Research on the Applications of Local Wave Analysis in Machine Fault Diagnosis

摘要

A new method for time-frequency analysis designated as Local Wave Analysis (LWA) is introduced. This method is especially well suited for analyzing time-series data that represent non-stationary and nonlinear processes. The method stands in contrast to classical methods, including Fourier analysis that are generally applicable only to periodic or stationary data that represent linear processes. This method is based principally on the concept of local wave decomposition (LWD), according to which any complicated set of data can be decomposed into a finite and often small number of "intrinsic mode functions" (IMFs) that admit well-behaved Hilbert transforms. This decomposition method is adaptive and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and non-stationary processes. With the Hilbert transform, the intrinsic mode functions yield instantaneous frequencies as functions of time that give sharp identifications of imbedded structures. The final presentation of the results is an energy-frequency-time distribution, designated as the Hilbert Spectrum. This method is superior to the methods of time-frequency analysis available. Through the application research on the vibration signals, which are collected from the shaft of the gear box of a petroleum pump machine, we find new ways to solve machine fault diagnosis problem. With the method we can get the time-frequency spectrum―Hilbert Spectrum of the vibration signals and the Marginal Spectrum of Hilbert Spectrum, which can accurately detect the fault of gear, and based on the Hilbert Spectrum, we can definite the Degree of Stationarity of the vibration signal, through which we can find some useful information about gear working. Practical application proves that the utilization of nonlinear and nos-stationary character of machinery vibration signal is better than the ignorance of it, and thus we can extract more fault characteristic, and obtain more accurate diagnostic results. Finally, the related problems that need further study in this field are pointed out, too.