Unsteady-state signal detection method based on improved self-adaptive morphological filtering

Abstract

Disclosed is an unsteady-state signal detection method based on improved self-adaptive morphological filtering. The method is used for feature extraction of fault signals of various kinds of rotating mechanical equipment, and includes drawing a time domain chart of original signals, searching and marking all local maximums of the signals from the original signals, and calculating a contour area defined by the local maximums and a time axis in the time domain chart to serve as a reference area; performing morphological filtering on the original signals through structure elements with different lengths and a close-open morphological operator, calculating a contour area defined by obtained local extremums of the signals and the time axis after the morphological filtering of the different structure elements, and calculating a difference between the area and the reference area to determine the length of the structure element corresponding to the a minimum difference; and using the obtained structure element for the morphological filtering of the signals, and determining faults according to frequency spectrums of the signals after analysis processing. The unsteady-state signal detection method based on the improved self-adaptive morphological filtering improves efficiency and accuracy of fault determination of rotating equipment.

Description

technical field [0001] The invention relates to a method for analyzing and detecting unsteady signals, in particular to a method for detecting unsteady signals through adaptive shape filtering, which is used for detecting unsteady signals reflecting fault characteristics in vibration signals of rotating machinery. Background technique [0002] The detection of unsteady components in the signal reflecting the failure of mechanical equipment has a wide range of applications in the fields of fault diagnosis of mechanical equipment and detection of biomedical signals. The common non-stationary component detection method is to observe whether there is a non-stationary component in the time-domain signal, but since the noise will inevitably be mixed in the detection process, the non-stationary component that represents the fault will also be polluted by noise. Direct observation The accuracy and efficiency of the method are very low. Another commonly used method is to analyze the...

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Problems solved by technology

The common non-stationary component detection method is to observe whether there is a non-stationary component in the time-domain signal, but since the noise will inevitably be mixed in the detection process, the non-stationary component that represents the fault will also be polluted by noise. Direct observation The accuracy and efficiency of the method are very low

Another commonly used method is to analyze the periodic characteristics of the signal in the frequency domain, but for the short-duration unsteady signal in the signal, it shows a small amplitude in the frequency spectrum, and even is also submerged by noise, so through Frequency domain analysis and detection often cannot get significant features

In addition, filtering and analyzing the signal is also an effective method to remove the noise interference in the signal and extract the unsteady components, but its effect is greatly affected by the key parameters of the designed filter, such as cut-off frequency, bandwidth, center frequency and other factors. Big

With the deepening of research, in practice, the commonly used detection method is to decompose the wavelet cladding layer of the signal, then demodulate the wavelet node signal, analyze the frequency domain characteristics, and then obtain the period of the non-stationary component in the signal, but these The technology has the disadvantage of requiring prior knowledge or taking a long time

Therefore, generally speaking, the existing detection methods have the disadvantages of low efficiency and low accuracy in judging rotating equipment faults

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