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Time - frequency method and artificial neural network classifier for induction motor drive system defects classification

Behim Meriem, Merabet Leila, Saad Salah

Diagnostyka

2024

Vol. 25, No. 1

art. no. 2024110

In this paper, by introducing two statistical parameters, energy and L-kurtosis, a new fault diagnostic system combining Wavelet Packet Decomposition and Multilayer Perceptron Neural Network is designed to improve efficiency and precision of induction motor defects diagnosis. This method is applied to vibratory signals of asynchronous motor running at two different rotational speeds (1500 rpm and 2000 rpm) at a sampling frequency of 8 KHz to detect three main types of defects: bearing faults, load imbalance and misalignment. These speeds are considered as the usual medium running speeds of induction motor. According to the results, the high performance and accuracy of this new faults diagnostic system is proved and confirmed, thus it can be used in the detection of other machines defects.

Reactive energy compensator effect on the reliability of a complex electrical system using Bayesian networks

Reffas Omar, Sahraoui Yacine, Nahal Mourad, Ghoul Rachida Hadiby, Saad Salah

Eksploatacja i Niezawodność

2020

Vol. 22, no. 4

684--693

The static synchronous compensator is presented in order to improve an electrical network system performance. The present work aims to develop a Bayesian methodology for assessing the time-variant reliability of a complex electrical system taking into account reactive energy compensator (STATCOM). However, the complex aspect is not only related to the complexity of electrical system components architecture, nevertheless is allied to electrical network and STATCOM interactions. The Bayesian network is used for coping with this complexity constraint. The reliability-based assessment of reactive energy compensator effect is applied to a real case of a complex electrical system. The proposed Bayesian methodology application reveals that the STATCOM has a significant influence on electrical system reliability and the developed model can provide valuable information for decision makers to improve the system reliability performance.