Two-stage prediction of machinery fault trend based on deep learning for time series analysis

• Published in: Digital signal processing Vol. 117; p. 103150
• Main Authors: Xu, Hongling, Ma, Ruizhe, Yan, Li, Ma, Zongmin
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.10.2021
• Subjects: Extra-tree; Fault trend; LSTM; Random forest; Time series; Random forest; LSTM; Fault trend; Time series; Extra-tree
• ISSN: 1051-2004; 1095-4333
• DOI: 10.1016/j.dsp.2021.103150

Fault prediction technology provides a way to reduce the loss caused by equipment failure. Currently, many efforts of failure prediction pay more attention to the fault trend and the remaining useful life. This paper devotes to predicting a specific failure mode of device in the future. We propose a two-step prediction method with two sub-models to predict the fault mode. The first model, named regression model, combines Attention-based-LSTM (Long Short-Term Memory) and Random Forest (RF) to predict time series trends, where wavelet packet analysis is adopted to extract elementary features of time series and form multivariable time series. Based on the status data forecasted by the first model, the second model, named classification model, combines Attention-based-LSTM and Extra-Tree (ET) to classify fault mode. The classified fault will occur in the device at some point in the future. In the regression model, Attention-based-LSTM performs deep level feature extraction of vibration sensor signals, and the result is fed to the last layer (i.e., an RF layer), which takes responsibility for predicting the future sequence. The future sequence is then provided to the classification model, where Attention-based-LSTM is used to extract implicit features, and ET classifier is used to accept these features and figure out the failure phenomenon. Our approach for predicting failure modes includes the severity and type of fault, which are rarely investigated synthetically. The experimental results of bearing data show that the sub-models can achieve high prediction accuracy and our method can predict the future failure mode with precision.