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

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.