Bearing Vibration Signal Fault Diagnosis Based on LSTM-Cascade CatBoost

Abstract

Bearing is one of the most concerned parts in the field of fault diagnosis. At present, there are numerous excellent algorithms applied to bearing fault detection. This paper proposes a new fault bearings diagnosis model named LSTM-Cascade CatBoost, which can directly classify bearing vibration signals in the case of multiple granularity and high dimensions without signal processing. The model is based on gcForest, whose complexity can be adjusted automatically to the size of data set and it uses LSTM to extract features of time series signals instead of multi-grained scanning for improving the model’s feature extraction ability. CatBoost is used as the base classifier of cascade forest to improve the classification accuracy of the model. Experimental results show the fact that this model is highly accurate in CWRU and XJTU-SY datasets. Besides, it not only proves that the feature extraction ability of LSTM is significantly better than that of multi-grained scanning, but CatBoost as a base classifier can further improve the accuracy of cascade forest.