Multi-spectrum fusion decision fault diagnosis method for high-speed electric multiple-unit bearing

A multi-spectrum fusion decision fault diagnosis method for a high-speed electric multiple-unit bearing, comprising the following steps:

• step S100:
• collecting, by an acceleration sensor, vibration acceleration signals of the high-speed electric multiple-unit bearing, and performing classification according to fault types of the bearing, the fault types totally comprising four classes of an inner ring fault, an outer ring fault, a rolling body fault and a holder fault;
• step S200:
• performing analysis of four kinds of spectra of a Fourier spectrum, an envelope spectrum, a power spectrum and a fast spectral kurtosis-square envelope spectrum on the collected vibration acceleration signals respectively, to obtain a spectrum data set composed of different fault types and different spectrum analysis methods;
• step S300:
• extracting strength of a fault feature frequency corresponding to a fault position from the spectrum data set according to the fault feature frequency of a monitored bearing as fault features, simplifying the spectra in the spectrum data set based on the fault features, to obtain a fault feature data set composed of different fault types and different spectrum analysis methods, each piece of data in the fault feature data set being data based on the fault types respectively, and performing analysis of the Fourier spectrum, the envelope spectrum, the power spectrum, and the fast spectral kurtosis-square envelope spectrum on each piece of data in the fault feature data set, to obtain fault features extracted from the four kinds of spectra;
• step S400:
• constructing a fault feature function by weight fusion of four fault features from the four kinds of spectra, and converting each piece of data in the fault feature data set into a fusion data point representing a fault type and a diagnosis effect of the data;
• step S500:
• optimizing weights of the four fault features through a criterion that a fisher discrimination ratio maximizes an intra-class spacing and minimizes an inter-class spacing, to obtain an optimal weight combination of four classes of spectrum fault features in the fault feature dataset, and meanwhile obtaining a numerical value interval formed by fusion data points after fusion of the different fault types to form an experience range for judging the fault types;
• and
• step S600:
• inputting test data, performing analysis of the Fourier spectrum, the envelope spectrum, the power spectrum, and the fast spectral kurtosis-square envelope spectrum on the test data, then performing weight fusion on the test data according to the optimal weight combination to form a fusion spectrum, extracting the fault features of the four kinds of spectra and performing weight fusion to form fusion data points, judging fault types of the fusion data points according to the experience range, and finally implementing fault diagnosis of the high-speed electric multiple-unit bearing.