ITD multi-scale fluctuation analysis state monitoring method and device

Abstract

The invention discloses an ITD multi-scale fluctuation analysis state monitoring method and device. The method comprises the steps of: carrying out decomposition on an equipment vibration signal through an ITD algorithm; removing a noise component and a trend term through a nonlinear discrimination algorithm, and reserving fractal signal components; carrying out interpolation on a local extreme point of each fractal signal component through a rational spline interpolation function; fitting an envelope by using a least square method; separating a frequency modulation part; estimating an instantaneous frequency of each fractal signal component by using a TEO algorithm, and calculating a corresponding instantaneous scale; determining a vibration signal detrending result according to an analysis scale; calculating a multi-fractal spectrum of the detrending signal; extracting singular indexes corresponding to the left end point, the right end point and the extreme point of the multi-fractal spectrum to serve as characteristic parameters of the equipment operation state so as to recognize the equipment operation state; and deploying the algorithm to the equipment state monitoring device so as accurately distinguish the equipment operation state. The equipment state monitoring system has good flexibility and portability, is high in practicability, and convenient for engineering applications.

Description

technical field [0001] The invention relates to the field of equipment state monitoring and fault diagnosis, in particular to an ITD multi-scale fluctuation analysis state monitoring method and device. 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 ...

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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