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Appl. Sci. 2018, 8(11), 2332; https://doi.org/10.3390/app8112332

Effective Prediction of Bearing Fault Degradation under Different Crack Sizes Using a Deep Neural Network

1
School of IT Convergence, University of Ulsan, Ulsan 44610, Korea
2
School of Electronics and Computer Engineering, Chonnam National University, Gwangju 61186, Korea
*
Author to whom correspondence should be addressed.
Received: 24 October 2018 / Revised: 12 November 2018 / Accepted: 19 November 2018 / Published: 21 November 2018
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
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Abstract

Exact evaluation of the degradation levels in bearing defects is one of the most essential works in bearing condition monitoring. This paper proposed an efficient evaluation method using a deep neural network (DNN) for correct prediction of degradation levels of bearings under different crack size conditions. An envelope technique was first used to capture the characteristic fault frequencies from acoustic emission (AE) signals of bearing defects. Accordingly, a health-related indicator (HI) calculation was performed on the collected envelope power spectrum (EPS) signals using a Gaussian window method to estimate the fault severities of bearings that served as an appropriate dataset for DNN training. The proposed DNN was then trained for effective prediction of bearing degradation using the Adam optimization-based backpropagation algorithm, in which the synaptic weights were optimally initialized by the Xavier initialization method. The effectiveness of the proposed degradation prediction approach was evaluated through different crack size experiments (3, 6, and 12 mm) of bearing faults. View Full-Text
Keywords: degradation levels; bearing defects; AE signal; DNN; envelope spectrum analysis degradation levels; bearing defects; AE signal; DNN; envelope spectrum analysis
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Nguyen, H.N.; Kim, C.-H.; Kim, J.-M. Effective Prediction of Bearing Fault Degradation under Different Crack Sizes Using a Deep Neural Network. Appl. Sci. 2018, 8, 2332.

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