In this study, a new effective approach for detection and classification of stator winding faults in induction motors is presented. The approach is based on current analysis. It uses multiple features extraction techniques, where Park transform, zero crossing time signal, and the envelope are extracted from the three‐phase stator currents. Then, statistical features are calculated from time and frequency domains of each extracted signal. The Features selection techniques (ReliefF, minimum redundancy and max relevancy, and support vector machine approach based on recursive feature elimination) are used to select from the extracted features the most relevant ones. As a classifier, the self‐organising map neural network is used. The proposed procedure is experimentally studied using stator current signals obtained from various faulty cases and a healthy induction motor at different load variations. The experimental results verify that the proposed strategy is able to distinguish the faulty cases from the healthy ones. Also, it effectively identifies the faulty phase in addition to the extent of the fault.

Misalignment is one of the most common mechanical faults in electrical rotating machinery, it can lead to partial or total breakdown of a motor in the long run may. This paper investigates the application of the Autoregressive Model of torque signal to detect and diagnose the misalignment fault. First, the torque signal obtained from experiment in different conditions: healthy condition, angular misalignment, and parallel misalignment fault are normalized to center the signal at zero mean and scales it to unit standard deviation. Then the AR model coefficients are extracted as features. Min Redundancy max Relevance (mRMR) feature selection technique is used to select the optimal features and reduce the dimension data. Finally, the Self Organizing Map neural network is used for classification of the different conditions. The experimental results show the effectiveness of the proposed method, were both angular and parallel misalignment might be easily detected and discriminated from each other.

In this paper, we present a fuzzy logic based vector control for a permanent magnet synchronous machine (PMSM). Two schemes of fuzzy controllers are used, a fuzzy PI controller and a fuzzy parameters PI controller with adaptive parameters (FPPIC). The performances of the proposed Fuzzy Logic Controllers (FLC) are investigated and compared to those obtained from classical proportional-integral controllers (PIC) at different dynamic operating conditions. In the vector control scheme, the FLC controllers are used in the speed loop. The results obtained in the simulated tests show that the FLC is more robust and hence found to be suitable in high performance PMSM speed control when FPPIC presents the best dynamics performances in start-up part and in reference tracking.

Mechanical faults present a large portion of induction motor failures, if left undetected, it can lead to partial or total breakdown of the machine. This paper propose a scheme to detect and diagnose mechanical faults in an induction motor by the AR model coefficients of the Torque signal. First, the torque signal obtained from experiment in different conditions: healthy condition, motor with dynamic eccentricity fault, and motor with misalignment fault are normalized to exclude the load effect. Then the AR model coefficients are extracted as features to reduce the dimension data while keeping the effective information. Finally, the Self Organizing Map neural network is used for classification of the different conditions. The experimental results show the effectiveness of the proposed method, were both eccentricity and misalignment faults might be easily detected and discriminated from each other.