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.

The paper proposes a failure diagnosis method for machinery in unsteady operating condition using instantaneous power spectrum (IPS) and genetic programming (GP). The IPS is used to extract feature frequency of each machine state from measured vibration signal for distinguishing failures by the Relative Crossing Information (RCI). Excellent symptom parameters for detecting failures are automatically generated by GP. The method proposed in the paper is verified by applying it to the failure diagnosis of rolling bearing.