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Bearing fault feature extraction method based on multi-period differential mean value and cyclic spectrum coherence

A technology of fault characteristics and extraction methods, applied in mechanical bearing testing, character and pattern recognition, instruments, etc., can solve problems such as frequency ambiguity, achieve the effect of enhancing the impact characteristics of bearing faults and enhancing the second-order cyclostationary characteristics

Pending Publication Date: 2022-07-22
KUNMING UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In order to solve this problem, the present invention provides a bearing fault feature enhancement method based on multi-period differential mean value and cyclic spectrum coherence. Technique for Instantaneous Angular Velocity IAS i The characteristic component of bearing fault in the signal is enhanced, thereby suppressing the interference of encoder installation error, estimation error and measurement noise component; secondly, for the frequency ambiguity problem caused by multi-period differential mean technology, combined with the second-order cyclostationary characteristic of rolling bearing fault component , using cyclic spectral coherence technology to eliminate frequency ambiguity caused by multi-period differential mean technology and further extract bearing fault characteristic components

Method used

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  • Bearing fault feature extraction method based on multi-period differential mean value and cyclic spectrum coherence
  • Bearing fault feature extraction method based on multi-period differential mean value and cyclic spectrum coherence
  • Bearing fault feature extraction method based on multi-period differential mean value and cyclic spectrum coherence

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Embodiment 1

[0036] Example 1: as figure 1 As shown, this embodiment is the method of the present invention for extracting the fault characteristics of the simulated bearing, and the specific process includes:

[0037] The calculation formula of the bearing outer ring fault signal model used in the simulation analysis is:

[0038]

[0039]

[0040] where w(θ) represents the average angular velocity, w o (θ) represents the encoder installation error, ξ represents the damping coefficient, f n Represents fixed 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 represents the inclination angle between the encoder rotary shaft and the rotary shaft, and the initial angle θ e ∈[φ e ,2kπ+φ e ...

Embodiment 2

[0053] 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

[0054] In this embodiment, a bearing test bench is used, such as Figure 5 As shown, an ETF100-H851007B 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 0.3mm and a depth of about 0.28mm is machined on the outer ring by wire cutting; the fault characteristic frequency f of the bearing outer ring is obtained from the following formula reb is 5.15×.

[0055]

[0056] Step 1: Obtained IAS i The fault waveform of bearing outer ring is as follows Image 6 As...

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Abstract

The invention discloses a multi-cycle differential mean and cyclic spectrum coherent bearing fault feature extraction method, which is based on the advantage that a differential technology is not interfered by amplitude and the multi-cycle accumulation characteristic, and enhances a bearing fault component in an original instantaneous angular velocity signal through a multi-cycle differential mean technology. Further, interference of components such as encoder installation errors, instantaneous angular velocity estimation errors and measurement noise is suppressed; secondly, aiming at a frequency fuzzy problem caused by a multi-period differential mean value technology, combining with a second-order cyclostationary characteristic of a rolling bearing fault component, and adopting a cyclic spectrum coherence technology to further extract the bearing fault component; and finally, bearing fault features are revealed through envelope order spectrum analysis. The multi-cycle differential mean value method provided by the invention can effectively enhance the bearing fault feature component, and further eliminate the interference of the encoder installation error component and the measurement noise component on the bearing fault feature identification.

Description

technical field [0001] The invention relates to a bearing fault feature extraction method with multi-period differential mean value and cyclic spectrum coherence, belonging to the technical field of fault diagnosis technology and signal processing analysis. Background technique [0002] Bearings are the supporting components of rotating machinery, and their health directly affects the 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) changes regularly. Therefore, bearing fault feature extraction based on IAS signal is one of the hotspots in the field of fault diagnosis. [0003] Bearings, as the supporting parts of rotating machinery, do not transmit torque, but under the action of radial loads, the contact stiffness of the rolling elements and raceways at the fault location will change regularly, and the ti...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06F17/13G06F17/18G01M13/04
CPCG06F17/13G06F17/18G01M13/04G06F2218/02G06F2218/08
Inventor 陈鑫郭瑜柳小勤樊家伟田田徐万通
Owner KUNMING UNIV OF SCI & TECH
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