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.

In general, the amplitude-weighting method for an acoustic transducer array is widely used to improve the array directivity and reject disturbances. This paper presents a method to effectively reduce the side lobe level while minimizing the main lobe width increase. This is done using the simulated annealing algorithm (SAA) for a uniformly spaced arc array of omnidirectional underwater acoustic transducers, even at low signal-to-noise ratio (SNR). We propose a new cost function for the SAA and obtain the weighting coefficients for all array elements using the SAA, and next compare them with various amplitude weighting methods. Through simulation and comparison, it is verified that the proposed method is effective in beamforming of the uniform arc array of underwater acoustic transducers.