A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults

SHENFIELD, Alex and HOWARTH, Martin (2020). A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults. Sensors, 20 (18), e5112.

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Open Access URL: https://www.mdpi.com/1424-8220/20/18/5112 (Published version)
Link to published version:: https://doi.org/10.3390/s20185112


Real-time acquisition of large amounts of machine operating data is now increasingly common due to recent advances in Industry 4.0 technologies. A key benefit to factory operators of this large scale data acquisition is in the ability to perform real-time condition monitoring and early-stage fault detection and diagnosis on industrial machinery—with the potential to reduce machine down-time and thus operating costs. The main contribution of this work is the development of an intelligent fault diagnosis method capable of operating on these real-time data streams to provide early detection of developing problems under variable operating conditions. We propose a novel dual-path recurrent neural network with a wide first kernel and deep convolutional neural network pathway (RNN-WDCNN) capable of operating on raw temporal signals such as vibration data to diagnose rolling element bearing faults in data acquired from electromechanical drive systems. RNN-WDCNN combines elements of recurrent neural networks (RNNs) and convolutional neural networks (CNNs) to capture distant dependencies in time series data and suppress high-frequency noise in the input signals. Experimental results on the benchmark Case Western Reserve University (CWRU) bearing fault dataset show RNN-WDCNN outperforms current state-of-the-art methods in both domain adaptation and noise rejection tasks.

Item Type: Article
Additional Information: ** From MDPI via Jisc Publications Router ** Licence for this article: https://creativecommons.org/licenses/by/4.0/ **Journal IDs: eissn 1424-8220 **History: published 08-09-2020; accepted 03-09-2020
Uncontrolled Keywords: condition monitoring, fault diagnosis, deep learning, artificial intelligence
Identification Number: https://doi.org/10.3390/s20185112
Page Range: e5112
SWORD Depositor: Colin Knott
Depositing User: Colin Knott
Date Deposited: 11 Sep 2020 16:22
Last Modified: 17 Mar 2021 22:47
URI: https://shura.shu.ac.uk/id/eprint/27196

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