VMD equipment fault diagnosis method and system

Abstract

The invention discloses a VMD equipment fault diagnosis method and system, and the method comprises the steps: decomposing an equipment vibration signal through employing a VMD algorithm, removing a noise component and a trend term through employing a nonlinear discrimination algorithm, reserving a fractal signal component, carrying out the interpolation through employing a Newton interpolation function extreme point, fitting an envelope through employing a least square method, and separating a frequency modulation part, using a DQ algorithm for estimating instantaneous frequency and calculating a corresponding instantaneous scale, determining a vibration signal de-trending result according to an analysis scale, calculating a multi-fractal spectrum of a de-trending signal, extracting coordinates of a left end point, a right end point and an extreme point of the multi-fractal spectrum as characteristic parameters of an equipment operation state, and identifying the operation state of the equipment. The algorithm is deployed to an equipment state monitoring system, the equipment operation state can be accurately distinguished, and the equipment state monitoring system has good flexibility and portability and is convenient for engineering application.

Description

technical field [0001] The invention relates to the field of equipment state monitoring and fault diagnosis, in particular to a VMD equipment fault diagnosis method and system. Background technique [0002] Equipment vibration signals contain rich fractal features, which can describe the operating state of equipment. Box dimension, power spectrum analysis and rescaled range method can estimate single fractal parameters of stationary signals, and detrended fluctuation analysis (DFA) can estimate single fractal dimensions of non-stationary signals. However, when the equipment fails, its vibration signal is usually non-stationary and has multi-fractal characteristics. At this time, the traditional fractal dimension estimation method will produce relatively large errors. Multifractal detrended fluctuation analysis (MFDFA) can estimate the multifractal parameters of non-stationary signals, but the MFDFA method has the problems of manual determination of the analysis scale, diffi...

AI Technical Summary

Problems solved by technology

Using the method proposed in the present invention to analyze the equipment vibration signal can effectively extract the multi-fractal characteristics of the equipment vibration signal, and overcome the analysis scale existing in the MFDFA method that needs to be manually determined, the order of the fitting polynomial trend is difficult to determine, and the gap between data segments Continuous problem, solve the original signal fractal structure damage and negative frequency phenomenon existing in MFDFAemd method, has the advantages of high accuracy and precision of analysis results, high accuracy of equipment operation status identification results, etc.

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