Intelligent Motor Fault Diagnosis Method Based on Deep Learning
This paper delves into a deep learning-based intelligent motor fault diagnosis method,predominantly employing the long short-term memory(LSTM)model.Firstly,the fundamental principles of the LSTM model are thoroughly explored,elucidating its advantages in time series analysis.Secondly,a motor fault diagnosis method based on LSTM is proposed,involving deep learning modeling of motor vibration signals to accurately capture anomalous motor operating states.Extensive model testing is conducted using the CMAPSS dataset,validating the effectiveness and reliability of the proposed method in motor fault diagnosis.The research findings indicate that the method exhibits commendable performance in multi-category fault scenarios,providing an advanced and viable solution for intelligent health management of motor systems.
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