Fault Prognosis of Gearbox Based on Convolutional LSTM Autoencoder using Current Signal Data

초록

Fault prognosis and accurate prediction of gearbox remaining useful life (RUL) are challenging due to the lack of mathematical models and external factors like operating conditions and ambient temperature. Data-driven approaches are widely used for fault prognosis and RUL prediction, but extracting informative features from data is time-consuming and challenging. In this study, we propose an effective deep learning model, Inception Convolutional long short-term memory (LSTM) Autoencoder (ICLSTMAE), for automatic extraction of degradation features from run-to-fail data in both the time and frequency domains. The ICLSTMAE model using convolutional LSTM (CLSTM), that combines the advantages of convolutional neural networks (CNNs) and LSTM networks to extract both spatial and temporal features from the data. In the proposed method, the extracted degradation features are from current signals collected from the control motor attached to the gearbox, and they are used as input to a deep LSTM (DLSTM) model for RUL prediction. Experiments on a gearbox failure testing system demonstrated the effectiveness of the proposed method compared to the method developed in previous studies.

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