Hybrid PSO based wavelet neural networks for intelligent fault diagnosis

Authors:
Qianjin Guo;Haibin Yu;Aidong Xu
Affiliations:
Shenyang Inst. of Automation, Chinese Academy of Sciences, Liaoning, China;Shenyang Inst. of Automation, Chinese Academy of Sciences, Liaoning, China;Shenyang Inst. of Automation, Chinese Academy of Sciences, Liaoning, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

Citing 4
Cited 2

Swarm intelligence

Swarm intelligence
Wavelet neural networks for function learning

IEEE Transactions on Signal Processing
Using wavelet network in nonparametric estimation

IEEE Transactions on Neural Networks
Accuracy analysis for wavelet approximations

IEEE Transactions on Neural Networks

Development and application of internet-based remote monitoring and diagnostic platform for key equipment

International Journal of Computer Integrated Manufacturing - Networked Manufacturing and Mass Customization in the ECommerce Era: the Chinese Perspective
Rolling bearings fault diagnosis based on adaptive gaussian chirplet spectrogram and independent component analysis

ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

A model of wavelet neural network (WNN) using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of Particle Swarm Optimization (PSO) and gradient descent algorithm (GD), and is thus called HGDPSO. The Particle Swarm Optimizer has previously been used to train neural networks and generally met with success. The advantage of the PSO over many of the other optimization algorithms is its relative simplicity and quick convergence. But those particles collapse so quickly that it exits a potentially dangerous property: stagnation, which state would make it impossible to arrive at the global optimum, even a local optimum. HGDPSO was proposed for neural network training to avoid premature and eliminate stagnation in PSO. The effectiveness of the HGDPSO based WNN is demonstrated through the classification of the fault signals in rotating machinery. The simulated results show its feasibility and validity.