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.

[img]
Preview
PDF
sensors-20-05112.pdf - Published Version
Creative Commons Attribution.

Download (12MB) | Preview
Open Access URL: https://www.mdpi.com/1424-8220/20/18/5112 (Published version)
Link to published version:: https://doi.org/10.3390/s20185112
Related URLs:

    Abstract

    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: 11 Sep 2020 16:22
    URI: http://shura.shu.ac.uk/id/eprint/27196

    Actions (login required)

    View Item View Item

    Downloads

    Downloads per month over past year

    View more statistics