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Electric vehicle motor fault diagnosis using improved wavelet packet decomposition and particle swarm optimization algorithm

Zheng Wenfang, Wang Tieying

Archives of Electrical Engineering

2024

Vol. 73, nr 2

481--498

This study addresses the issue of diagnosing faults in electric vehicle motors and presents a method utilizing Improved Wavelet Packet Decomposition (IWPD) combined with particle swarm optimization (PSO). Initially, the analysis focuses on common demagnetization faults, inter turn short circuit faults, and eccentricity faults of permanent magnet synchronous motors. The proposed approach involves the application of IWPD for extracting signal feature vectors, incorporating the energy spectrum scale, and extracting the feature vectors of the signal using the energy spectrum scale. Subsequently, a binary particle swarm optimization algorithm is employed to formulate strategies for updating particle velocity and position. Further optimization of the binary particle swarm algorithm using chaos theory and the simulated annealing algorithm results in the development of a motor fault diagnosis model based on the enhanced particle swarm optimization algorithm. The results demonstrate that the chaotic simulated annealing algorithm achieves the highest accuracy and recall rates, at 0.96 and 0.92, respectively. The model exhibits the highest fault accuracy rates on both the test and training sets, exceeding 98.2%, with a minimal loss function of 0.0035. Following extraction of fault signal feature vectors, the optimal fitness reaches 97.4%. In summary, the model constructed in this study demonstrates effective application in detecting faults in electric vehicle motors, holding significant implications for the advancement of the electric vehicle industry.

Fuzzy approach for induction motor fault diagnosis

Zidani F., Abdessemed R., Nait Said M., Benbouzid M.E.H.

Archives of Electrical Engineering

2002

Vol. 51, nr 2

137-146

This paper is concerned in the motor fault detection and diagnosis. Using fuzzy logic strategy, a better understanding of the heuristics underlying the motor fault detection/diagnosis process can be achieved. The proposed fuzzy approach is based on the stator currents Park's vector pattern. Stator currents in an induction motor were measured, recorded and employed for computation of the stator currents Park's vector pattern under different operating conditions. Simulated experimental results are presented in terms of motor fault detection accuracy and knowledge extraction feasibility. The results show the effectiveness of the proposed method.

Artykuł poświęcony jest problematyce diagnostyki uszkodzeń silników. Zastosowanie logiki zbiorów rozmytych prowadzi do lepszego zrozumienia praktycznych metod detekcji i diagnozowania uszkodzeń. Proponowane zastosowanie zbiorów rozmytych oparte jest o Parkowskie wykresy wektorowe prądów stojana. Dokonano pomiarów i rejestracji przebiegu tych prądów i przeanalizowano wykresy Parka w różnych stanach pracy silników. Badania symulacyjne prowadzono pod kątem skuteczności i dokładności proponowanej metody w detekcji i diagnostyce uszkodzeń. Rezultaty wskazują, że metoda jest efektywna.