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Bearing fault feature enhancement method of parameter adaptive decomposition structure

A fault characteristic and self-adaptive technology, applied in mechanical bearing testing, instruments, calculations, etc., can solve the problems of different interference levels of bearing fault components, improved bearing signal modulation, weak IAS changes, etc., to achieve enhanced bearing fault characteristics, low Calculate the cost, enhance the effect of adaptability

Pending Publication Date: 2022-07-15
KUNMING UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

[0003] On the one hand, due to the assembly error between the inner diameter of the encoder and the shaft, the installation error of the encoder cannot be avoided in engineering applications; the energy amplitude of the installation error of the encoder is positively correlated with the speed The overall error produced by different degrees of eccentric error and tilt error is not the same, that is, the degree of interference to the bearing fault component is different
On the other hand, the IAS changes caused by early bearings are relatively weak, and are often submerged in measurement noise and encoder installation errors, making it difficult to identify faults

Method used

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  • Bearing fault feature enhancement method of parameter adaptive decomposition structure
  • Bearing fault feature enhancement method of parameter adaptive decomposition structure
  • Bearing fault feature enhancement method of parameter adaptive decomposition structure

Examples

Experimental program
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Embodiment 1

[0056] Embodiment 1: This example describes the method of the present invention for simulating the extraction of fault characteristics of the outer ring of the instantaneous angular velocity bearing, such as image 3 As shown, the specific process includes:

[0057] The calculation formula of the bearing outer ring fault signal model used in the simulation analysis is:

[0058]

[0059]

[0060] where w(θ) represents the average angular velocity, w o (θ) represents the encoder installation error, ξ represents the damping coefficient, f n Represents the natural frequency, ψ=θ-jΘ-τ j , the angle sequence θ=2π / N, 4π / N, 6π / N..., n(θ) represents the encoder measurement noise, ρ=Δr / r is the ratio of the eccentric distance between the geometric center and the rotation center, r is the encoding The diameter of the encoder hole, Δr is the eccentric distance between the geometric center and the rotation center, β is the inclination angle between the encoder rotating shaft and t...

Embodiment 2

[0087] Embodiment 2: This embodiment describes that the method of the present invention is used to extract the fault characteristics of the actual rolling bearing outer ring

[0088] In this embodiment, a bearing test bench is used, such as Figure 5 As shown, a ReSatron optical encoder is installed on the experimental bench, the number of encoder lines is N=5000, and 10 6 The sampling rate PicoScope high-speed acquisition device obtains the corresponding angle information and time information. The bearing type of this test bench is NU206E (N b =13,E b =9.525,E p =46,α=0), in order to simulate the fault of the bearing outer ring, a groove with a width of about 0.5mm and a depth of about 0.5mm is machined on the outer ring by wire cutting; the fault characteristic frequency f of the outer ring of the bearing is obtained by the following calculation formula reb is 5.15×.

[0089]

[0090] Step 1: Obtained IAS i The fault waveform of bearing outer ring is as follows Im...

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Abstract

The invention discloses a bearing fault feature enhancement method of a parameter adaptive decomposition structure, and belongs to the technical field of fault diagnosis and signal processing and analysis. In order to solve the problem that encoder installation errors interfere with bearing fault feature identification, the invention provides a bearing fault feature enhancement method of a parameter adaptive decomposition structure, and the method comprises the following steps: firstly, adaptively dividing the filtering length of a Savitzky-Golay filter to obtain residual signals under different parameters; secondly, representing the richness of bearing fault information contained in each residual signal in combination with a diagnosis index (IIDF), and obtaining a corresponding optimized filtering length parameter when the IIDF value is maximum; a Savitzky-Golay filter based on an optimal parameter is used for eliminating encoder installation errors, and bearing fault features are revealed through corresponding envelope spectrum analysis; the PDS structure provided by the method has the advantage of obtaining high-precision parameters with low calculation cost, and by combining with a Savitzky-Golay filter, the interference of encoder installation error components on bearing fault feature identification can be effectively eliminated.

Description

technical field [0001] The invention relates to a bearing fault feature enhancement method with a parameter self-adaptive decomposition structure, belonging to the technical field of fault diagnosis technology and signal processing analysis. Background technique [0002] Bearings are the supporting parts of rotating machinery, and their health directly affects the running accuracy and life of rotating machinery. When a bearing fails, the contact stiffness between the rolling element and the raceway at the fault location changes, and the corresponding instantaneous angular velocity (IAS) will change regularly, so the IAS signal contains rich bearing fault information. Therefore, bearing condition monitoring and fault diagnosis based on IAS signal is one of the hotspots in the field of fault diagnosis. However, the suppression of the encoder installation error cannot be ignored for the feature extraction of rolling bearing faults, and the suppression of its interference compo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G01M13/04
CPCG01M13/04G06F2218/02
Inventor 陈鑫郭瑜
Owner KUNMING UNIV OF SCI & TECH
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