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

A morphological filtering and signal detection technology, applied in the field of analysis and detection of non-steady-state signals, can solve the problems of noise submersion, low accuracy and efficiency, small amplitude, etc., achieve high accuracy and achieve the effect of automatic identification

Inactive Publication Date: 2012-09-19
UNIV OF SCI & TECH OF CHINA
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  • Claims
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AI Technical Summary

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

Method used

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

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Embodiment 1

[0030] The time domain waveform of the simulated signal is as figure 2 As shown in (a), the sampling frequency is 12KHz, that is, the sampling interval is 1 / 12000s. In order to verify its noise reduction performance, white noise with an amplitude of 0.5 is superimposed on the simulated signal, and the time domain waveform is as follows figure 2 (b) shown. figure 2 (c) is its spectrum, it can be seen that no effective frequency components can be seen from it.

[0031] The proposed improved morphological filtering method is used to process the signal, and the specific steps are as follows:

[0032] 1. According to step (1) of the content of the invention, find figure 2 (b) The extreme value of the time-domain signal shown, and the area enclosed by it and the time axis is calculated.

[0033] 2. According to step (2) of the content of the invention, the length of the structural element is multiplied by taking the sampling interval as the reference length, and the differen...

Embodiment 2

[0036] Actual bearing data are used for processing. The bearing model is 6205-2RS JEM SKF. The parameters are shown in Table 1.

[0037] Table 16205-2RS JEM SKF bearing parameters (unit: inch)

[0038]

[0039] The signal sampling frequency is 12KHz, and the characteristic frequency of the outer ring fault is 107.3Hz when the bearing rotates at 1797rpm. Figure 4 (a) is the fault signal of the outer ring of the bearing at a speed of 1797rpm, from which the fault pulse can be seen but the fault type cannot be qualitatively judged, Figure 4 (b) is the frequency spectrum corresponding to the signal, and the fault-related frequency components cannot be identified from the signal spectrum diagram. Utilize the morphological filter method that the present invention proposes to this signal processing, concrete steps are:

[0040] 1. According to step (1) of the content of the invention, find Figure 4 (a) The extreme value of the time-domain signal shown in (a), and the area ...

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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...

Claims

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Application Information

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IPC IPC(8): G01H17/00
Inventor 沈长青孔凡让
Owner UNIV OF SCI & TECH OF CHINA
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