Diagnosis Method for Gear Equipment by Sequential Fuzzy Neural Network

Authors:
Xiong Zhou;Huaqing Wang;Peng Chen;Jingwei Song
Affiliations:
College of Mechanical Engineering, Chongqing University, Chongqing, China;Graduate School of Bioresources, Mie University, Tsu, Mie, Japan 514-8507 and School of Mech. & Elec. Eng., Beijing University of Chemical Technology, Beijing, China;Graduate School of Bioresources, Mie University, Tsu, Mie, Japan 514-8507;Huadong Jiaotong University, China
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Year:
2008

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Automated function generation of symptom parameters and application to fault diagnosis of machinery under variable operating conditions

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

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Abstract

This paper proposes a new method called "sequential fuzzy neural network" to diagnose fault of gear equipment automatically and precisely. Symptom parameters in time domain, by which fault of the gear equipment can be detected and distinguished, are selected according to its values calculated from the signals measured in each state of gear equipment. The probability density functions are translated to possibility distribution functions by possibility theory to express the relationship between the gear condition and the symptom parameters. The fuzzy neural networks proposed in this paper can sequentially distinguish fault types of gear equipment. Examples of practical diagnosis are shown to verify the efficiency of this method.