Research on diagnosis method of motor vibration signal based on MSCNN-LSTM

Abstract Vibration signal is often considered an important basis for diagnosing motor faults. However, the original vibration signal features a single time series that needs to be shorter. This paper introduces a fault diagnosis approach, MSCNN-LSTM, which integrates a multi-scale one-dimensional convolutional neural network with a long short-term memory network, reflecting the ongoing advancements in deep learning for fault diagnosis. Convolution kernels of varying sizes are accustomed to realizing information integration of various scales and broadening the dimensions of vibration signals. In addition, one-dimensional convolution networks effectively solve the problem of excessively long timing of original signals, and LSTM captures timing dependence for diagnosing motor faults. The experimental results indicate that MSCNN-LSTM has high accuracy in motor fault diagnosis based on vibration signals.