Affiliation:
1. Department of Integrated Technology and Control Engineering, School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China
2. Aeronautical Radio Electronics Research Institute, Shanghai 200233, China
Abstract
Mechanical fault prediction is one of the main problems in condition-based maintenance, and its purpose is to predict the future working status of the machine based on the collected status information of the machine. However, on one hand, the model health indices based on the information collected by the sensors will directly affect the evaluation results of the system. On the other hand, because the model health index is a continuous time series, the effect of feature learning on continuous data also affects the results of fault prognosis. This paper makes full use of the autonomous information fusion capability of the stacked autoencoder and the strong feature learning capability of continuous deep belief networks for continuous data, and proposes a novel fault prognosis method. Firstly, a stacked autoencoder is used to construct the model health index through the feature learning and information fusion of the vibration signals collected by the sensors. To solve the local fluctuations in the health indices, the exponentially weighted moving average method is used to smooth the index data to reduce the impact of noise. Then, a continuous deep belief network is used to perform feature learning on the constructed health index to predict future performance changes in the model. Finally, a fault prognosis experiment based on bearing data was performed. The experimental results show that the method combines the advantages of stacked autoencoders and continuous deep belief networks, and has a lower prediction error than traditional intelligent fault prognosis methods.
Funder
National Research Projects of China
Subject
Control and Optimization,Control and Systems Engineering
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