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Appl. Sci. 2017, 7(1), 41; doi:10.3390/app7010041

Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery

1
School of Mechanical and Electric Engineering, Soochow University, Suzhou 215131, China
2
School of Urban Rail Transportation, Soochow University, Suzhou 215131, China
*
Author to whom correspondence should be addressed.
Academic Editor: David He
Received: 17 October 2016 / Revised: 12 December 2016 / Accepted: 27 December 2016 / Published: 30 December 2016
(This article belongs to the Special Issue Deep Learning Based Machine Fault Diagnosis and Prognosis)
View Full-Text   |   Download PDF [3207 KB, uploaded 30 December 2016]   |  

Abstract

Fault diagnosis in rotating machinery is significant to avoid serious accidents; thus, an accurate and timely diagnosis method is necessary. With the breakthrough in deep learning algorithm, some intelligent methods, such as deep belief network (DBN) and deep convolution neural network (DCNN), have been developed with satisfactory performances to conduct machinery fault diagnosis. However, only a few of these methods consider properly dealing with noises that exist in practical situations and the denoising methods are in need of extensive professional experiences. Accordingly, rethinking the fault diagnosis method based on deep architectures is essential. Hence, this study proposes an automatic denoising and feature extraction method that inherently considers spatial and temporal correlations. In this study, an integrated deep fault recognizer model based on the stacked denoising autoencoder (SDAE) is applied to both denoise random noises in the raw signals and represent fault features in fault pattern diagnosis for both bearing rolling fault and gearbox fault, and trained in a greedy layer-wise fashion. Finally, the experimental validation demonstrates that the proposed method has better diagnosis accuracy than DBN, particularly in the existing situation of noises with superiority of approximately 7% in fault diagnosis accuracy. View Full-Text
Keywords: fault diagnosis; deep learning; stacked denoising autoencoder; feature extraction; integrated deep fault recognizer fault diagnosis; deep learning; stacked denoising autoencoder; feature extraction; integrated deep fault recognizer
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Guo, X.; Shen, C.; Chen, L. Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery. Appl. Sci. 2017, 7, 41.

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