Title: Fault diagnosis for hydraulic system on a modified multi-sensor information fusion method

Authors: Zengshou Dong; Xujing Zhang; Jianchao Zeng

Addresses: Department of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China ' Department of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China ' The System Simulation and Computer Application Research Laboratory, Department of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China

Abstract: A modified multi-sensor information fusion method for hydraulic fault diagnosing system is proposed in this paper. Combined with the improved JDL data fusion model and the hierarchical processing idea, it can solve some difficult fault diagnosis problems of hydraulic system. The adaptive weighted least squares estimation method is used to clean the data and extract the feature in data layer. The multi-parallel particle swarm optimisation (PSO)-Hopfield neural network is applied in feature level for local diagnosis. When the time-airspace integrates, there is a direct data communication and feedback between each level based on modified Dempster-Shafer (D-S) evidence theory in decision-making level. The final diagnosis has a direct data communication and feedback between each level, and it can make the information of each level based on data mining as soon as possible. Experimental results show that the method in conflicted evidence has high correct rate and can avoid index explosion and fix the fault exactly.

Keywords: modified D-S evidential theory; hierarchical fusion; improved JDL fusion model; hydraulic systems; fault diagnosis; PSO-Hopfield artificial neural networks; case analysis; multi-sensor fusion; sensor fusion; multiple sensors; particle swarm optimisation; PSO; ANNs.

DOI: 10.1504/IJMIC.2013.051931

International Journal of Modelling, Identification and Control, 2013 Vol.18 No.1, pp.34 - 40

Available online: 05 Feb 2013 *

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