SSD and GZC machine state monitoring method and device

Abstract

The invention discloses an SSD and GZC machine state monitoring method and device. An SSD algorithm is utilized to decompose an equipment vibration signal; a nonlinear discrimination algorithm is utilized to remove noise components and trend terms, and fractal signal components are reserved; a piecewise Hermite interpolation function is adopted to interpolate extreme points; a least square method is utilized to fit an envelope; a frequency modulation part is separated; a GZC algorithm is utilized to estimate an instantaneous frequency and calculate a corresponding instantaneous scale; a detrending result of the vibration signal is determined according to an analysis scale; a multi-fractal spectrum of the detrending signal is calculated; coordinates of a left end point, a right end point and an extreme point of the multi-fractal spectrum are extracted to as to serve as characteristic parameters of an equipment operation state to identify the equipment operation state; and the algorithm can be deployed into the equipment state monitoring device, so that the equipment operation state can be accurately distinguished. The equipment state monitoring device has good flexibility and portability, and is convenient for engineering application.

Description

technical field [0001] The invention relates to the field of equipment status monitoring and fault diagnosis, in particular to a method and device for monitoring the status of SSD and GZC machines. 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 ...

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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