Rolling bearing fault diagnosis method based on PCA (principal component analysis) and ELM (extreme learning machine)

A fault diagnosis, rolling bearing technology, applied in the testing of mechanical components, testing of machine/structural components, measuring devices, etc., can solve problems such as classifier performance degradation, multi-dimensional feature redundancy, etc.

Inactive Publication Date: 2019-08-20
KUNMING UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the multi-dimensional features used in the above methods are prone to redundancy, resulting in a decrease in the performance of the classifier.

Method used

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  • Rolling bearing fault diagnosis method based on PCA (principal component analysis) and ELM (extreme learning machine)
  • Rolling bearing fault diagnosis method based on PCA (principal component analysis) and ELM (extreme learning machine)
  • Rolling bearing fault diagnosis method based on PCA (principal component analysis) and ELM (extreme learning machine)

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0074] Example 1: Intrinsic Time Scale Decomposition (ITD), as a new adaptive decomposition method, is not only superior to EMD algorithm in suppressing end effects, but also superior to EMD algorithm in terms of decomposition speed, and is used in bearing fault diagnosis Has a good effect.

[0075] Principal component analysis (PCA) is a typical dimensionality reduction method, which can effectively reduce the dimensionality of high-dimensional data and eliminate the redundancy between features, thereby ensuring the performance of the classifier. Due to the non-linear relationship between bearing characteristics and its operating state, support vector machine (SVM) and extreme learning machine (ELM) machine learning methods are usually used to identify the bearing operating state, and extreme learning machine (ELM) is used as a new pattern recognition method It can effectively overcome the problem of support vector machine (SVM) requiring a large number of iterative algorithms. ...

Embodiment 2

[0132] Example 2: Such as Figure 2-5 As shown, the present invention verifies that the method can accurately classify and identify bearing faults through the following engineering experiments, and the specific steps are as follows:

[0133] Step 1. The experimental data of the present invention adopts bearing data from Case Western Reserve University in the United States, and the sampling frequency is 48 kHz. There are two types of bearing status: normal status and fault status. Among them, the fault status selects three fault statuses: fault size 0.007 mils and fault size 0.014 mils: rolling element failure, inner ring failure, and outer ring failure, a total of seven situations. There are 50 samples in each state, 30 samples are used as the training set, and 20 are used as the test set, a total of 350 samples. The experimental conditions and sample numbers are shown in Table 1. The number of sampling points for each sample is 2048 sampling points.

[0134] Table 1 Experiment...

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Abstract

The invention relates to a rolling bearing fault diagnosis method based on the PCA (principal component analysis) and ELM (extreme learning machine) and belongs to the technical field of fault diagnosis and signal processing and analysis. According to the method, intrinsic time-scale decomposition (ITD) is utilized to decompose vibration signals, and inherent rotation (Pr) components are screened,and the entropy values and time-domain features of all selected PR components are calculated; principal component analysis (PCA) is adopted to perform dimensionality reduction processing on the obtained features, and the dimensionality-reduced features are adopted to build an ELM (extreme learning machine) fault diagnosis model; and therefore, the recognition of the state of a rolling bearing isrealized. With the method of the invention adopted, the problem that the single feature of a rolling bearing is unlikely to accurately reflect the state of the bearing and the problem of the decreaseof the performance of a pattern recognition algorithm due to dimensionality increase with many features containing much bearing state information can be solved. A bearing experiment shows that the method can effectively recognize bearing states, and is simple in principle and high in practicability.

Description

Technical field [0001] The invention relates to a method for diagnosing rolling bearing faults based on PCA and ELM, and belongs to the field of mechanical fault diagnosis and signal processing. [0002] technical background [0003] With the development of industrialization, more and more industries adopt large-scale automation equipment for production. Rolling bearings are one of the commonly used parts of large-scale automation machinery and equipment. If their delivery fails, it will cause incalculable economic losses to normal production and life. It even threatens personal safety. Therefore, it is particularly important to diagnose the faults of rolling bearings. [0004] The vibration signal of a rolling bearing is a typical non-stationary signal, usually using wavelet decomposition and empirical mode decomposition signal processing methods. However, the artificially selected wavelet basis has a great influence on the effect of wavelet decomposition, so the wavelet decomposi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/045
CPCG01M13/045
Inventor 吴建德徐存知王晓东黄国勇范玉刚
Owner KUNMING UNIV OF SCI & TECH
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