Failure Mode Effects and Criticality Risk Analysis Review: Application within the Scope of the Wind Turbine

RESEARCH ARTICLE

Failure Mode Effects and Criticality Risk Analysis Review:

Application within the Scope of the Wind Turbine

Rim BAKHAT¹*, Chaimaa BENTAHER², Soumaya AHAROUAY ³

1-3Management Logistics and Applied Management Department, Faculty of Law Economics and social Sciences, University of Abdelmalek Essaadi, Tangier, Morocco

*Correspondence: [email protected]

A B S T R A C T Key words:

Logistics, FMECA,

MIL-STD-1629A, RPN,

Wind Turbine,

Failure mode effects and criticality analysis (FMECA) is an important probabilistic reliability model and an appropriate tool for future failures problem-solving. MIL-STD-1629A is one of the most popular FMECA standards from the U.S. Department of Defense. The following paper presents the results a Risk Priority Numbers (RPN) conducted for wind turbine assembly. This methodology is performed on the functional modes of the wind turbine components to perceive its performance, and determine its critical failures. The ranking of the failure modes criticality is realized based on the data collected from experts and decision-makers working in the renewable energy production area in Morocco. Further, the findings demonstrate that the generator and power electrical system are the two most critical components in the wind turbine system. Furthermore, the adopted methodology will help the decision-makers to improve the design of the critical components that require more attention and at the same time eliminate the inherent risks and deliver a system that respects the production standards.

1. Introduction

In the actual context of the modern asset growing complexity, the adoption of cost- effective tools has become necessary to meet operational requirements. Another important tendency is featured by the fact that current technical systems such as aircraft, military, renewable energies, petroleum equipment comprise a large number of sub-systems that are generally interrelated in a way that the main system is readily available to perform a set of required tasks and they are dispersed over a large geographical area (Rustenburg, et al., 2001;

Sleptchenko, et al., 2005). However, maintenance has become an indispensable research topic for both (management and engineering disciplines) and a concept widely used in many different industrial fields. Hitherto, these disciplines rely on

maintenance activities to upkeep equipment available and reliable at lowest costs as well as seek for both cost-effectiveness and operational performance. Recently, the exploitation of reliability tools by organizations has significantly increased to ameliorate the corresponding preventive measures, the capability of responding with emergencies, and the ability to stay competitive in the market place. Hereafter, maintenance activities must be recognized as one of the main current crucial management issues and must be frequently controlled in different operational environments to increase the performance of complex systems,

Maintenance actions and their support tasks ensure a high level of complex systems and play a major role in asset management. The repair of this sort of systems requires an important amount of

capital asset in order to stay available (Tysseland, 2007; Brick, et al., 2009). The key challenge and the core interest of reliability engineer are to detect fatal failures and prevent them from occurring (Blanchard, 2004) by using proper inspections and selecting the optimal maintenance strategies. One of the most significant systematic processes to select the cost-effective maintenance approach in that has been highlighted by many experts in the last few decades is FMECA. Today’s organizations development reveals the necessity of adopting holistic based decision-making methods that connect design and operation phases (Khabbaz, 2009). The focus has been put on the determination of fatal failures and preventing these potential failures from occurring by applying FMECA as a reliability analysis tool. FMECA is one of the most widely practical design methodologies and risk assessment alternative method in modern manufacturing engineering used to provide the supportability engineering concepts with accurate data about the excessive failure rates and their effects on overall systems, which, in turn, define the projected whole life cycle costs of the system itself (Bertling, et al., 2005). On the other side, the availability and supportability concepts are significantly used throughout the operation phase. Considering system reliability and maintainability performance, the focus and the framework of this study is based on failures solving-problem by increasing the system reliability & maintainability and reducing system failures. Although there are numerous FMECA standards, the most useful one is MIL-STD-1629A which had been developed by the U.S. Department of Defence (U.S. Dept. of Defence 20301). According to the most reliable and common definition of FMECA standard MIL- STD-1629A proposed by the U.S. Department of Defence, the push factors such as severity, detection and occurrence are determined and deducted by experts opinion which overwhelmingly comprises ambiguity and fuzziness (Einarsson et al., 1998).

2. Research Background

In this section, the research background is divided into four sub-sections. First, the frame of references related to the research study is presented. Second, the review of the diverse techniques in FMECA model is underpinned.

Third, the risk priority numbers method is explained. The research gaps are provided in the fourth sub-section.

2.1. Frame of References

Numerous risk and failure analysis methods are available in the business world to determine the various reasons behind the occurrence of the fatal failure of subsystem, parts and/or components. Some of these methods are What-If Analysis, Cause-Consequence Analysis, Fault Tree Analysis (FTA), Failure Modes Effects and Criticality Analysis (FMECA), Hazard and Operability Analysis, etc. (Glancey, 2006). On the other hand, organizations use now other different risks methods to identify potential failures such as;

MOSAR, HAZOP, PLSA, FMECA, and ETA (Tixier et al., 2002). A failure mode, effects and criticality analysis (FMECA), was initially developed by the U.S. Military in 1940 and it was mainly designed for space and aviation sector by NASA office of manned space flight for the Apollo program (Apollo program RA-006-013- 1A, 1966). Few years later, this approach was applied in the marine, nuclear and railways.

Table 1:

Concept Matrix Used for the FMECA model

Reviewed Articles

Definition and use of FMECA

Conduction of FMECA

FMECA TYPES

Strengths and Weaknesses of the method NASA

Apollo Program RA-006- 013-1A, 1966

•

U.S. DoD,

1977 • •

U.S. DoD,

1980 • • •

DoD Reliability Analysis Centre AD- A278 508, 1993

• • •

Tixier,

2002 •

Braglia et

al., 2003 • •

Yan-bin et

al., 2005 • •

Wang,

2007 •

Yun-Seong Lee et al., 2011

• •

Jun, 2012 •

Carlson,

2014 • • •

Y. Sinha et

al., 2014 • • •

Bahr, 2015 • •

Roseella Onofrio et al., 2015

• •

Omar Ngala Sarr et al, 2017

• •

Xiang- dong Li et al., 2017

• •

Christian Spreafico et al., 2017

• •

Silvia et al.,

2018 • • •

Nowadays, the FMECA is used in several risk- based based industries including engineering, automotive, design mechanical, medical, chemical, etc. (Manger et al., 2015). The failure modes, effects, and criticality analysis (FMECA) is an inductive bottom-up risk analysis method (Hwang et al., 2012) and reliability analysis tool which examines all probable ways that parts, assemblies, items, equipment and the system may fail. The latter is also able to determine all possible causes for each failure and identify the effects that the potential failure will have on the whole system performance as well (U.S. DoD, 1980). Table 1 summarizes the concept matrix of visualized articles and military guidelines for the FMECA literature review.

2.2. Techniques in FMECA model

FMECA method was in the first place applied by the U.S. Military. The effects were considered as system maintenance requirements, system maintainability, mission success performance, personnel and system safety (U.S. DoD, 1980).

According to Bahr (2015), FMECA is described as ‘an analysis tool that identifies all the ways a particular component can fail, what its effects would be at the subsystem level and ultimately on the system and what the criticality is’. Yun-Seong Lee (2011) explains FMECA as ‘a procedure for analysis of potential failure modes within a system using the classification by the severity or evaluation of the failure’s effect upon the system, where the failure modes refer categories of detailed failures according to the mechanism under a certain circumstance of the whole system.

It also includes a criticality analysis (CA) which is used to chart the probability of failure modes against the severity of their consequences’.

FMECA was initially developed by NASA as a ‘design methodology’ in early 1960s to guarantee the reliability of aerospace systems (Bowles et al., 1995; Boral et al., 2020). Since then, it has been adopted by various sorts of industrial areas for its simplicity of application, such as nuclear (Song et al., 2013) manufacturing (Lo et al., 2019), renewable energy (Bakhat et al., 2020), automotive (Carpitella et al., 2018), engineering (Gupta et al., 2021) and has become a reliable tool for risk and reliability analysis of complex systems.

According to CEI EN 60812, FMECA is divided into two main sequential analyses: failure modes and effects analysis (FMEA) and criticality analysis (CA). FMEA is a systematic process for the determination of fatal failure modes and their main causes and effects on the whole system performance. Moreover, FMEA ought to be realized prior to achieving the CA. CA is an extension of FMEA that allows analysts to quantitatively rank (prioritize) the subsystems

potential failure modes with respect to their risk criticality (Sinha, 2014). In addition, this analysis can be achieved with or without available data.

Moreover, the CA can be utilized quantitatively or qualitatively (Hwang, 2015). The process of conducting the approaches (both quantitative and qualitative) within FMECA is explained in Figure 1. The flow of FMECA is developed from different academic sources and taken from the literature review mentioned in Table 1.

Figure 1: Developed FMECA Workflow Source: Own elaboration

FMEA is a procedure that covers the definition of the system and the subsystems being studied, identification of potential failures, analysis for potential failure modes and the classification of their effects according to their severity. When a qualitative CA is conducted, the potential failure modes probability of occurrence have to be taken from experts’ judgment. However, when a quantitative CA has performed the probability of occurrence of the mentioned failures have to be collected from real available data.

First, the system and subsystem definition is the necessary step in achieving the FMECA model. This definition generally includes the performance, mission, restraints, functions, and operational modes of the system at each level. The following figure describes the system indenture levels. The second step of the FMEA procedure is the reliability logic block diagram. According to MIL-STD-756, the construction of the block diagram for a system is a necessary step to represent the interrelationships of its entities and analyse the interdependencies of a functional group as well. The reliability logic block diagram allows experts to detect the potential failure modes effects thru each indenture system level as prescribed in Figure 2. Additionally, a system part can be considered as the main system at any level and can be diagrammed in a similar manner for depicting the failure modes and their effects.

Figure 3 illustrates a typical example of reliability logic block diagram, and the mentioned notes describe the interdependencies of the system components.

As shown in Figure 3, the system block diagram illustrates the flow of functions and operations as drawn by the schematic sequence drawings. Therefore, each entity of the system should be logically described on the block and labelled, respectively. The third step of the FMEA

1System inputs and outputs can be either functional or operational.

procedure is the failure modes identification. The potential failure modes are identified by detecting item inputs and outputs¹depicted in the system block diagram

Figure 2: Typical Example of Indenture System Levels (MIL-STD-1629A)

All expected failure modes for each part must be determined. To know that an analysis is completed, each part failure mode must be examined for the following factors; ‘premature operation, failure to operate at the proper time, intermittent operation, failure to stop operating at the proper time, loss of output, and degraded output or reduced operational capability (MIL- SDT-1629A)’.

The fourth step of the FMEA procedure is the failure effects analysis. It is performed on each component of the reliability logic block diagram.

All fatal failures consequences must be identified and noted. Failure effect levels are obviously categorized as follows; ‘system failure, degraded operation, system status failure, and no immediate effect’ (MIL-STD-1629A).

The fifth and last step of the FMEA procedure is the classification of fatal failure effects.

Generally, the failure effect is classified in terms of the worst/ dangerous consequences of the system entities. The severity classification has to

be assigned to each system indenture level and it is qualitatively measured in accordance with these categories; ‘catastrophic, critical, marginal, and minor’ (MIL-STD-1629A).

Figure 3: Typical Example of Reliability Logic Block Diagram (NASA APOLLO, 1966)

The Criticality Analysis (CA) is also part of reliability analysis procedure which generally comes after FMEA procedure and identifies a system parts criticality. The criticality analysis of a system could be performed in two different steps. When the failure modes in FMEA are determined in terms of occurrence, then they must be qualitatively analyzed. When the data is available to calculate the criticality of the system failures then they are quantitatively analyzed (Gan, L, 2011).

Once it is intended that a quantitative CA will be conducted, the quantitative criticality analysis worksheet is filled respectively². Then, the data taken from the FMEA sheet must be moved into the quantitative criticality analysis worksheet. In this case, the calculation of the modal failure is necessary. The modal failure rate is explained by the following formula (MIL-STD-1629A):

𝝀

= 𝜶𝝀

𝝀

𝜶

𝝀

The formula of the modal failure rate is calculated by multiplying the total system failure rates. According to MIL-STD-1629A ‘procedures for performing a failure mode, effects and criticality analysis’. The criticality number is a relative measure that calculates the system failure modes frequency in order to prioritize importance based on the modal failure.

The formula used to calculate the criticality number is described as follows (MIL-STD- 1629A):

𝑪

= (𝜷𝜶𝝀

𝒕)

𝑪

number

2Some categories in the quantitative CA worksheet are derived from FMEA sheet.

𝜷

𝜶

𝝀

𝒕

The criticality number is derived from formula 1 but it takes into consideration the duration of the system mission that may be expressed in operating cycles or in hours. The 𝜷 rates consider the conditional probability of failure modes that express their effects with respect to the criticality classification and they allow analysts to properly judge the criticality classification. According to MIL-STD-1629A, the 𝜷 rates are quantified with respect Table 2.

Table 2:

Standardization of Failure Effect Probability (MIL- STD- 1629A)

Failure Effect 𝛽 Value

Actual Loss 100%

Probable Loss >10% To > 100%

Possible Loss >0% To > 10%

No Effect 0%

In addition, the item or part criticality number can be used to measure and rank the effects of each part failure. This number is calculated by summing all of the criticality numbers with respect to the severity of the failure occurrence probability and their effects. The formula utilized to calculate this total critical number is displayed (MIL-STD-1629A):

𝑪

= ∑ (𝜷𝜶𝝀

𝒕)

𝒏 𝒋

𝒏=𝟏

(3)

Where : n= 1,2,3,… j

Or,

𝑪

= ∑

(𝑪

)

Where:

𝑪

𝑪

𝒋

In most cases, the important critical failure modes are the ones with great criticality and high severity. The results of the part critical number will help the decision makers and engineers construct the fault tree analysis (FTA), thereby improving the whole system design in order to avoid future fatal failures. The quantitative approach for evaluating the criticality number is mostly employed in the nuclear, aerospace and chemical industries (Braglia et al., 2003).

2.3. Risk Priority Number (RPN)

A second approach for criticality calculation is based on qualitative analysis due to the unavailability of data. The criticality analysis in this case heavily relies on the risk priority number (RPN) method. Due to the simplicity of this approach, the RNP method was initially industrialized by the Automotive Industry Action Group (AIAC) and developed in detail with the help of the manual ‘Potential Failure Modes and Effects Analysis-FMEA’.

In this analysis, the failure modes rates and failure modes probability are not taken into account. First, the role of this approach is to rank failure modes severity with respect to their probability of occurrence. Once it is intended that a qualitative CA will be conducted, the qualitative criticality analysis worksheet is filled respectively (the worksheet is given in Appendix E)³. Thus, the data mined from the FMEA sheet must be moved into the qualitative criticality analysis worksheet.

3 Some categories in the qualitative CA worksheet are derived from FMEA sheet.

The additional columns in sheet describe the use and the calculation of the risk priority number (RNP). The latter takes into accont the probability of failure modes and severity of system parts.

Next, the criticality of these failure modes is the product of three main risk parameters while each risk parameter accepts discrete values i.e the scale is ranging from 1 to 10. The combination is explained in the next equation (Bowles, 2003):

𝑹𝑷𝑵 = (𝑺) × (𝑶) × (𝑫) (4) Where:

𝑺

𝑶 = Probability of failure modes occurence

𝑫 = Visibility of failure detectability In the FMECA tool, failure modes with great RPNs are expected to the product in more critical and severe consequences comparatively to lower RPNs (Khalil et al., 2004). Finally, the decision- makers can improve preventive measures and develop rapid intervention and response plan for each potential failure depending on its importance priority and risk magnitude.

Once the criticality analysis (for both approaches) is completed, analysists evaluate the criticality and severity of each defined failure mode and envisage the risk level by putting the indices to the column of description. The results of this evaluation are expressed by employing a criticality matrix.

Figure 4: Criticality Matrix (MIL-STD-1629A)

A criticality matrix is a practical tool for adopting and developing corrective strategies implementation. It also offers a graphical description of determining and comparing different failure modes for all parts and/or

components within a given system with respect to their severity. As shown in Figure 4, the farther along the diagonal line from the starting point that failure modes are recorded, the deeper the criticality of the failure⁴. According to MIL-STD- 1629A, the severity of failures could be classified into four main categories. These levels are itemized as follows:

Table 3:

Severity Classification Levels (MMIL-STD-1629A) Description Categories Definition Catastrophic I Death or system loss

Critical II

Severe injury, severe occupational illness, or major system damage.

Marginal III

Minor injury, minir occupational illness, or minor system damage.

Minor IV

Less than minor injury, occupational illness, or minor system damage.

The matrix illustrated above is constructed by adding failure modes references into matrix places which show the classification categories of the

4 Probability of failure occurence and criticality are illustrated for convenience

severity and also the probability of failure occurrence level (calculated criticality number) for each part failure modes. The final result of the criticality matrix shows the relative prioritizing for each part failures.

2.4. Research Gaps

Unfortunately, FMECA revealed some important shortcomings during the application in real industrial cases concerning the feasibility of the method, especially in terms of the qualitative approach of criticality analysis (Chang 2001). As shown in Figure 4, it seems difficult for decision- makers to prioritize the order of the maintenance tasks due to the fuzziness in the analysis. For instance, it doesn’t show which component has a stronger effect on the whole system as illustrated in the dashed columns (B-I and A-II). Many authors have underlined the FMECA’s several shortcomings in terms of its reliability and theoretical contribution. FMECA tool is a long and heavy task for any organization to undertake especially in terms of the necessary time for the tool development and the total of efforts put in the maintenance. During the FMECA performance, various imprecise and ambiguous information is noticed which generally lead to difficult failure analysis (Xu K. 2002).

3. Results and Discussion 3.1. System description

The assessment of the system risk and failure throughout FMECA is based on academic researches and experts experiences. The major aim of FMECA is to efficiently minimize system failure rates. The present study investigates the wind turbines as a complex system in terms of the hierarchy of the levels and with a long life-cycle (over 20 years). Moreover, the research comprises an FMECA team including five experts (𝑹_𝟏, 𝑹_𝟐, 𝑹_𝟑, 𝑹_𝟒, 𝑹_𝟓) on different levels of the

system to evaluate the potential failure modes. The three risk factors S, O, and D are taken into consideration through the risk priority number RPN evaluation as given in Equation 4.

A wind turbine is a complex system that transforms natural energy from the wind to electrical energy. Wind turbine system structure is hierarchical and entails numerous attached parts and components with each other to form a united assembly which produces the electrical. Figure 5 illustrates the main components and parts of the wind turbine assembly that help in generating electrical power. Table 4 presents an analysis of wind turbine sub-systems and components and describes the difference between the failure categories in components (aerodynamic, mechanical, and electrical). The wind turbine studied in this present research is B63 with 294 megawatts power rating and 63 meters long and 17 tonnes weights. It is one of North Africa’s largest and most powerful implanted wind turbine systems. The present study has identified twenty- five failure modes in the determined process, as explained in Table 4. The wind turbine system is divided into twelve main assemblies that comprise the twenty-five fatal failure modes. The wind turbine failure modes are outlined as follows: Gear teeth slip (𝑭𝑴_𝟏), Blade crack (𝑭𝑴_𝟐), Error in positioning (𝑭𝑴_𝟑), Fracture in the shell (𝑭𝑴_𝟒), Nacelle & Tower fracture (𝑭𝑴_𝟓), Electrical

Table 4:

Failure Modes in Wind Turbine

Assembly Failure mode

Wind Turbine Blade Gear teeth slip Blade crack Wind Turbine Hub Assembly Error in positioning

Fracture in the shell Nacelle & Tower Fracture

Pitch System

Electrical overload Low insulation level Excessive loading Yaw System Fatigue or excessive loading Hydraulic system (main brakes) Full speed

Over heating Main Shaft set

Main shaft vibration Main shaft malfunction Fatigue cracks Main Gearbox

Gearbox vibration Gearbox malfunction Gearbox abnormal noise

Generator

Excessive

fatigue/misalignment Misalignment with shaft poor lubrication

Overheating Frequency Converter System Shorting the circuit Centralized Lubrication system Overloading

Shorting circuit Power electrical system Fatigue

Distorting

overload (𝑭𝑴_𝟔), Low insulation level (𝑭𝑴_𝟕), Fatigue or excessive loading (𝑭𝑴_𝟗), Brakes full speed (𝑭𝑴_𝟏𝟎), Brakes overheating (𝑭𝑴_𝟏𝟏), Main shaft vibration (𝑭𝑴_𝟏𝟐), Main shaft malfunction (𝑭𝑴_𝟏𝟑), Fatigue cracks (𝑭𝑴_𝟏𝟒), Gearbox vibration (𝑭𝑴_𝟏𝟓), Gearbox malfunction(𝑭𝑴_𝟏𝟔), Gearbox abnormal noise (𝑭𝑴_𝟏𝟕), Generator excessive fatigue/misalignment (𝑭𝑴_𝟏𝟖), Misalignment with shaft poor lubrication (𝑭𝑴_𝟏𝟗), Generator overheating (𝑭𝑴_𝟐𝟎), Frequency converter system shorting the circuit (𝑭𝑴_𝟐𝟏), Centralised lubrication system overloading (𝑭𝑴_𝟐𝟐), Centralised lubrication system shorting circuit (𝑭𝑴_𝟐𝟑), Power electrical system

fatigue(𝑭𝑴_𝟐𝟒), Power electrical system distorting (𝑭𝑴_𝟐𝟓). However, a failure mode, effects and criticality analysis (FMECA) method of a system such as wind turbine very often requires a division of all the levels in the hierarchy from top to bottom. Figure 5 illustrates the hierarchical levels of the wind turbine assembly and the relationships between indenture level and final indenture level.

This scheme has been developed, based on deep knowledge in this area, to explain the interrelations among these levels given below.

Figure 5: hierarchical levels of the wind turbine Source: Own elaboration

Once the hierarchy of the system is developed, a series of consecutive questions come to decision-maker’s mind. For example, “why this sub-system failure mode happened?” , “what its effect on the rest of the parts and its severity on the whole system?”, and “how to detect future fatal failures?”, “how the risk could be prevented during the system life cycle?”…etc. Therefore, the most critical failures revealed in wind turbine system requires sophisticated approaches to detect.

3.2. RPN Technique Application

In the FMECA methodology, the numerical values are allocated to each sort of failure mode in the assembly to underline specific ranges of the risk. The core aim of the present method is to determine and control or even overcome risk within a macro system with hierarchical levels such as a wind turbine. Generally, FMECA is carried out by a team of experts involving maintenance, design, and logistics engineers whose experience is enough to reveal all risk factors. The RPN technique consigns numerical values to each failure mode linked with failure effect, utilizing Severity (S), Occurrence (O), and Detection as risk factors criteria. These sort criteria are so often computed by using a numerical scale, ranging from 1 to 10 (with 1 being the best and 10 being the worst) (Das et al.,

Table 5:

Possibility of a Failure Mode Occurrence (O) Source: M.K. Das et al., 2011

Rank Linguistic

Terms Meaning

Frequency of Failure Mode Occurrence 1 Remote Failure is unlikely 1/10⁶ 2

3 Low Relatively few

failures

1/20,000 1/4000 4

5 6

Moderate Occasional failures

1/1000 1/400 1/80 7

8 High Repeated failures 1/40

1/20 9

10 Very High Failure is almost inevitable

1/8

≥1/2

Table 6:

Possibility of a Failure Mode Severity (S) Source: M.K. Das et al., 2011

Rank Linguistic

Terms Meaning

1 Minor

Unreasonable to expect that the minor nature of this failure would have any real effect on the system performance. Customer probably will not even notice the failure.

2

3 Low

Failure causes only a slight customer annoyance. Customer will probably only notice a slight deterioration of the system performance.

4 5 6

Moderate

Failure causes some customer dissatisfaction. Customer is made uncomfortable or is annoyed by failure.

Customer will notice some system performance deterioration.

7

8 High

High degree of customer dissatisfaction due to the nature of the failure such as an inoperable vehicle or an inoperable convenience subsystem. Failure does not involve safety or noncompliance with government regulations.

9

10 Very High

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

Table 7:

Possibility of a Failure Mode Detection (D) Source: M.K. Das et al., 2011

Rank Linguistic

Terms Meaning 1

2 Very High Program will almost certainly detect a potential design weakness.

3

4 High Program has a good chance of detecting a potential design weakness.

5

6 Moderate Program may detect a potential design weakness.

7

8 Low Program is not likely to detect a potential design weakness.

9 Very Low Program probably will not detect a potential design weakness.

10 Non-

detection

Program cannot detect a potential design weakness or there is no design verification program.

2011). This technique is practical for risk analysis and revealing inherent failure in wind turbine assembly. The three risk factors S, O, and D are supposed to have similar importance during the assessment process. The RPN technique helps the FMECA team to determine the elements of the system that require urgent and immediate action for improvement. For instance, this quick intervention may lead to the design modifications in the system to mitigate the failure effect and reduce maintenance costs.

In this research work, the traditional method of the RPN is applied to the wind turbine system with the support of the FMECA team. The collected data are representative where Table 5 presents the linguistic terms of the failure mode occurrence frequency, ranging from 1 to 10. Table 6 proposes the linguistic terms of the failure mode severity on the whole system, ranging from 1 to 10. Table 7 illustrates the linguistic terms of the failure mode detection possibility, ranging from 1 to 10. In the present work, data have been gathered from five experts working in the renewable energy production area with a work experience ranging from 10 to 25 years and most of them have post- graduation qualifications. Based on the collected data, the traditional method of RPN including the risk criteria factors (Severity, Occurrence, and Detection) is applied as explained in Table 8, for the wind turbine B63 with a 294 megawatts power rating and 63 meters long and 17 tonnes weights and by appying Equation 4. Then, the ranking of the total values of the risk factors criteria is computed based on the sum values. The sub- systems and components were analysed and ranked, respectively. The components are ranked in descending order of their RPN values.

According to the obtained results, it is observed that the alternatives (19, 22, 25) are ranked as the most critical failure modes which means that the generator, centralized lubrication system, and power electrical system are the most critical components in the wind turbine assembly. After

reporting the team of experts about the obtained results, it is concluded that special efforts have be devoted to ameliorate the design of these critical components at the early stage of the project.

Besides, in the viewpoint of the risk evaluation, it is compulsory to assess the component that frequently fail and prepare an appropriate schedule for preventive maintenance strategy to prevent the collapse of the system in the future.

4. Conclusion

In system architecting, the complexity of systems develops as functions are subjoined.

Every time the new function is added within a system, its complexity rises exponentially. The goal of system architecting is to perceive the interrelashionships of all the complex system and offer a set of understandable parameters that develop future activities. Based on the procedure introduced in MIL-STD-1629A from the U.S Department of Defense, this paper studies the practicability of the risk priority numbers (RPN) method to determine the most critical failure modes. This risk assessment methodology has been applied to a wind turbine system B63 with a 294 megawatts power rating and its components were ranked and assessed, respectively. RPN has some limits considering the identification of the risks at early stage and the inherent subjectivity of the experts’ importance. As a solution to these boundaries, a fuzzy logic approach could be applied to diminish the risk and reveal the critical components in the wind turbine assembly in order to offer a reliable system that can produce clean energy without interruptions.

Table 8: Application of the Traditional RPNs in Wind Turbine Assembly

RISK FACTORS CRITERIA

ID FAILURE MODE ALTERNATIVES Occurrence Severity Detection RPN= (O)x(S)x(D) RANK

FM01 Gear teeth slip Alternative 1 2 9 4 72 14

FM02 Blade crack Alternative 2 1 10 2 20 20

FM03 Error in positioning Alternative 3 3 10 5 150 10

FM04 Fracture in the shell Alternative 4 1 9 1 9 22

FM05 Fracture Alternative 5 1 1 5 5 25

FM06 Electrical overload Alternative 6 5 5 7 175 8

FM07 Low insulation level Alternative 7 3 4 5 60 16

FM08 Excessive loading Alternative 8 3 5 8 120 11

FM09 Fatigue or excessive loading Alternative 9 6 9 2 108 13

FM10 Full speed Alternative 10 2 9 2 36 19

FM11 Over heating Alternative 11 8 4 9 288 5

FM12 Main shaft vibration Alternative 12 1 1 9 9 22

FM13 Main shaft malfunction Alternative 13 1 1 7 7 24

FM14 Fatigue cracks Alternative 14 1 2 6 12 21

FM15 Gearbox vibration Alternative 15 3 2 8 48 18

FM16 Gearbox malfunction Alternative 16 3 10 2 60 16

FM17 Gearbox abnormal noise Alternative 17 4 2 8 64 15

FM18 Excessive fatigue/misalignment Alternative 18 4 10 3 120 11

FM19 Misalignment with shaft poor lubrication Alternative 19 7 10 5 350 2

FM20 Overheating Alternative 20 6 10 4 240 6

FM21 Shorting the circuit Alternative 21 4 10 4 160 9

FM22 Overloading Alternative 22 7 6 8 336 3

FM23 Shorting circuit Alternative 23 8 4 6 192 7

FM24 Fatigue Alternative 24 8 5 8 320 4

FM25 Distorting Alternative 25 9 8 5 360 1

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