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Mixed domain feature bearing fault diagnosis method based on Pearson's correlation coefficient

A Pearson correlation and fault diagnosis technology, which is applied in the testing of mechanical components, the identification of patterns in signals, and the testing of machine/structural components. , to achieve the effect of improving diagnostic accuracy, clear and accurate expression, and improved diagnostic accuracy.

Pending Publication Date: 2021-09-21
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

[0004] The technical problem to be solved in the present invention is: to propose a mixed domain characteristic bearing fault diagnosis method based on Pearson correlation coefficient, to solve: (1) difficulty in feature extraction and feature set construction; (2) excessively high dimension of nonlinear feature set, etc. question

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  • Mixed domain feature bearing fault diagnosis method based on Pearson's correlation coefficient
  • Mixed domain feature bearing fault diagnosis method based on Pearson's correlation coefficient
  • Mixed domain feature bearing fault diagnosis method based on Pearson's correlation coefficient

Examples

Experimental program
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Effect test

Embodiment 1

[0120] Embodiment 1: as Figure 1-10 As shown, a mixed-domain characteristic bearing fault diagnosis method based on Pearson correlation coefficient includes the following steps: First, extract 6 time-domain dimensionless vectors, 10 time-domain dimensioned vectors, and 4 frequency-domain vectors from the original signal The eigenvectors, 6 wavelet transform eigenvectors and 10 adaptive noise CEEMDAN eigenvectors of complete integrated empirical mode decomposition, combined with the extracted feature parameters, construct the bearing fault mixed domain feature set; secondly, use the Pearson correlation coefficient to extract Correlation analysis is performed on the mixed domain features of the high-dimensional fault feature set, and the easy-to-recognize low-dimensional main feature vector is extracted from the high-dimensional fault feature set; finally, the low-dimensional feature set is imported into the random forest as the input of pattern recognition.

[0121] Further, 6...

Embodiment 2

[0212] Embodiment 2: In this example, the method as shown in Embodiment 1 is adopted to carry out the fault diagnosis of the bearing, and the specific implementation steps are as follows:

[0213] (1) This experiment uses the bearing fault data set collected by the Bearing Data Center of Case Western Reserve University in the United States. The data set is the fan end bearing data at a sampling frequency of 12K. The fault data of 4 different states of normal, inner ring fault, rolling element fault and outer ring fault (6 o'clock direction) are collected respectively. In addition to the normal data, each state has 3 A total of 10 fault types are used as the data source for this experiment. Each class of data is divided into 115 classification samples, and there are 1150 samples in total for 10 classes. The training set size is 700, that is, 70 for each class, and the test set is 450, 45 for each class. The classification is shown in Table 1, where RF, IF, and OF are faults o...

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Abstract

The invention relates to a mixed domain feature bearing fault diagnosis method based on a Pearson's correlation coefficient, and belongs to the technical field of mechanical engineering automation. The method comprises the following steps: firstly, extracting six time domain dimensionless vectors, ten time domain dimensionless vectors, four frequency domain feature vectors, six wavelet transform feature vectors and ten self-adaptive noise complete ensemble empirical mode decomposition (CEEMDAN) feature vectors from an original signal, and constructing a bearing fault mixed domain feature set in combination with extracted feature parameters; secondly, extracting low-dimensional feature vectors easy to identify from the high-dimensional fault feature set by using a Pearson's correlation coefficient; and finally, importing the low-dimensional feature set into a random forest for classification and identification. Experimental results show that the classification accuracy of the mixed domain bearing fault diagnosis method based on the Pearson's correlation coefficient can reach 97.32%, and compared with other methods, the mixed domain bearing fault diagnosis method based on the Pearson's correlation coefficient has obvious advantages.

Description

technical field [0001] The invention relates to a mixed domain characteristic bearing fault diagnosis method based on Pearson correlation coefficient, which belongs to the technical field of mechanical engineering automation. Background technique [0002] As an important part of the mechanical system, rotating machinery is widely used in fields such as electric power, metallurgy, chemical industry, and machinery manufacturing. Its health status not only affects the safe and stable operation of the equipment itself, but also directly affects the later production. What's more, equipment failure may cause local damage, huge economic loss and even casualties. Numerous studies have shown that bearing failures account for 30% of rotating machinery failures. Therefore, in-depth research on condition monitoring and fault diagnosis of bearings is of great significance for maintaining equipment safety and reducing maintenance costs. In recent years, with the rapid development of si...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G01M13/045
CPCG01M13/045G06F2218/06G06F2218/10
Inventor 李亚王玉承
Owner KUNMING UNIV OF SCI & TECH
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