首页|Real-Time Intelligent Diagnosis of Co-frequency Vibration Faults in Rotating Machinery Based on Lightweight-Convolutional Neural Networks
Real-Time Intelligent Diagnosis of Co-frequency Vibration Faults in Rotating Machinery Based on Lightweight-Convolutional Neural Networks
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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.
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
College of Mechanical Engineering,Hunan University of Science and Technology,Xiangtan 411201,China
国家自然科学基金国家自然科学基金北京市自然科学基金Beijing Municipal Youth Backbone Personal Project of China
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