End-To-End Convolutional Neural Network Model for Gear Fault Diagnosis Based on Sound Signals
Source abstract
Abstract
Currently gear fault diagnosis is mainly based on vibration signals with a few studies on acoustic signal analysis. However, vibration signal acquisition is limited by its contact measuring while traditional acoustic-based gear fault diagnosis relies heavily on prior knowledge of signal processing techniques and diagnostic expertise. In this paper, a novel deep learning-based gear fault diagnosis method is proposed based on sound signal analysis. By establishing an end-to-end convolutional neural network (CNN), the time and frequency domain signals can be fed into the model as raw signals without feature engineering. Moreover, multi-channel information from different microphones can also be fused by CNN channels without using an extra fusion algorithm. Our experiment results show that our method achieved much better performance on gear fault diagnosis compared with other traditional gear fault diagnosis methods involving feature engineering. A publicly available sound signal dataset for gear fault diagnosis is also released and can be downloaded as instructed in the conclusion section.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Music and Audio Processing · Machine Fault Diagnosis Techniques · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
Guizhou Provincial Key Laboratory of Internet Collaborative Intelligent Manufacturing, Guizhou University, Guiyang 550025, China
Key Laboratory of Advanced Manufacturing Technology of Ministry of Education, Guizhou University, Guiyang 550025, China
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
National Institute of Measurement and Testing Technology, Chengdu 610021, China
Funding
National Natural Science Foundation of China · Science and Technology Project of Guizhou Province
References (31)
Show source-provided references
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