Abstract
With the development of Industry 4.0, not only the equipment but also the operational conditions in industrial manufacturing are becoming more and more complex. It is necessary to diagnose failures, whose probability is now increasing violently. As a typical deep learning model, the Deep Belief Network (DBN) can be employed to extract features from the original data directly. Compared with traditional fault diagnosis methods, the DBN can get rid of the dependence on signal processing technology and diagnosis experience. In this paper, the fault diagnosis approach based on DBN is studied to identify the bearing failure. First of all, the basic principles of DBN and the steps of fault diagnosis are described. Then some key parameters of DBN which affect the fault identification performance are analyzed and determined according to the simulation experiments. The practicability of this method is verified by comparing with Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) at last.
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Dan, Q., Liu, X., Chai, Y., Zhang, K., Li, H. (2019). A Fault Diagnosis Approach Based on Deep Belief Network and Its Application in Bearing Fault. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2018 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 528. Springer, Singapore. https://doi.org/10.1007/978-981-13-2288-4_29
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DOI: https://doi.org/10.1007/978-981-13-2288-4_29
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