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- Title
Diagnosing Faults in Rolling Bearings of an Air Compressor Set Up Using Local Mean Decomposition and Support Vector Machine Algorithm.
- Authors
Dhakar, Atul; Singh, Bhagat; Gupta, Pankaj
- Abstract
Objective: This paper describes an air compressor fault diagnosis method based on audio signals and applied to one faulty and one healthy condition of rolling bearing considering uni-directional microphone along with a single NI 9234 Data Acquisition (DAQ) hardware unit having multiple ports, a NI 9172 USB interface, and a Lab-VIEW based Data acquisition interface. Methodology: Acquired non-stationary and non-linear bearing fault signals are processed using most recent non-traditional Local Mean Decomposition (LMD) signal processing technique. Further, six statistical indicators (Mean, Variance, RMS, RMA, AMA, and Kurtosis) have been evaluated for feature extraction. Further, four classifying techniques namely: SVM, Naïve Bayes, KNN, and Discriminant have been used for the exploration and classification of the fault features in rolling bearing of an air compressor set-up. Findings: It has been observed that LMD along with Kurtosis and SVM machine learning approach is quite accurate for processing and monitoring in situ rolling bearing fault features in air compressor set-up.
- Subjects
ROLLER bearings; AIR compressors; SUPPORT vector machines; USB technology; ALGORITHMS; FAULT diagnosis
- Publication
Journal of Vibration Engineering & Technologies, 2024, Vol 12, Issue 4, p6635
- ISSN
2523-3920
- Publication type
Article
- DOI
10.1007/s42417-024-01275-6