SEQUENTIAL DIAGNOSIS METHOD FOR ROTATING MACHINERY IN UNSTEADY OPERATING CONDITION BY TIME-FREQUENCY ANALYSIS AND POSSIBILITY THEORY
In this paper, the diagnosis sensitivities of time-frequency analysis methods, such as the short-time Fourier transform (STFT), the wavelet analysis (WA) and the Wigner-Ville distribution (WVD) are investigated for the condition diagnosis of machinery in unsteady operation condition. In order to diagnose failures and evaluate the diagnosis sensitivity, the extraction method of the feature spectra by the relative crossing information (RCI), and the sequential diagnosis algorithm are proposed. The symptom parameters (SPs) are also defined in frequency domain, and the least-squares mapping (LSM) method is used for synthesizing the SPs in order to increase the diagnosis sensitivity of the symptom parameters. The performance of this approach is evaluated using three time-frequency transformation techniques. Finally, the fuzzy diagnosis method by the possibility theory is also proposed for practical diagnosis of plant machinery. The efficiency of the methods proposed in this paper is verified by applying them to the fault diagnosis of a rolling bearing.
Time-frequency Analysis Sequential Diagnosis Possibility Theory Unsteady Fuzzy
Peng CHEN Huaqing WANG
Graduate School of Bioresources, Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 5148507, Japan Diagnosis and Self-Recovery Engineering Research Center, Beijing University of Chemical Technology,
国际会议
北京
英文
297-307
2008-10-27（万方平台首次上网日期，不代表论文的发表时间）