In:
Transactions of FAMENA, Faculty of Mechanical Engineering and Naval Architecture, Univ. of Zagreb, Vol. 46, No. 4 ( 2022), p. 31-44
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.
Type of Medium:
Online Resource
ISSN:
1333-1124
,
1849-1391
DOI:
10.21278/TOF.464036521
Language:
Unknown
Publisher:
Faculty of Mechanical Engineering and Naval Architecture, Univ. of Zagreb
Publication Date:
2022
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