Fault Diagnosis of New Certain Mine Sweeping Plough’s Electrical Control System Based on Data Fusion

Abstract:

The fault diagnosis of electrical control system of certain type mine sweeping vehicle is difficult due to its complex structure and advanced technique. So in the multi-sensor failure diagnosis process, as a result of various reasons, such as the existence of measurement noise, diagnosis knowledge incomplete and so on, it makes the fault diagnosis uncertainty and affects the reliability and the accuracy of the diagnosis result. This article according to the analysis of electrical control system's fault characteristic of the mine sweeping plough’s, proposes a technique based on data fusion fault diagnosis method. The diagnosis process is divided into the sub system and the system-level, the subsystem uses the BP neural network to classify the fault mode, the system-level uses the D-S evidence theory carries on the comprehensive decision judgment for the whole system's fault. Application shows if some sub-neural network diagnosis has error, using D-S evidence theory fusion can effectively improve the accuracy of diagnosis.

• Periodical: Applied Mechanics and Materials (Volumes 713-715)
• Pages: 539-543
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.713-715.539
• Online since: January 2015
• Authors: Yong Zhao, Xiao Qiang Yang*, Yin Hua Xu, Jian Bin Li
• Keywords: BP Neural Network, Data Fusion, Fault Diagnosis, Mine Sweeping Plough

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