LMD equipment fault diagnosis method and system

Abstract

The invention discloses an LMD equipment fault diagnosis method and system. An LMD algorithm is used to decompose equipment vibration signals, a nonlinear discrimination algorithm is used to remove a decomposition noise component and a trend term, a fractal signal component is retained, a piecewise Hermite interpolation function is used to interpolate an extreme point, least square fitting enveloping is utilized to separate a frequency modulation part, a DQ algorithm is used to estimate instantaneous frequency and calculate a corresponding instantaneous scale, a vibration signal detrending result is determined according to the analysis scale, a multi-fractal spectrum of the detrending signal is calculated, and coordinates of a left end point, a right end point and an extreme point of the multi-fractal spectrum are extracted as characteristic parameters of an equipment operation state to identify the equipment operation state. 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 method and system for fault diagnosis of LMD equipment. 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...

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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