Intelligent diagnosis method of multi-fault state for plant machinery using wavelet analysis, genetic programming and possibility theory

Abstract

This paper proposes an intelligent diagnosis method for plant machinery in multi-fault state using wavelet analysis, genetic programming (GP), and possibility theory. The wavelet analysis is used to extract feature spectra of multi-fault state from measured vibration signal for the diagnosis. Excellent symptom parameters for distinguishing fault states are automatically generated by GP. Because the value of symptom parameter calculated to express the feature of the vibration signal fluctuates even if machine state does not change, fuzzy diagnosis is necessary. After obtaining the excellent symptom parameters by GP called GP-SPs, the membership functions of GP-SPs are needed for fuzzy diagnosis. We also discuss the identification method of membership function of symptom parameters using probability theory and possibility theory, and show the inference method for identifying faults types. The methods proposed in this paper are verified by applying them to the diagnosis of rolling bearing in multi-fault state.