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Gear Fault Diagnosis through Vibration and Acoustic Signal Combination Based on Convolutional Neural Network

by Liya Yu 1, Xuemei Yao 2,3,*, Jing Yang 1 and Chuanjiang Li 1
1
School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
2
Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China
3
School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang 550025, China
*
Author to whom correspondence should be addressed.
Information 2020, 11(5), 266; https://doi.org/10.3390/info11050266
Received: 29 March 2020 / Revised: 23 April 2020 / Accepted: 7 May 2020 / Published: 14 May 2020
Equipment condition monitoring and diagnosis is an important means to detect and eliminate mechanical faults in real time, thereby ensuring safe and reliable operation of equipment. This traditional method uses contact measurement vibration signals to perform fault diagnosis. However, a special environment of high temperature and high corrosion in the industrial field exists. Industrial needs cannot be met through measurement. Mechanical equipment with complex working conditions has various types of faults and different fault characterizations. The sound signal of the microphone non-contact measuring device can effectively adapt to the complex environment and also reflect the operating state of the device. For the same workpiece, if it can simultaneously collect its vibration and sound signals, the two complement each other, which is beneficial for fault diagnosis. One of the limitations of the signal source and sensor is the difficulty in assessing the gear state under different working conditions. This study proposes a method based on improved evidence theory method (IDS theory), which uses convolutional neural network to combine vibration and sound signals to realize gear fault diagnosis. Experimental results show that our fusion method based on IDS theory obtains a more accurate and reliable diagnostic rate than the other fusion methods. View Full-Text
Keywords: vibration signal; acoustic signal; fault diagnosis; convolutional neural network; data fusion vibration signal; acoustic signal; fault diagnosis; convolutional neural network; data fusion
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Yu, L.; Yao, X.; Yang, J.; Li, C. Gear Fault Diagnosis through Vibration and Acoustic Signal Combination Based on Convolutional Neural Network. Information 2020, 11, 266.

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