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Sensors 2018, 18(5), 1523; https://doi.org/10.3390/s18051523

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN

1
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
2
College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
3
Faculty Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft 2628 CD, The Netherlands
*
Author to whom correspondence should be addressed.
Received: 31 March 2018 / Revised: 6 May 2018 / Accepted: 8 May 2018 / Published: 11 May 2018
(This article belongs to the Special Issue Sensors for Fault Detection)
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Abstract

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears. View Full-Text
Keywords: planetary gear; partition; feature extraction; degradation; VMD; SVD; CNN planetary gear; partition; feature extraction; degradation; VMD; SVD; CNN
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Liu, C.; Cheng, G.; Chen, X.; Pang, Y. Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN. Sensors 2018, 18, 1523.

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