Fault Diagnosis Research of Hydraulic Excavator Based on Fault Tree and Fuzzy Neural Network

Abstract:

Directing to the dispersiveness and faintness failure characteristics of hydraulic excavator, the fault diagnosis method was presented based on the fault tree and fuzzy neural network. On the basis of analysis of the hydraulic excavator system works, the fault tree model of hydraulic excavator was built by using fault diagnosis tree. And then, utilizing the example of hydraulic excavator fault diagnosis, the method of building neural network, obtaining training samples and neural network learning in the process of intelligent fault diagnosis are expounded. And the status monitoring data of hydraulic excavator was used as the sample data source. Using fuzzy logic methods the samples were blurred. The fault diagnosis of hydraulic excavator was achieved with BP neural network. The experimental result demonstrated that the information of sign failure was fully used through the algorithm. The algorithm was feasible and effective to fault diagnosis of hydraulic excavator. A new diagnosis method was proposed for fault diagnosis of other similar device.

• Periodical: Applied Mechanics and Materials (Volumes 303-306)
• Pages: 1350-1356
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.303-306.1350
• Online since: February 2013
• Authors: Guo Ping Li, Qing Wei Zhang, Ma Xiao
• Keywords: BP Neural Network (BPNN), Fault Diagnosis, Fault Tree Analysis (FTA), Fuzzy Logic, Hydraulic Excavator

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