Automated fault detection without seismic processing

Once the transformer is connected to the bus bar, it will feed several lines where the loads are connected. The figure 2.4 represents a solidly grounded distribution network with

The results show that the simple representation, combined with an evolutionary design is better able to predict training times than a more random data generation process (with a

A novel method, named with Data Fine-pruning training, is developed to reduce the time consumption of training a model by sensibly and temporarily pruning a fraction of input

As in the case of the multivariate control charts ( T 2 or MEWMA), we will fix a threshold (limit) on the a posteriori probabilities allowing to take decisions on the process: if,

In order to evaluate the performances of our detection method with bayesian network, and to compare them to others detection methods (T² and MEWMA control charts), we have chosen

The section IV presents a new method in order to take into account non- Gaussian signal in the fault detection with Bayesian network.. We evaluate this proposed method on a

In fact, most of us (and I include myself here) have done rather worse than that – in that having identified the need to think beyond growth we have then reverted to the more

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