Automotive Motor Fault Diagnosis Model Integrating Machine Learning Algorithm and Fuzzy Control Theory

Aiming at the inaccuracy and instability of traditional automatic fault diagnosing methods, the study proposes to construct a fault diagnosing algorithm on the foundation of the improved particle swarm algorithm and Perit net based on fuzzy strategy, and to construct a fault diagnosis model of the improved automobile motor in this way. The performance verification of the constructed fault diagnosis algorithm finds that the accuracy of the algorithm is 98.7%, the mean square error is 1.7, and the average absolute percentage error is 2.4 x 10-2, which is better than other algorithms. In addition, the study also validates the effectiveness of the motor fault diagnosing model and finds that it has an accuracy of 0.94, a precision rate of 87.2%, an average error of 0.15, an area under the line of the subject's characteristic curve of 0.76, an F1 value of 0.75, and a running time of 3.6 s. Summing up the above results, the research constructed by the study that integrates machine learning algorithms and fuzzy control theory automobile motor fault diagnosis model has excellent performance. The innovation of this study is to combine machine learning optimization algorithms with fuzzy control theory, which provides new ideas and methods for motor fault diagnosis, significantly improves the accuracy and stability of diagnosis. At the same time, the application scope of fuzzy systems has been successfully expanded to the fault detection and diagnosis of automotive motors, providing a demonstration for the integration of fuzzy systems with other advanced technologies. The improved motor fault diagnosis model constructed through research can meet social needs and provide new reference and guidance for the development of automotive motor fault diagnosis technology.