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Appl. Sci. 2017, 7(5), 515;

Detection of Pitting in Gears Using a Deep Sparse Autoencoder

School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China
Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
College of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
Author to whom correspondence should be addressed.
Academic Editor: César M. A. Vasques
Received: 15 March 2017 / Revised: 5 May 2017 / Accepted: 12 May 2017 / Published: 16 May 2017
(This article belongs to the Special Issue Deep Learning Based Machine Fault Diagnosis and Prognosis)
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In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network. Sparse coding with dictionary learning is viewed as an adaptive feature extraction method for machinery fault diagnosis. An autoencoder is an unsupervised machine learning technique. A stacked autoencoder network with multiple hidden layers is considered to be a deep learning network. The presented method uses a stacked autoencoder network to perform the dictionary learning in sparse coding and extract features from raw vibration data automatically. These features are then used to perform gear pitting fault detection. The presented method is validated with vibration data collected from gear tests with pitting faults in a gearbox test rig and compared with an existing deep learning-based approach. View Full-Text
Keywords: gear; pitting detection; deep sparse autoencoder; vibration; deep learning gear; pitting detection; deep sparse autoencoder; vibration; deep learning

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Qu, Y.; He, M.; Deutsch, J.; He, D. Detection of Pitting in Gears Using a Deep Sparse Autoencoder. Appl. Sci. 2017, 7, 515.

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