Rolling Bearing Diagnosis Based on LMD and Neural Network
Inner ring pitting, the outer indentation and rolling element wear are typical faults of rolling bearing. In order to diagnose these faults rapidly and accurately, the paper proposes a novel diagnosis method of rolling bearing based on the energy characteristics of PF component and neural network by the vibration signal of local mean decomposition(Local mean decomposition, LMD). The vibration signal is decomposed into several PF components by the local mean decomposition, the calculated energy characteristics of the PF component are inputted to the neural network to identify the type of rolling bearing faults. At the same time, the genetic algorithm is introduced to optimize the structure parameters of neural network, which improves diagnostic rate and accuracy of faults. The results show that this method has a higher diagnosis and recognition rate for the typical faults of rolling bearing.
Keywords: Rolling bearing, LMD, Genetic algorithms, Neural network, Fault diagnosis.
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ABOUT THE AUTHORS
Baoshan Huang
Baoshan Huang
1 National Key Laboratory of Vehicular Transmission, Beijing Institute of Technology, Beijing, China 2 Beijing Institute of Technology, Zhuhai, China
Wei
Wei Xu
University of Macau, Macao SAR
Xinfeng Zou
University of Macau, Macao SAR
Baoshan Huang
Baoshan Huang
1 National Key Laboratory of Vehicular Transmission, Beijing Institute of Technology, Beijing, China 2 Beijing Institute of Technology, Zhuhai, China
Wei
Wei Xu
University of Macau, Macao SAR
Xinfeng Zou
University of Macau, Macao SAR
