Fault Diagnosis in Rotating Machinery Using Fuzzy Measures and Fuzzy Integrals

In the fault diagnosis of rotating machinery using fuzzy measures and fuzzy integrals, the optimization of membership functions and identification of fuzzy measures are important for accurate diagnosis. Herein, a method for optimizing membership functions is proposed based on the statistical properties of vibration spectra and identifying fuzzy measures based on interaction levels using partial correlation coefficients between spectra. The possibility of a given fault is obtained from fuzzy integrals using membership degrees determined by the membership function, and the fuzzy measures for the set of spectra. The method is also evaluated using the example of diagnosis of misalignment and unbalance faults.