Weak signal enhancement detection method based on complementary stochastic resonance filter

A stochastic resonance and detection method technology, which is applied in the testing of machines/structural components, mechanical components, mechanical bearings, etc., can solve the problems affecting the accuracy of fault diagnosis and unsatisfactory filtering effect, so as to improve and enhance the detection effect , to achieve the effect of adaptive enhancement and accuracy improvement

Active Publication Date: 2017-03-22
ANHUI UNIVERSITY
View PDF6 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Traditional stochastic resonance filters are based on the classic one-dimensional stochastic resonance model, which is a single-input and single-output system. When the signal-to-noise ratio of the input signal is low, the filtering effect is not ideal, and the bearing cannot be well realized. The detection of weak fault characteristic signals affects the accuracy of fault diagnosis, so it is not suitable for the detection of bearing weak signal enhancement under strong noise background
[0004] From the above analysis, it can be seen that for the existing weak signal enhancement detection technology based on stochastic resonance filter, the one-dimensional stochastic resonance filter has limitations

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Weak signal enhancement detection method based on complementary stochastic resonance filter
  • Weak signal enhancement detection method based on complementary stochastic resonance filter
  • Weak signal enhancement detection method based on complementary stochastic resonance filter

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0035] In order to verify the weak signal enhanced detection performance of the complementary stochastic resonance filter proposed by the present invention, at first the noise-containing sinusoidal signal is analyzed, and the parameters of the noise-containing sinusoidal signal are set to: A 1 =A 2 =1,f 0 =100Hz, Its waveform and spectrum are as figure 2 shown. It can be seen from the time domain waveform that the sinusoidal signal is completely submerged by noise. It can be seen from the frequency spectrum that the driving frequency f 0 , but the noise interference is very obvious. Bundle figure 2 The noisy signal of is split in half into two sub-signals S 1 (t) and S 2 (t) and input to the two input ends of the complementary stochastic resonance filter, in the optimal parameter δ 1 =-1.0e6,δ 2 =-1.0e6,S R1 =1.0e4,S R2 =1.0e6, T R1 =1.0e-1, T R2 =1.1e-1, the optimal output of the complementary stochastic resonance filter is as follows image 3 shown. It can...

Embodiment 2

[0037] In order to further verify the superiority of the present invention in weak signal enhanced detection, the inner ring signal of the bearing fault is analyzed. The model of the inner ring fault bearing is shown in Table 1:

[0038] Table 1 Inner ring fault bearing parameters and fault information

[0039]

[0040] When the inner ring of the bearing has a partial fault, the moving rollers will pass through the faulty part of the inner ring in turn and collide to generate shock vibration, which further causes machine resonance. Therefore, the bearing fault signal is composed of a series of attenuated shock signals modulated by resonance frequency. On the other hand, at a certain speed, the time interval between two adjacent impact signals (the reciprocal of which is the fault characteristic frequency) can reflect different fault types of bearings. Since the fault signal is an amplitude modulated signal, the resonance demodulation technique is often used to demodulate t...

Embodiment 3

[0042] In order to further verify the superiority and effectiveness of the method proposed by the present invention, the bearing outer ring fault signal is analyzed, and the bearing model is shown in Table 2:

[0043] Table 2 Outer ring fault bearing parameters and fault information

[0044]

[0045] Analysis results such as Figure 6 with Figure 7 shown, from Figure 6 It can be seen from the figure that the fault characteristic frequency f of the outer ring of the bearing is o Ambient noise is very noticeable, interfering with accurate diagnosis of bearing faults. The resulting output of the complementary stochastic resonance filter is as follows Figure 7 As shown, the optimal parameter is: δ 1 =-7.9e4,δ 2 =1.0e9,S R1 =4.0e4,S R2 =1.0e7,T R1 =1.0, T R2 = 1.0. The energy of high frequency and low frequency noise in the frequency spectrum is reduced, and the fault characteristic frequency f o is effectively enhanced, so that it can be clearly judged that there...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a weak signal enhancement detection method based on a complementary stochastic resonance filter, which comprises the steps of (1) dividing a signal with the length being 2N points in a half-and-half manner into two sub-signals S1(t), S2(t), wherein the signal is acquired by preprocessing, and the length of each sub-signal is N points; (2) building a complementary stochastic resonance filter; (3) calculating a value of the weighted spectral kurtosis of a main channel output signal x(t); and (4) searching a maximum value in a six-dimensional weighted spectral kurtosis matrix, wherein the output signal x(t) corresponding to the maximum value is an optimal filtering output signal of the complementary stochastic resonance filter, and performing spectral analysis on the signal, thereby being capable of judging the fault type of a bearing according to fault characteristic frequency of the bearing. According to the invention, the more advanced double-channel complementary stochastic resonance filter is adopted, appropriate system parameters are adjusted in an adaptive manner through a weighted spectral kurtosis indicator, and weak periodic signals of a main channel are enhanced by using phase difference noises of complementary channels, so that enhancement for the weak fault characteristic frequency of the bearing is realized, and the accuracy of fault diagnosis of the bearing is improved.

Description

technical field [0001] The invention relates to the technical field of weak signal enhancement detection, in particular to a weak signal enhancement detection method based on a complementary stochastic resonance filter. Background technique [0002] Bearing is an important core component of rotating machinery. Bearing condition monitoring and fault diagnosis are of great significance to ensure the safe and reliable operation of rotating machinery. Vibration signal analysis methods are often used in bearing fault diagnosis. Firstly, a sensor is installed on the bearing housing to collect vibration signals. By analyzing the signals, its time domain, frequency domain, and time-frequency domain characteristics can be obtained. Further analysis of these characteristics can obtain the operating status of the bearing. However, in actual working conditions, the collected vibration signal is easily disturbed by environmental background noise, machine operation noise, and electrical ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/04
CPCG01M13/045
Inventor 陆思良苏云升赵吉文王骁贤刘方刘永斌
Owner ANHUI UNIVERSITY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap