result, tailored maintenance concepts exist across a range of parts and machines with varying degrees of complexity.

Fundamentally, maintenance can be defined as the “set of activities required to keep physical assets in their desired operating condition or to restore them to this condition.[2]” Moving forward individual parts, pieces of equipment, or complex designs will be referred to as “assets.” Maintenance technology is the separate equipment and tools to support an asset. Maintenance operations are the service interventions performed on assets to repair them. Maintenance management refers to the coordination of all of these activities.

Anything that is subject to maintenance whether it be automobiles, electronics, or jet engines can be considered to be an asset. All of the parties involved in the design, manufacture, use, and repair of assets have a stake in the in range of technical and commercial issues

involved with maintenance. The connections between the technical and commercial aspects of maintenance can be complex as shown in Figure 2-1 [2]. Maintenance concepts have direct effects on the degradation and maintainability requirements for the design of the asset, and the operating costs of a business because some party must ultimately pay for the maintenance. Maintenance lies directly between the technical and commercial sides of a business. Figure 2-1 also shows that there are secondary and tertiary impacts of maintenance: the profit, design, and production rate of the asset. The successful implementation of a maintenance concept requires an interdisciplinary approach. The most common disciplines are:

• Engineering • Science • Economic • Legal • Statistics • Operational Research • Reliability Theory

Figure 2-1: The complex linking of technical and commercial issues in maintenance concepts [2] Maintenance management occurs at three levels: strategic, tactical, and operational as shown in Figure 2-2. At the strategic level, maintenance strategy considers how maintenance relates to other parts of the business such as deciding whether or not to outsource maintenance entirely or what changes to technical or commercial strategies are needed to support the

desired level of maintenance. At the tactical level, maintenance planning and scheduling occur. Maintenance policies are set after deciding how many facilities will perform maintenance and where the facilities will be located. Determining the appropriate number of spare parts is also a tactical decision. Finally, at the operational level, maintenance work execution occurs. Data is collected, root causes are investigated, and actual repairs are made [2].

Functional Requirements Design/ Upgrade Maintainability Requirements Production Rate Equipment Degradation Maintenance Output Operating Costs

Revenue Investment Profit

Business Goals Technical

Figure 2-2: The strategic, tactical, and operational levels of maintenance management [2]

2.2

First and Second-Generation Maintenance Concepts

The first generation of maintenance existed from the first mechanical repairs up to World War II. Industry had yet to become fully mechanized. From a management perspective,

downtime was not important and repairing equipment was not a priority. Equipment was inherently much more reliable because it was simple and overdesigned. Given this simplicity, there was no need for formal maintenance management concepts.

During the war, demand soared for industrial goods while there was a shortage in labor. To compensate, more machines and automation were introduced into production activities. After the war, factories had both more total machines and more complex machines. With the

increased mechanization, machine downtime and availability became of greater importance as it now took longer to repair the broken machines, and the costs associated with these repairs also rose. From a business perspective, failures and their costs should be prevented. This led to the first preventative maintenances practices such as equipment overhauls at set periods of time in the 1960s [3]. As maintenance costs increased in relation to other operating costs, maintenance planning and control systems were used to make maintenance management concepts more

Strategic Level Tactical Level Operational Level Maintenance Strategy Maintenance Planning and Scheduling Maintenance Work Execution • Business perspective

• Technical & commercial • In-house v. Outsourcing • Replacement/Design Changes

• Degradation (reliability science) • Maintenance policies

• Logistics (Spares, facilities, etc.)

• Data collection

• Data analysis (root cause, other factors)

efficient. With a large amount of capital invested in assets, including maintenance costs, firms began to explore ways to optimize the life and value of assets.

2.3

Third Generation Maintenance Management Concepts

In the 1970s improvements to maintenance management concepts were the result of a reinforcing feedback loop. New expectations of maintenance generated new research on maintenance, which resulted in new maintenance techniques. As these new techniques were implemented, new expectations developed. The first new expectation was that a low level of downtime was expected across all industries. To increase availability, firms improved the availability, or operational time, of their assets. To sustain a high level of availability, there was also a greater focus on quality and safety standards.

The key to the new research of the third generation was centered on when assets fail. In previous generations, it was assumed that assets would either fail early in their life or late in their life. This led to the creation of a “bathtub” curve. However, as will be discussed in the following section, there are actually six modes to describe when asset failures occur [3].

Many new maintenance techniques were developed in the third generation. While some of these techniques will be discussed in the overview of specific maintenance concepts that follow, developments included decisions support tools like failure modes and effects analysis, condition monitoring, designing assets with maintainability in mind, and dedicated maintenance teams and organizations. Figure 2-3 summarizes the progression of maintenance expectations and techniques across the first three generations.

Third Generation

Second Generation • Higher asset availability and _reliability • Increased quality

• Longer equipment life • Condition monitoring • Smaller, faster computing • Failure modes and effects

analysis • Teamwork First Generation • Higher asset availability • Scheduled overhauls • Systems for planning work • Primitive computing • Fix it when broke

1940 1950 1960 1970 1980 1990 2000

Figure 2-3: The progression of maintenance expectations and techniques from 1940 through 2000 [3]

2.4

Reliability Centered Maintenance (RCM)

Reliability Centered Maintenance (RCM) is a concept that traces its origins to the aircraft industry in the mid-20th _{century. There is an underlying assumption in maintenance theory that}

scheduled maintenance causes an increase in reliability. Intuitively, parts wear out, and an asset becomes less reliable as its operational life increases. The more often assets are overhauled, the better they will be protected against failure. The key was to find the interval (strict hard-time policy) that allowed for reliable operation of the assets. In the 1960s,

maintenance managers at United Airlines began to conduct actuarial analyses of aircraft failure data, and their studies revealed that hard-time policies were not effective at controlling failure rates. The FAA formed a taskforce with the airlines to examine these issues. The resulting

report by Nowlan and Heap for the US Department of Defense, Reliability-Centered Maintenance, provides the foundations of RCM [4].

The tenets of RCM examine how failures occur, the consequences of these failures, and the good that preventative maintenance can accomplish. The causes of failure can be

imperfectly understood, random, or economically prohibitive to determine. For each asset, the RCM process asks seven questions [3]:

1. What are the functions and associated performance standards of the asset in its present operating conditions?

2. In what ways does it fail to fulfil its function? 3. What causes each functional failure? 4. What happens when each failure occurs? 5. In what way does each failure matter?

6. What can be done to prevent or predict each failure?

7. What should be done if a suitable proactive task cannot be found?

Assets can be divided into two functional categories based on how users intend to use these assets. Primary functions apply to the reasons why an asset was obtained. Typical

primary functions would include speed, output fuel efficiency and quality. Secondary functions of an asset are those that support the primary functions such as controls systems, environmental requirements, and the aesthetics of the asset. Evaluating an asset with respect to its primary and secondary functions reveals to maintenance managers how these assets actually operate.

In RCM, failures are assessed by identifying which circumstances define a failed state and then identifying the events that cause an asset to enter the failed state. The failed states themselves are known as functional failures. When an asset is in a functional failure, it is unable to fulfil a function to a standard of performance which is acceptable to the user. Functional failures can also include partial failures where an asset may still be operational but does so at a

as failure modes. The most frequent failure modes are the result of normal wear and tear, but failures resulting from design flaws or human and operational errors must also be considered [3].

Failure effects describe the what happens in each failure mode. There must be evidence that a failure has actually occurred. Maintenance professionals must be able to determine if failure effects produce an unsafe state or have an impact on current operations and production. Most importantly, failure effects should clearly identify if any physical damage has occurred and what must be done to repair the failure.

In RCM, the consequences of failures are more important than the underlying technical characteristics of each failure. In general, maintenance is not meant to prevent failures entirely, but rather minimize the impact of their consequences. RCM has four types of failure

consequences:

• Hidden failure consequences do not have a direct impact, but they can increase the probability at which other failure modes occur

• Safety and environmental consequences are the result of failures that may injure a person, lead to loss of human life, or violate existing safety and environmental standards • Operational consequences are the result of failures that impact the means of production

of an asset, which should be considered in addition to the direct cost of repair • Non-operational consequences are those that do not impact safety or operations

By evaluating the different types of failure consequences, maintenance activities can be prioritized to focus on those that will have the most benefit to an organization. Failure

management occurs with both proactive and default actions. Proactive tasks are the actions taken before a failure occurs to prevent assets from entering a failed state. As will be discussed later, proactive tasks lead to predictive and preventative maintenance. Default actions are those that deal directly with an asset in a failed state and are used when an effective proactive task is

not available. Examples of default actions include redesigning an asset or operating it to the point of failure.

Before examining proactive tasks in greater detail, it is best to examine the most

common patterns of when failures occur. Intuitively, one should be able to examine failure data to determine the useful life of an asset and implement proactive tasks before failures occur. In a simple case, the probability of failure would look like that shown in Figure 2-4. An asset enters into service, operates with near constant or slowly increasing chances of failure before entering the failure zone, where failure is much more likely to occur. The key is being able to predict the transition between useful life and the failure zone [3,4].

Figure 2-4: A traditional view of when failures occur: a period of useful life leading to a distinct failure zone [4]

However, assets are much more complex, and while the pattern in Figure 2-4 is true for certain types of assets, it may not be the dominant failure mode in a complex asset. In fact, there are a six common patterns of failure across a range of mechanical and electronic assets as shown in Figure 2-5. Pattern A is the infamous “bathtub curve,” which begins with a period of infant mortality where failure is more likely, followed by a constant useful life and finally the failure zone. Pattern B is the same as Figure 2-4. In Pattern C, the probability of failure slowly

Useful Life Failure _Zone

Conditional Probability of

Failure

increases. In Pattern D the probability of failure is low when the asset enters into service, but this increases sharply soon after to a constant level. Pattern E shows a constant probability of failure from when an asset enters into service across all time. This is effectively a pattern of random failures. Pattern F is the opposite of Pattern B, where failures are more like during entry into service but soon drop to a near constant chance of failure.

Figure 2-5: Six common failure patterns of operating time versus conditional probability of failure [4]

Investigations on the repair of commercial aircraft revealed that 4% of failures were represented by Pattern A, 2% by B, 5% by C, 7% by D, 14% by E, and 68% by Pattern F. These findings contradict the belief that there is a connection between reliability (or failure) and

operating life. Unless the dominant failure mode is related to the operational life of the asset,

A

B

C

D

E

F

proactive maintenance all together. If failure consequences are severe or safety related, something must be done to prevent or predict the failures [3,4].

Proactive tasks are one way to mitigate the consequences of these failures. Under RCM, proactive tasks are classified as scheduled restoration tasks, scheduled discard tasks, and scheduled on-condition tasks. Scheduled restoration tasks remanufacture an asset’s

components to an “as new” condition before a set age limit regardless of its condition at that time. Scheduled discard tasks involve the removal and replacement of a part with a brand-new part, regardless of its condition at the time. In general, these two types of proactive tasks are referred to as preventative maintenance.

On-condition tasks focus on finding the early warnings or signals of failures, which are known as potential failures. Potential failures are defined by the identified physical conditions that indicate if a functional failure is about to occur or is occurring. Assets remain in service on the condition that they are operating within acceptable performance standards. On-condition maintenance spans predictive maintenance, condition-based maintenance, and condition monitoring.

RCM provides criteria to determine if a proactive task is technically feasible and how often it should occur. If a proactive task is not technically feasible or not worth performing from an economic perspective, then a default action must be taken. When viewed through the types of consequences of failure, proactive tasks are worth doing when:

• The risks from hidden failures associated with that function are reduced to an acceptable level; if such a task does not exist, failure finding tasks must occur

• The risks from safety related failures are reduced to a low level or eliminated entirely; if such a task is not possible, the asset must be redesigned, or the process must be changed

• The cost of reducing the risks from the operational failures are less than the operational consequences over the same period of time

• The cost of reducing the risks from non-operational failures is less than the cost of repair over the same period of time

When implemented, the RCM process has tangible benefits. RCM integrates safety decisions across all maintenance activities. Safety implications of failures are considered before operational implications. RCM focusses maintenance actions on where they have the most benefit by prioritizing those that have the greatest impact on the performance of the asset. RCM produces longer useful life of expensive items by utilizing on-condition maintenance. Finally, documentation from the RCM process is comprehensive and can be entered into a data base.

2.5

Failure Modes and Effects Analysis (FMEA)

Similar to how RCM focuses on the consequences of failure, failure modes and effects analysis (FMEA) is used to identify for an asset its function, types of failures, the effects of these failures, and root causes. The risks of each cause are indexed so that the reliability of the asset can be increased in a methodical manner. FMEA is applicable to both new assets as well as assets that are already in the field. There are six main phases of FMEA [5,6]:

1. In the planning phase, objectives of the failure analysis as well as the scope and applicable assets of the analysis are defined. With complex assets, an interdisciplinary team is best suited for this function. Initial documentation and data are collected by the team.

2. In the analysis phase, the team identifies the functions and features of the asset, characterizes the types of potential failures of each function, identifies the effects for each type of failure, identifies the possible causes of each failure, and characterizes the current controls in place to prevent failures.

3. In the risk assessment phase, severity, occurrence, and detection indices are assigned using a point system each cause of failure. The indices must be examined

4. In the improvement phase, the team lists potential actions that can prevent failures, prevent the causes of failures, reduce the occurrence of failures, limit the effects of failures, or increase the probability that a failure can be detected.

5. In the monitoring phase, actual assets are analyzed, and data is collected. Potential failures are compared with ones that actually occur. The previous steps are repeated as needed as new data is collected.

6. In the conclusion phase, FMEA has provided a systematic indexing of information about the failure of assets, a better understanding of problems with the assets, the

implementation of continuous improvement items, and an increase in quality.

The critical component of FMEA is the risk assessment phase. A flow chart of the risk assessment phase is provided by Figure 2-6. For a particular failure mode, occurrence is assessed by examining the failure rates and assigning an index in accordance with Table 1, the severity effects are assigned an index in accordance with Table 2, and the level of detection is assigned an index in accordance with Table 3. After the three indices have been determined, the risk priority number (RPN) can be found by taking the product of the three indices.

Figure 2-6: A process flow diagram of the risk assessment phase of FMEA [6]

Table 1: Occurrence failure rates and indices for FMEA risk assessment [6]

Probability of Failure Possible Failure Rates Ranking/Index

Very high: failure is almost inevitable

≥ 1 in 2 10

1 in 3 9

High: repeated failures 1 in 8 8

1 in 20 7

Moderate: occasional failures

1 in 80 6

1 in 400 5

1 in 2,000 4

Low: relatively few failures 1 in 15,000 3

1 in 150,000 2

Remote: failure is unlikely ≤ 1 in 1,500,000 1

Failure Mode Effects Severity (S) Causes Occurrence (O) Controls Detection (D) Priority RPN = S x O x D

Table 2: Severity criteria and indices for FMEA risk assessment [6]

Effect Criteria: severity of effect Ranking

Hazardous – without warning

Very high severity ranking when a potential failure mode affects safe operation and/or involves noncompliance with regulations without warning

10

Hazardous – with warning

Very high severity ranking when a potential failure mode affects safe operation and/or involves noncompliance with regulations with warning

9 Very high Product/item inoperable, with loss of primary function 8 High Product/item operable, but at reduced level of performance.

Customer dissatisfied 7

Moderate Product/item operable, but may cause rework/repair and/or

damage to equipment 6

Low Product/item operable, but may cause slight inconvenience

to related operations 5

Very low Product/item operable, but possesses some defects

(aesthetic and otherwise) noticeable to most customers 4 Minor Product/item operable, but may possess some defects

noticeable by discriminating customers 3

Very minor Product/item operable, but is in noncompliance with company

policy 2

None No effect 1

Table 3: Detection criteria and indices for FMEA risk assessment [6]

Detection Criteria: likelihood of detection by design control Ranking

Absolute uncertainty

Design control will not and/or cannot detect a potential

cause/mechanism and subsequent failure mode; or there is no design control

10

Very remote Very remote chance the design control will detect a potential

cause/mechanism and subsequent failure mode 9 Remote Remote chance the design control will detect a potential

cause/mechanism and subsequent failure mode 8 Very low Very low chance the design control will detect a potential

cause/mechanism and subsequent failure mode 7 Low Low chance the design control will detect a potential

cause/mechanism and subsequent failure mode 6 Moderate Moderate chance the design control will detect a potential

cause/mechanism and subsequent failure mode 5 Moderately

high

Moderately high chance the design control will detect a

High High chance the design control will detect a potential

cause/mechanism and subsequent failure mode 3 Very high Very high chance the design control will detect a potential

cause/mechanism and subsequent failure mode 2 Almost certain Design control will almost certainly detect a potential

cause/mechanism and subsequent failure mode 1

2.6

Preventive and Predictive Maintenance (PPM)

While they are often confused with one another or used interchangeably, there are subtle differences between preventive maintenance (PM) and predictive maintenance (PdM). PM is a series of tasks that either extend the life of an asset or detects that an asset has

experienced critical wear and is going to fail. PdM refers to maintenance techniques that inspect an asset to predict if a failure will occur usually in association with advanced technology and sensors. Together, PM and PdM refer to preventive and predictive maintenance (PPM). PPM is of greatest benefit in two cases. First, PPM reduces the probability of death, injury, or

environmental damage to zero or near zero. Second, the cost of PPM is lower than the cost of the consequences of failure [7].

2.6.1 Preventive Maintenance

Preventative maintenance leverages the fact that physical assets are subjected to stresses. As assets operate, they deteriorate, or their resistance to stress decreases over time. The amount of stress can be quantified in calendar time, running time, distance traveled, or in operating cycles. Collectively, these all refer to the age of an asset in operation. In the review of RCM, the relationships between age and failures were reviewed. Assets have a useful life and a failure zone. Preventive maintenance defines the boundary between these two zones and schedules PM tasks before failure occurs.

Recall the failure pattern previously shown by Figure 2-4. Asset manufacturers and operators still assume that similar assets that perform a similar duty under the same conditions will do so reliably for a period of time and most of the items will begin to wear out at the same

time. Age related failure patterns are only applicable if an asset is mechanically simple or if the dominant failure mode of a complex asset is age based [3].

Age related failures are commonly found in assets that operate with direct wear in a product, but they can also be associated with fatigue, corrosion, and combustion. Fatigue is usually the product of high frequency or cyclical loads. Thus, if one can determine the useful life for an asset, two PM tasks can be used before failure: scheduled restorations and scheduled discards.

Remember, scheduled restoration entails remanufacturing a single component or overhauling an entire assembly before a specified age limit, regardless of its condition at that time. Restoration tasks are also known as scheduled rework. The frequency of a scheduled restoration task is governed by the age at which the item or component shows a rapid increase in the probability of failure. Historical data is the best way to determine the frequency of

scheduled restoration tasks.

If the useful life of an asset can be determined from historical data, scheduled

restorations are feasible only if the assets survive to this age, and they can be restored to their original level of resistance to failure. Otherwise, these failures would only be classified as unanticipated failures. With respect to safety consequences, all assets must survive to the point of the scheduled restoration tasks. These assets are governed by safe-life limits.

However, the economics of restoration tasks must also be considered. Assume that an asset has a useful life of 12 months but an average life of 18 months. Over the course of three years, there is an expectation that two failures will occur if no PM is performed. Over the same time period, a restoration task would be performed three times. If the cost of failure is $5,000, failures would cost $10,000 over the three-year time period. If the cost of each restoration was only $3,000, they would cost $9,000 in total, and the restorations would be economically viable. If the average life were extended, and the cost of overhaul remained the same, the overhauls would no longer be economically viable [3].

Economics must never override safety concerns. This is where scheduled discard tasks are used to discard an item or component before an age limit, regardless of its condition at the time. The replacement of the component with a new one will restore the asset to its original level of resistance to failure. Just like scheduled restorations, the frequency of scheduled discards is dependent on an asset having a useful life and a failure zone. Safe-life limits apply to failures with safety consequences. Because safety is involved, the tasks must prevent all failures. As Figure 2-7 shows, the safe-life limit must be less than the time at which failures occur.

Figure 2-7: Setting of safe-life limit in relation to failure occurrence for scheduled discard tasks [3]

The safe-life limit is established by dividing the average life or age when failures begin by an arbitrary factor of safety such as 3 or 4. Figure 2-7 shows a factor of safety of 3. The intent of safe-life limits is to avoid the occurrence of a failure, so scheduled discard tasks are only worth performing if they ensure that failures do not occur before the safe-life limit.

Conditional Probability of Failure Time (years) Safe-Life Limit Time When Failures Start to Occur 1 2 3 4 5 6 7 8

However, recalling failure patterns D, E, and F from Figure 2-5, failures are random, the failure zone is not clearly defined, and the idea of PM tasks at set intervals does not apply. PdM is one such way to address these types of failure.

2.6.2 Predictive Maintenance (PdM) and Condition Monitoring

Expanding on the definition of PM, inspection activities are inherently predictive. An inspector examines an asset and determines if the amount of wear on the asset will result in failure. PdM is a way to view data regardless of how the inspection is done. It is important that the conclusion drawn from the inspection be based on observation, judgment, and reasoning, which determine if an action is predictive. In a sense, PdM is a way to leverage data. The conclusions we extract from the data determine the predictive nature [7].

For PdM, these activities often include the use of instrumentation or some other

technology to observe an asset. For non-age-related failures, most will give a warning that they are about to occur. Monitoring the sensor data from the asset allows for intervention before failures occur. Figure 2-8 demonstrates the events leading up to a failure. At some point in time, an asset begins to fail. This point is usually not related to age and is difficult to detect. As

degradation continues, failure can be detected at point “P.” This point is known as a potential failure. A potential failure is an identifiable condition which indicates that a functional failure is either occurring or about to occur. If failure is not detected at this point, degradation of the asset accelerates until it ultimately fails at point “F.” Thus, this is known as the P-F curve. If a potential failure is detected between point P and F it may be possible to take action to prevent or avoid the consequences of a failure. Tasks designed to detect potential failures are known as on-condition tasks. Assets are monitored and left in service on the on-condition that they continue to meet performance standards[3,4].

Figure 2-8: The potential failure to failure curve demonstrates when predictive maintenance occurs [3]

The interval or amount of time between points P and F must also be considered. On-condition tasks must be carried out at intervals less than the P-F interval, which is also known as the warning period. Depending on the asset or failure mode, the P-F interval could be several seconds or stretch to decades. If an on-condition task is performed at intervals longer than the P-F interval, there is a chance that the failure will be missed. An on-condition task frequency that is half of the P-F interval is usually sufficient.

However, as Figure 2-9 shows, the P-F interval is not constant across assets of the same type. When selecting an appropriate on-condition task interval should be substantially less than the shortest P-F interval across a range of assets in order to detect all potential failures. The best way to establish a precise P-F interval is to simulate the occurrence of failures without consequences. This can be done with parts in a laboratory setting or using computer modeling [3].

Point where failure starts to occur (unrelated to age)

Point where it can be determined that an asset is failing (“potential failure”)

Point where failure has occurred (functional failure) P F Time Condition

Figure 2-9: Inconsistent P-F intervals across the same assets, which leads to variability [3] On-condition tasks are feasible when:

• It is possible to define a clear potential failure condition • The P-F interval is reasonably consistent

• It is practical to monitor an asset at intervals less than the P-F interval • The P-F interval is long enough for on-condition tasks to be performed

There are four major categories of on-condition methods: condition monitoring techniques involving the use of special equipment, techniques that leverage variations in product quality, techniques that monitor primary asset for failure effects using existing sensors or gauges, and techniques that rely on the human senses. When specialized equipment is used to monitor the condition of an asset, this is known as condition monitoring. There are hundreds of condition monitoring techniques with all of them being designed to detect potential failure effects. These can be classified broadly as dynamic effects, particle effects, chemical effects,

P F1 Time Condition F2 Shortest P-F interval Longest P-F interval

physical effects, temperature effects, and electrical effects. Most instruments are only capable of monitoring one condition at a time, and it can be difficult to optimize monitoring across a range of sensors. Condition monitoring techniques can be highly effective when implemented correctly but costly wastes of time and money when implemented poorly.

Using product quality, statistical process control (SPC) is one method which is often used to implement condition monitoring. SPC can measure an asset’s attributes such as dimensions or weight. This information can then be used to determine if the current condition is the result of an asset that is operating stably.

Primary effects such as speed, flow rate pressure, temperature or power are often one of the best sources of information for condition monitoring. Primary effects can be monitored by humans or automatically tracked and recorded in a computer database. The records of these effects can be compared with other reference information to search for potential failures. The readings must be taken at a frequency which is less than the P-F interval and the devices providing the information must be properly maintained in order to provide an appropriate level of fidelity.

Inspection techniques based on the human senses are usually not appropriate for complex assets as human senses are subjective and failure will usually occur shortly after they are capable of sensing a failure. Also, if the asset operates in a harsh environment, humans may not be able to monitor the conditions safely. Thus, care must be taken when selecting the best sensing categories for monitoring. As more asset data is collected from a variety of assets across multiple conditions, even more modern maintenance techniques are needed.

2.7

Prognostics and Fourth Generation Maintenance Techniques

With PdM and on-condition maintenance, maintenance is only performed on an asset when the system condition actually requires it. Traditionally, measured conditions were

provide a warning that failure is about to occur and do not quantify the actual remaining useful life (RUL) for an asset. Since 2000, much effort has been spent in developing fourth generation prognostic maintenance methods that can accurately predict when an asset will fail. Formally, prognostics relies on the data from previous operating modes to predict the future state of the monitored asset and to give an estimation of the RUL. Predictions of this nature require knowledge about the current state of the asset in order to extrapolate the future conditions of the asset. Prognostics include both data and model-based techniques. Data based techniques focus on finding patterns and anomalies in large sets of collected data. Model based techniques utilize knowledge on the physics of failure to assess the remaining useful life [8,9].

The gap between the system and component level of a complex asset is also a

challenge for prognostics. Physics based prognostic methods are developed at the component level, but asset manufacturers are interested in performing maintenance on the entire asset or at the system level. Prognostic methods must be able to bridge these two levels. Ideally prognostic methods would be available for all components. This would be impossible for complex assets like ships or airplanes given the large number of components and subsystems they contain. Thus, critical parts must be selected for prognostics. The same methods that were used to determine critical parts for in RCM, PM, and PdM can be used to select the critical components for prognostics.

Once the critical components of an asset have been selected, physical parameters must be observed. A deep and thorough knowledge between physical parameters and failure modes is required. Physical parameters fall into the following domains: thermal, electrical, mechanic, chemical, humidity, biological, optical, and magnetic. A systematic method for identifying which parameters should be monitored is not yet available. Best practices are currently used to select these parameters for prognostics.

Once parameter data has been collected it must be saved in a digital format. In addition to the parameter data, data describing the operating conditions are also stored. They are

checked for possible errors and missing samples or can be filtered to remove noise, which is known as preprocessing.

After the data has been collected it, is time to combine it with the model by making a prediction on the RUL. The final step in the prognostic method is to validate the methods and model against actual failure data. Only a certain number of failures are available from an asset’s history given ongoing PMM activities which can make the validation set of available data small.

This chronology and review of standard PMM methods and nomenclature provides a framework to examine maintenance practices across a variety of assets. Studying the maintenance practices of specific assets will allow for the identification of predictive

maintenance techniques used in industry today. The common predictive techniques from one asset or industry may also be of use to those in another.

Chapter 3

3

Case Examples of Predictive Maintenance

This section will review examples of predictive maintenance for complex assets that have been implemented in the field. A variety of complex assets are considered from existing literature. In addition to these assets, current maintenance practices and predictive methods for turbofan jet engines will also be examined.

3.1

Predictive Maintenance for Light Emitting Diodes

Light emitting diodes (LEDs) are one of the most readily available lighting solutions in use today. LEDs can be found in smart phones, light bulbs, computer monitors, and large advertisement billboards. LEDs are prized for their high brightness and long useful life, which can be as long as 70 thousand hours of operation. In this context, LEDs are highly reliable, and the brightness of LEDs over their long useful life can be managed with PPM [10,11].

Manufacturers of LEDs for use as components in other assets have to guarantee a certain value for their useful life. The useful life of an LED is known as the lumen maintenance (LM) life, which is the amount of time when the initial light output falls below a predetermined limit. Depending on the application, this threshold could range anywhere between 50% to 70% of the initial LED brightness [10].

There are three common failure modes for LEDs:

• Semiconductor failures that include dislocations that generate movement and cause die cracking

• Interconnect failures such as wiring failures, corrosion of metal contacts, and electrostatic discharge

• Packaging failures that include delamination of substrates, lens cracking, and failures of soldering

For LEDs, RUL is typically predicted by using existing degradation data and least-squares regression. The TM-21 standard was created to provide a framework to predict the LM for LEDs based on existing maintenance data. Regression is used to estimate parameters for a degradation model, and a predicted failure curve is generated to obtain the LM. In the TM-21 method, luminous flux data, which is the amount of light emitted by an LED, was used as proxy of the optical performance of the LEDs [12].

In one study, the methods in the TM-21 standard were replaced with particle filtering (PF) techniques, and the results were compared against the existing LED maintenance data [12]. PF uses Monte Carlo simulations to estimate prognostics for nonlinear processes. PF uses sequential importance to reduce the number of samples needed to predict a future state and easily combines data from various measurement sources [13]. The PF method is able to dynamically update itself by absorbing new measurements while accounting for uncertainties [12].

A visualization of the comparison between the predictions for both the TM-21 and PF methods is shown in Figure 3-1 by comparing the LM to the operating time of the LEDs. The stars represent the collected LM data. The TM-21 predicted LM is shown in red while the PF predicted LM is shown in green including its 90% confidence interval in blue. The actual useful life of the LED is indicated on the x-axis. Predictions of the remaining LM were made at 690, 920, 1150, and 1380 hours across a total of four LEDs. Figure 3-2 compares the TM-21 and PF predictions against the actual remaining life the LED at those instances in time. The red line representing the PF prediction is much closer to the real RUL, in red, compared to the TM-21

TM-21 method [12]. Improvements over the TM-21 standard, like this PF technique, would allow the creators of assets that contain LED components to better predict the useful life of the

assets. These manufacturers would also be able to revise their maintenance schedules or levels of spare LEDs accordingly. This is one example where existing maintenance data can be used to make a more accurate prediction of RUL.

Figure 3-1: Example of LED lumen maintenance life prediction comparing standard TM-21 method with PF method [12]

Figure 3-2: A comparison of real, TM-21, and PF LED remaining useful life predictions [12]

3.2

Predictive Maintenance for Railway Infrastructure

Trains and railway infrastructure are another example of complex assets. Spread over large geographies, rail networks are expected to operate at maximum availability, which leaves very little time for maintenance activities. Thus, PPM are of great benefit to railway

infrastructure. The RUL for both the trains (rolling stock) and the tracks themselves can be monitored with data and analysis of physical models [8].

The primary failure mode for railways are defects in the track surfaces that result in rail breaks or even derailments [14]. Given that tracks are stationary and cover a large area, track conditions are best assessed by monitoring the rolling stock for wear conditions on their wheels rather than installing a vast network of sensors across the track network [15]. The process to

Figure 3-3 [16]. Track data includes the initial profile or shape of the rail, the curve radius of a given section of the track, the gauge or size of track, the inclination or tilt angle of the track, and the length of the track. Inputs from the rolling stock data are the initial profile of the wheel, the speed of the train, and the type of train. These two data streams are then combined in a multi-body physics simulation that models both the contact between the wheel and the rail and the local wear on the shape of the rail. This is used to predict a new profile of the rail, which is fed back into the model until the rail reaches a predetermined wear limit set by the railway

maintenance planners [15]. The total wear of the rail is equal to the sum of the area of the rail gauge and the worn area to produce a lower bound for track wear.

Over a timeline of several years, the wear area of the track can be predicted and compared with the as measured condition as shown in Figure 3-4. The wear area on the tracks produced by new wheels is greater than wear from worn wheels. The measured wear conditions are in alignment with the predicted wear conditions for worn rolling stock wheels. Given a

particular section of track and rolling stock traffic, the level of wear can be predicted into the future. Maintenance activities can be scheduled in advance when the predicted wear is below an acceptable level, which allows the operator enough time to suspend service on that portion of the track.

Figure 3-3: Process for rail wear prediction using track data, rolling stock data, and modeling [16] Track Data • Initial rail profile • Curve radius • Gauge • Rail inclination • Rail cant • Track length

Rolling Stock Data

• Initial wheel profile • Rolling stock speed • Rolling stock type

Multi-body dynamics simulation

Local contact model

Local wear model

Figure 3-4: Prediction of outer rail wear area for both new and worn rolling stock wheels [15]

3.3

Current Maintenance Practices and Predictive Methods for Turbofan

Engines

Jet engines are another group of complex assets that benefit from PPM activities and prognostic models. All jet engines found on commercial airplanes are known as turbofan engines. A review of how turbofan engines operate is presented in Appendix B. To better understand how PPM can benefit turbofan engines, this section will identify the critical

components and failure modes for turbofan engines. The maintenance programs for turbofan engines are just as complex as the assets themselves. An understanding of current

maintenance agreements and the scheduling of engine overhauls is also needed to implement PPM activities for these assets. Finally, the section will conclude with a review of current models used to predict the RUL for turbofan engines. The following chapter will review the data that is collected for these models.

While turbofan engines contain thousands of components and individual parts, the most critical components with respect to thrust, safety, maintenance, and failure modes are the components that lie along the gas path (the flow of air that is combusted in by the engine to create thrust): fan, low pressure compressor (LPC), high pressure compressor (HPC), the combustor (COMB), the high pressure turbine (HPT), and the low pressure turbine (LPT). An engine’s performance depends on the state or condition of these components based on individual parameters such as temperature, pressure, speed, or flow rate [17].

The most common failure modes of the gas path components are foreign object damage (FOD), blade erosion and corrosion, worn seals, plugged nozzles, and excessive clearances between the end of blades and the engine housing. Gas path analysis (GPA) can be used to identify and measure changes in engine performance, caused by a fault from one of the physical failure modes, by using the sensors already in the design of the engine. Performance parameters include thermal efficiencies, flow capacities, and nozzle areas. A summary of GPA is provided in Figure 3-5. GPA is a relative diagnostic process, and the performance of an engine at one point in time can be compared with itself at another point in time. Given a reference point, this information can be used to predict maintenance schedules, costs, and an engine’s time on wing (TOW) of an aircraft [17].The RUL of the engine is the remaining amount of time that it can operate on the wing of an aircraft before being removed for repair.

Figure 3-5: A process diagram of gas path analysis – monitor physical faults by observing changes in component performance [17]

3.3.2 Current Turbofan Maintenance Practices

There are three main practices used by airlines and MRO organizations today: hard time maintenance limits, on-condition maintenance, and condition monitoring maintenance. Turbofan maintenance activities require the disassembly and inspection of the critical components of an engine. This cannot be done while the engine is in operation or while an engine is attached to the wing of an aircraft (“on wing”).

Hard time maintenance activities remove engines at regular intervals for major repairs. This is also known as the time between overhaul (TBO) method. Overhaul is defined as the disassembly, inspection, repair, reassembly, and test of an engine. Again, the overhaul process is complex and cannot be performed in its entirety when an engine is attached to the wing of an aircraft. The engine must be removed from the wing and sent to a repair shop. This is why an engine overhaul is also known as a shop visit (SV). Over time, hard time maintenance practices

Physical Hardware Faults Degraded Component Performance Changes In Measurable Parameters Erosion Corrosion Fouling Worn Seals FOD Plugged nozzles Changes in: Thermal efficiencies Flow capacities Nozzle areas Speeds Temperature Fuel flow Power output Results in Producing Allows Isolation of Permits Correction of