Vibration signal for bearing fault detection using random forest

Tarek, Abedin and Koh, Siaw Paw and Yaw, Chong Tak and Phing, Chen Chai and Tiong, Sieh Kiong and Tan, Jian Ding and Kharudin, Ali and Kadirgama, Kumaran and Benedict, Foo (2023) Vibration signal for bearing fault detection using random forest. In: Journal of Physics: Conference Series; International Conference on Emerging Electronic and Automation Technology 2022, ICEEAT 2022 , 10-11 December 2022 , Virtual, Online. pp. 1-21., 2467 (012017). ISSN 1742-6588

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Based on the chosen properties of an induction motor, a random forest (RF) classifier, a machine learning technique, is examined in this study for bearing failure detection. A time-varying actual dataset with four distinct bearing states was used to evaluate the suggested methodology. The primary objective of this research is to evaluate the bearing defect detection accuracy of the RF classifier. First, run four loops that cycle over each feature of the data frame corresponding to the daytime index to determine the bearing states. There were 465 repetitions of the inner race fault and the roller element fault in test 1, 218 repetitions of the outer race fault in test 2, and 6324 repetitions of the outer race in test 3. Secondly, the task is to find the data for the typical bearing data procedure to differentiate between normal and erroneous data. Out of 3 tests, (22-23) % normal data was obtained since every bearing beginning to degrade usually exhibits some form of a spike in many locations, or the bearing is not operating at its optimum speed. Thirdly, to display and comprehend the data in a 2D and 3D environment, Principal Component Analysis (PCA) is performed. Fourth, the RF algorithm classifier recognized the data frame's actual predictions, which were 99% correct for normal bearings, 97% accurate for outer races, 94% accurate for inner races, and 97% accurate for roller element faults. It is thus concluded that the proposed algorithm is capable to identify the bearing faults.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Bearing; Fault detection; Principal component analysis; Random forest
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TJ Mechanical engineering and machinery
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Faculty/Division: College of Engineering
Faculty of Mechanical and Automotive Engineering Technology
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 12 Sep 2023 01:18
Last Modified: 12 Sep 2023 01:18
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