Comparisons of different deep learning-based methods on fault diagnosis for geared system

Bing Han, Xiaohui Yang, Yafeng Ren and Wanggui Lan

International Journal of Distributed Sensor Networks, 2019, vol. 15, issue 11, 1550147719888169

Abstract: The running state of a geared transmission system affects the stability and reliability of the whole mechanical system. It will greatly reduce the maintenance cost of a mechanical system to identify the faulty state of the geared transmission system. Based on the measured gear fault vibration signals and the deep learning theory, four fault diagnosis neural network models including fast Fourier transform–deep belief network model, wavelet transform–convolutional neural network model, Hilbert-Huang transform–convolutional neural network model, and comprehensive deep neural network model are developed and trained respectively. The results show that the gear fault diagnosis method based on deep learning theory can effectively identify various gear faults under real test conditions. The comprehensive deep neural network model is the most effective one in gear fault recognition.

Keywords: Fault diagnosis; gear vibration; signal processing; neural network; deep learning (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:15:y:2019:i:11:p:1550147719888169

DOI: 10.1177/1550147719888169

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