Application of SVDD Single Categorical Data Description in Motor Fault Identification Based on Health Redundant Data
The system’s self-protection mechanism immediately stops the motor when motor system in the event of a malfunction, so it is difficult to collect the fault data when monitoring the motor status. Under the premise of only collecting motor’s health data, using SVDD algorithm to train health data and building non-health data sets based on practical experience in this paper. Based on BP neural network, a random self-adapting particle swarm optimization algorithm (RSAPSO) is used to substitute the original gradient descent method in BP network, training speed and accuracy of BP network training is improved. Three commonly used test functions were used to test the performance of the improved PSO, and the improved particle swarm optimization is compared with the standard particle swarm optimization, particle swarm optimization with compression factor and adaptive particle swarm optimization. In this paper, three asynchronous motor Y225S-4 output shaft vibration acceleration signal in healthy state as a case to test the effectiveness of the algorithm, results show that in the case of only health data, the new algorithm based on single classification has better performance and can effectively monitor the working state of the motor.
KeywordsFault identification Health redundant data Support vector data description Particle swarm optimization
This research is supported by National Program on Key Basic Research Project of China (973 Program) (2014CB046306) and National Natural Science Foundation of China under Grant 61573361.
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