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Transactions of FAMENA, Vol. 46 No. 4, 2022.

Original scientific paper

https://doi.org/10.21278/TOF.464036521

Fault Feature Extraction of Bearings for the Petrochemical Industry and Diagnosis Based on High-Value Dimensionless Features

Nai-quan Su orcid id orcid.org/0000-0002-4220-0540 ; Guangdong Provincial Key Lab of Petrochemical Equipment and Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming, China; High-Tech Institute of Xi’an, Xi’an, China
Zhi-Jie Zhou ; High-Tech Institute of Xi’an, Xi’an, China
Qing-hua Zhang ; Guangdong Provincial Key Lab of Petrochemical Equipment and Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming, China
Shao-lin Hu ; Guangdong Provincial Key Lab of Petrochemical Equipment and Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming, China
Xiao-xiao Chang ; Guangdong Provincial Key Lab of Petrochemical Equipment and Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming, China

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Abstract

The time and frequency domain features of a petrochemical unit have a variety of effects on the fault type of bearings, and the signal exhibits nonlinearity, unpredictability, and ergodicity. The detection system's important data are disrupted by noise, resulting in a huge number of invalid and partial records. To reduce the influence of these factors on feature extraction, this work presents a method for the fault feature extraction of bearings for the petrochemical industry and for diagnosis based on high-value dimensionless features. Effective data are extracted from the obtained data using a complex data preprocessing approach, and the dimensionless index is expressed. Then, based on the distribution rule of the dimensionless index, the high-value dimensionless features are retrieved. Finally, to ensure sample completeness, a high-value dimensionless feature augmented model is developed. This approach is applied to the bearing fault experiment platform of a petrochemical unit to effectively classify the bearing fault features, which benefits theoretical guidance for the feature extraction of bearings for a petrochemical unit.

Keywords

petrochemical unit; bearing; high-value dimensionless; feature extraction

Hrčak ID:

285678

URI

https://hrcak.srce.hr/285678

Publication date:

10.12.2022.

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