中国机械工程学报2024，Vol.37Issue(2) ：264-282.DOI:10.1186/s10033-024-01021-9

Real-Time Intelligent Diagnosis of Co-frequency Vibration Faults in Rotating Machinery Based on Lightweight-Convolutional Neural Networks

Xin Pan Xiancheng Zhang Zhinong Jiang Guangfu Bin
中国机械工程学报2024，Vol.37Issue(2) ：264-282.DOI:10.1186/s10033-024-01021-9
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Real-Time Intelligent Diagnosis of Co-frequency Vibration Faults in Rotating Machinery Based on Lightweight-Convolutional Neural Networks

Xin Pan 1Xiancheng Zhang 2Zhinong Jiang 1Guangfu Bin3
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作者信息

  • 1. Beijing Key Laboratory of Health Monitoring and Self-recovery for High-End Mechanical Equipment,College of Mechanical and Electrical Engineering,Beijing University of Chemical Technology,Beijing 100029,China;Key Laboratory of Engine Health Monitoring-Control and Networking(Ministry of Education),College of Mechanical and Electrical Engineering,Beijing University of Chemical Technology,Beijing 100029,China
  • 2. Beijing Key Laboratory of Health Monitoring and Self-recovery for High-End Mechanical Equipment,College of Mechanical and Electrical Engineering,Beijing University of Chemical Technology,Beijing 100029,China
  • 3. College of Mechanical Engineering,Hunan University of Science and Technology,Xiangtan 411201,China
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Abstract

The co-frequency vibration fault is one of the common faults in the operation of rotating equipment,and realizing the real-time diagnosis of the co-frequency vibration fault is of great significance for monitoring the health state and carrying out vibration suppression of the equipment.In engineering scenarios,co-frequency vibration faults are highlighted by rota-tional frequency and are difficult to identify,and existing intelligent methods require more hardware conditions and are exclusively time-consuming.Therefore,Lightweight-convolutional neural networks(LW-CNN)algorithm is proposed in this paper to achieve real-time fault diagnosis.The critical parameters are discussed and verified by simulated and experimental signals for the sliding window data augmentation method.Based on LW-CNN and data augmentation,the real-time intel-ligent diagnosis of co-frequency is realized.Moreover,a real-time detection method of fault diagnosis algorithm is proposed for data acquisition to fault diagnosis.It is verified by experiments that the LW-CNN and sliding window methods are used with high accuracy and real-time performance.

Key words

Co-frequency vibration/Real-time diagnosis/LW-CNN/Data augmentation

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基金项目

国家自然科学基金(51875031)

国家自然科学基金(52242507)

北京市自然科学基金(3212010)

Beijing Municipal Youth Backbone Personal Project of China(2017000020124 G018)

出版年

2024
中国机械工程学报
中国机械工程学会

中国机械工程学报

CSTPCD
影响因子：0.765
ISSN：1000-9345
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