A hybrid PSO-GD based intelligent method for machine diagnosis

• Published in: Digital signal processing Vol. 16; no. 4; pp. 402 - 418
• Main Authors: Guo, Qian-jin, Yu, Hai-bin, Xu, Ai-dong
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.07.2006
• Subjects: Gradient descent algorithm; Machine diagnosis; Particle swarm optimization; Wavelet neural network; Machine diagnosis; Wavelet neural network; Particle swarm optimization; Gradient descent algorithm
• ISSN: 1051-2004; 1095-4333
• DOI: 10.1016/j.dsp.2005.12.004

This paper presents an intelligent methodology for diagnosing incipient faults in rotating machinery. In this fault diagnosis system, wavelet neural network techniques are used in combination with a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of the constriction factor approach for particle swarm optimization (PSO) technique and the gradient descent (GD) technique, and is thus called HGDPSO. The HGDPSO is developed in such a way that a constriction factor approach for particle swarm optimization (CFA for PSO) is applied as a based level search, which can give a good direction to the optimal global region, and a local search gradient descent (GD) algorithm is used as a fine tuning to determine the optimal solution at the final. 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.