A framework for final drive simultaneous failure diagnosis based on fuzzy entropy and sparse bayesian extreme learning machine.
This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on F 1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach.
School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430000, China.
Qing
Q
Ye
School of Computer Science and Technology, Wuhan University of Technology, Wuhan 430000, China.
Hao
H
Pan
Yangtze University College of Technology and Engineering, Jingzhou 430023, China.
Changhua
C
Liu
D001185:Artificial Intelligence; D001499:Bayes Theorem; D019277:Entropy; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D060388:Support Vector Machine