Skip to Main Content
In order to effectively diagnose faults for rotating machinery in the variable rotating speed, a novel diagnosis method is proposed based on time-frequency analysis techniques, the automatic feature extraction method, and fuzzy inference. The diagnosis sensitivities of three time-frequency analysis methods, namely, the short-time Fourier transform (STFT), wavelet analysis (WA), and the pseudo-Wigner-Ville distribution (PWVD), are investigated for condition diagnosis of rotating machinery. In the case of the bearing diagnosis, the diagnosis sensitivity of the PWVD was found to be highest. An extraction method for instantaneous feature spectrum is proposed using the relative crossing information (RCI), by which the feature spectrum from time-frequency distribution can be automatically extracted by a computer in order to identify among the conditions of a machine. The symptom parameters are also defined in the frequency domain using the feature spectrum extracted by the RCI. The synthetic symptom parameters can be obtained by the least squares mapping (LSM) technique to increase the diagnosis sensitivity of the symptom parameters. Based on the above studies, a fuzzy diagnosis method using sequential inference and possibility theory was also proposed, by which the conditions of machinery can be well identified sequentially. Practical examples of diagnosis for a roller bearing are given in order to verify the effectiveness of the approaches proposed in this paper.