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Open AccessArticle

A Deep Learning Model for Fault Diagnosis with a Deep Neural Network and Feature Fusion on Multi-Channel Sensory Signals

by Qing Ye 1,*, Shaohu Liu 2 and Changhua Liu 3
1
School of Computer Science, Yangtze University, Jingzhou 430023, China
2
School of Mechanical Engineering, Yangtze University, Jingzhou 430023, China
3
General Office, Yangtze University, Jingzhou 430023, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(15), 4300; https://doi.org/10.3390/s20154300
Received: 22 June 2020 / Revised: 28 July 2020 / Accepted: 30 July 2020 / Published: 1 August 2020
Collecting multi-channel sensory signals is a feasible way to enhance performance in the diagnosis of mechanical equipment. In this article, a deep learning method combined with feature fusion on multi-channel sensory signals is proposed. First, a deep neural network (DNN) made up of auto-encoders is adopted to adaptively learn representative features from sensory signal and approximate non-linear relation between symptoms and fault modes. Then, Locality Preserving Projection (LPP) is utilized in the fusion of features extracted from multi-channel sensory signals. Finally, a novel diagnostic model based on multiple DNNs (MDNNs) and softmax is constructed with the input of fused deep features. The proposed method is verified in intelligent failure recognition for automobile final drive to evaluate its performance. A set of contrastive analyses of several intelligent models based on the Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM) and the proposed deep architecture with single sensory signal and multi-channel sensory signals is implemented. The proposed deep architecture of feature extraction and feature fusion on multi-channel sensory signals can effectively recognize the fault patterns of final drive with the best diagnostic accuracy of 95.84%. The results confirm that the proposed method is more robust and effective than other comparative methods in the contrastive experiments. View Full-Text
Keywords: array signal processing; feature fusion; deep neural network; multi-channel sensory signals; intelligent fault diagnosis array signal processing; feature fusion; deep neural network; multi-channel sensory signals; intelligent fault diagnosis
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Ye, Q.; Liu, S.; Liu, C. A Deep Learning Model for Fault Diagnosis with a Deep Neural Network and Feature Fusion on Multi-Channel Sensory Signals. Sensors 2020, 20, 4300.

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