Rolling bearing fault feature extraction method based on PCHIP-LCD

A PCHIP-LCD, rolling bearing technology, applied in the field of mechanical fault diagnosis, can solve problems such as distortion, decomposition of component waveform burrs, etc., to achieve the effect of improving accuracy, smoothing the envelope fitting curve, and improving the accuracy of envelope fitting

Inactive Publication Date: 2020-12-29
KUNMING UNIV OF SCI & TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the key to the LCD method lies in the construction of the mean curve. The mean curve fitting of the original LCD method is based on the linear transformation of the signal itself, which will cause glitches in the waveform of the decomposed components and cause distortion.

Method used

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  • Rolling bearing fault feature extraction method based on PCHIP-LCD
  • Rolling bearing fault feature extraction method based on PCHIP-LCD
  • Rolling bearing fault feature extraction method based on PCHIP-LCD

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0057] Example 1: The example uses bearing data from Case Western Reserve University, which is obtained through a bench test at a constant rotational speed. Such as figure 1 As shown, a rolling bearing fault feature extraction method based on PCHIP-LCD, the rolling bearing fault diagnosis method, the specific steps are:

[0058] Step1: Use the PCHIP-LCD method to decompose the vibration signal x(t) into several intrinsic scale components ISC 1 ,ISC 2 ,...,ISC m .

[0059] Step1.1: Collect the vibration signal r=x(t) of the rolling bearing, and the time-domain waveform diagram and frequency-domain diagram of the vibration signal r=x(t), such as image 3 shown.

[0060] Step1.2: Use the PCHIP method to construct the mean curve m 1 (t).

[0061] In the step Step1.2, the mean curve m 1 The construction steps of (t) are as follows:

[0062] Step1.2.1: Determine all extreme points of the signal x(t) (τ k ,X k ).

[0063] Step1.2.2: Divide extreme points into k intervals....

Embodiment 2

[0095] Example 2: The example uses bearing data from Case Western Reserve University, which is obtained through a bench test at a constant rotational speed. Such as figure 1 As shown, a rolling bearing fault feature extraction method based on PCHIP-LCD, the rolling bearing fault diagnosis method, the specific steps are:

[0096] Take the fault vibration signal data of the inner ring of the rolling bearing driving end for verification;

[0097] Step1: Use the PCHIP-LCD method to decompose the vibration signal x(t) into several intrinsic scale components ISC 1 ,ISC 2 ,...,ISC m .

[0098] Step1.1: Collect the vibration signal r=x(t) of the rolling bearing, and the time-domain waveform diagram and frequency-domain diagram of the vibration signal r=x(t) are as follows Figure 6 shown.

[0099] Step1.2: Use the PCHIP method to construct the mean curve m 1 (t).

[0100] In the step Step1.2, the mean curve m 1 The construction steps of (t) are as follows:

[0101]Step1.2.1:...

Embodiment 3

[0134] Embodiment 3: The platform used in Embodiment 3 is a NASA bearing life cycle experimental platform, a schematic diagram, as Figure 9 shown. In this paper, the NASA data length is 20480 points, the sampling frequency is 20kHz, the sampling point is 16384, the driving motor speed is 2000r / min, the radial load is 6000 pounds, and a total of 984 sets of data are collected.

[0135] Such as figure 1 As shown, a rolling bearing fault feature extraction method based on PCHIP-LCD, the rolling bearing fault diagnosis method, the specific steps are:

[0136] Take the fault vibration signal data of the outer ring of the rolling bearing driving end for verification;

[0137] Step1: Use the PCHIP-LCD method to decompose the vibration signal x(t) into several intrinsic scale components ISC 1 ,ISC 2 ,...,ISC m .

[0138] Step1.1: Collect the vibration signal r=x(t) of the rolling bearing, and the time-domain waveform diagram and frequency-domain diagram of the vibration signal ...

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Abstract

The invention relates to a rolling bearing fault feature extraction method based on PCHIP-LCD, and belongs to the technical field of mechanical fault diagnosis. A PCHIP-LCD method is adopted to decompose a vibration signal x (t) to obtain a plurality of intrinsic scale components (ISCs), selection and reconstruction of the ISCs components are completed by establishing an effective ISCs component screening rule based on a kurtosis and correlation coefficient (KC) combined weight index to obtain a reconstructed signal xnew (t), and Teager energy operator (TEO) demodulation analysis is performedon the reconstructed signal xnew (t) to obtain a TEO demodulation energy spectrum. And fault feature extraction of the rolling bearing is realized. The method can effectively extract the fault features of the vibration signals of the rolling bearing, and guarantees the normal operation of equipment.

Description

technical field [0001] The invention relates to a PCHIP-LCD-based rolling bearing fault feature extraction method, which belongs to the technical field of mechanical fault diagnosis. Background technique [0002] Rolling bearings have the advantages of simple structure, convenient manufacture, long life, and stable operation. They are the most widely used general-purpose mechanical parts in various rotating machines. Whether their operating status is normal often directly affects the performance of the entire machine (including its accuracy, reliability, sex and longevity, etc.). Therefore, in order to ensure the normal operation of rotating machinery equipment, it is very important to effectively extract the fault features of rolling bearings to evaluate the operating status of the bearings. [0003] The operation of rolling bearings is complex, and its vibration signal is a complex, non-stationary, and nonlinear signal. The purpose of the LCD algorithm is to decompose a c...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G01M13/045
CPCG01M13/045G06F2218/10
Inventor 王晓东杨创艳吴建德马军
Owner KUNMING UNIV OF SCI & TECH
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