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Sensors 2017, 17(12), 2834; https://doi.org/10.3390/s17122834

Bearing Fault Diagnosis under Variable Speed Using Convolutional Neural Networks and the Stochastic Diagonal Levenberg-Marquardt Algorithm

School of Electrical, Electronics and Computer Engineering, University of Ulsan, 44610 Ulsan, Korea
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Received: 13 November 2017 / Revised: 5 December 2017 / Accepted: 6 December 2017 / Published: 6 December 2017
(This article belongs to the Special Issue Sensors for Fault Detection)
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Abstract

This paper presents a novel method for diagnosing incipient bearing defects under variable operating speeds using convolutional neural networks (CNNs) trained via the stochastic diagonal Levenberg-Marquardt (S-DLM) algorithm. The CNNs utilize the spectral energy maps (SEMs) of the acoustic emission (AE) signals as inputs and automatically learn the optimal features, which yield the best discriminative models for diagnosing incipient bearing defects under variable operating speeds. The SEMs are two-dimensional maps that show the distribution of energy across different bands of the AE spectrum. It is hypothesized that the variation of a bearing’s speed would not alter the overall shape of the AE spectrum rather, it may only scale and translate it. Thus, at different speeds, the same defect would yield SEMs that are scaled and shifted versions of each other. This hypothesis is confirmed by the experimental results, where CNNs trained using the S-DLM algorithm yield significantly better diagnostic performance under variable operating speeds compared to existing methods. In this work, the performance of different training algorithms is also evaluated to select the best training algorithm for the CNNs. The proposed method is used to diagnose both single and compound defects at six different operating speeds. View Full-Text
Keywords: acoustic emissions; bearing; fault diagnosis; convolutional neural networks; stochastic diagonal Levenberg-Marquardt algorithm; variable speed acoustic emissions; bearing; fault diagnosis; convolutional neural networks; stochastic diagonal Levenberg-Marquardt algorithm; variable speed
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Tra, V.; Kim, J.; Khan, S.A.; Kim, J.-M. Bearing Fault Diagnosis under Variable Speed Using Convolutional Neural Networks and the Stochastic Diagonal Levenberg-Marquardt Algorithm. Sensors 2017, 17, 2834.

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