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Open AccessArticle

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

National Centre for Excellence in Food Engineering, Sheffield Hallam University, Sheffield S1 1WB, UK
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Author to whom correspondence should be addressed.
Sensors 2020, 20(18), 5112; https://doi.org/10.3390/s20185112
Received: 13 August 2020 / Revised: 26 August 2020 / Accepted: 3 September 2020 / Published: 8 September 2020
(This article belongs to the Section Fault Diagnosis & Sensors)
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. View Full-Text
Keywords: condition monitoring; fault diagnosis; deep learning; artificial intelligence condition monitoring; fault diagnosis; deep learning; artificial intelligence
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MDPI and ACS Style

Shenfield, A.; Howarth, M. A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults. Sensors 2020, 20, 5112.

AMA Style

Shenfield A, Howarth M. A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults. Sensors. 2020; 20(18):5112.

Chicago/Turabian Style

Shenfield, Alex; Howarth, Martin. 2020. "A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults" Sensors 20, no. 18: 5112.

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