Title :
Mechanical fault diagnosis for L-V circuit breakers based on energy spectrum entropy of wavelet packet and Naive Bayesian classifier
Author :
Liu, Jiao-min ; Zhao, Jian-li ; Li, Li ; Wang, Ya-ning
Author_Institution :
Coll. of Electr. Autom., Hebei Univ. of Technol., Tianjin, China
Abstract :
The paper has extracted the energy spectrum entropy of wavelet packet as the eigen vector of fault patterns, through analyzing the vibration signal in the decomposition of wavelet packet when Low-Voltage (LV) Circuit Breaker broke down. Based on the concept of Clustering Center, a Naïve Bayesian classifier has been constructed. By using the weight of probability measure, the correlations between the eigen vector has been described. Thus the simulated fault diagnosis of the LV circuit breaker has been achieved. Through simulating, the efficiency of the method has been verified, which could fasten the computing speed, optimize the real-time performance and classification precision comparing with the neural network which uses black-box modeling.
Keywords :
acoustic signal processing; circuit breakers; eigenvalues and eigenfunctions; fault diagnosis; mechanical engineering computing; neural nets; wavelet transforms; L-V circuit breakers; black-box modeling; eigen vector; energy spectrum entropy; low-voltage circuit breaker; mechanical fault diagnosis; naive Bayesian classifier; neural network; vibration signal; wavelet packet; Bayesian methods; Circuit breakers; Circuit faults; Entropy; Fault diagnosis; Wavelet analysis; Wavelet packets; Clustering center; Energy spectrum entropy of wavelet packet; Low-voltage circuit breaker; Mechanical fault diagnosis; Naïve bayesian classifier;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
DOI :
10.1109/ICMLC.2010.5580947