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Sensors 2017, 17(2), 414; doi:10.3390/s17020414

An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

1
School of Mechanical Engineering, Tianjin University, Tianjin 300354, China
2
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 30 December 2016 / Revised: 11 February 2017 / Accepted: 16 February 2017 / Published: 21 February 2017
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [3709 KB, uploaded 21 February 2017]   |  

Abstract

A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. View Full-Text
Keywords: fault diagnosis; multi-sensor data fusion; deep convolutional neural networks; feature learning; planetary gearbox fault diagnosis; multi-sensor data fusion; deep convolutional neural networks; feature learning; planetary gearbox
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Jing, L.; Wang, T.; Zhao, M.; Wang, P. An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox. Sensors 2017, 17, 414.

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