A novel fault classification feature extraction method for rolling bearing based on multi-sensor fusion technology and EB-1D-TP encoding algorithm

To improve the accuracy of bearing fault diagnosis in a multisensor monitoring environment, it is necessary to obtain more accurate and effective fault classification features for bearings. Accordingly, a bearing fault classification feature extraction method based on multisensor fusion technology a...

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Bibliographic Details
Published in:ISA transactions Vol. 142; pp. 427 - 444
Main Authors: Pan, Zuozhou, Zhang, Zhengyuan, Meng, Zong, Wang, Yuebing
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.11.2023
Subjects:
EB-1D-TP encoding algorithm
Feature extraction
Multi-sensor fusion
Rolling bearing
Feature extraction
Multi-sensor fusion
EB-1D-TP encoding algorithm
Rolling bearing
ISSN:0019-0578, 1879-2022, 1879-2022
Online Access:Get full text
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Summary:To improve the accuracy of bearing fault diagnosis in a multisensor monitoring environment, it is necessary to obtain more accurate and effective fault classification features for bearings. Accordingly, a bearing fault classification feature extraction method based on multisensor fusion technology and an enhanced binary one-dimensional ternary pattern (EB-1D-TP) algorithm were proposed in this study. First, an optimal equalization weighting algorithm was established to realize high-precision fusion of bearing signals by introducing an optimal equalization factor and determining the theoretical optimal equalization factor value. Second, an enhanced binary encoding method similar to balanced ternary encoding was developed, which increases the difference between the different fault features of the bearing. Finally, the new sequence obtained after encoding was used as the input to a support vector machine to classify and diagnose the faults of the rolling bearing. The experimental results show that the algorithm can significantly improve the accuracy and speed of rolling-bearing fault classification. Combining fusion-encoding features with other intelligent classification methods, the classification results were improved. •An optimized equalization random weighting algorithm is proposed.•A “Zero Removal” enhancement processing technique is proposed.•A new encoding method more suitable for one-dimensional signal is constructed.
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content type line 23
ISSN:0019-0578
1879-2022
1879-2022
DOI:10.1016/j.isatra.2023.07.015