Reliability improvement in the presence of weak fault features using non-Gaussian IMF selection and AdaBoost technique
- Authors
- Shifat, Tanvir Alam; Hur, Jang Wook
- Issue Date
- Aug-2021
- Publisher
- KOREAN SOC MECHANICAL ENGINEERS
- Keywords
- AdaBoost; BLDC motor; Fault classification; IMF; VMD
- Citation
- JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, v.35, no.8, pp.3355 - 3367
- Journal Title
- JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
- Volume
- 35
- Number
- 8
- Start Page
- 3355
- End Page
- 3367
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/20414
- DOI
- 10.1007/s12206-021-0709-7
- ISSN
- 1738-494X
- Abstract
- In machinery fault detection and identification (FDI), decomposing vibration signals into corresponding intrinsic mode functions (IMFs) reduces the intricacy in extracting weak fault features at the early failure state. However, selecting a suitable IMF for fault information extraction is a challenging task. Analyzing the non-Gaussian IMFs allows extracting effective fault-related information rather than the entire signal or other IMFs because the vibration signals are random in nature. In this study, we present an IMF selection method based on the maximum kurtosis value of each IMF. A kurtosis computation method named autogram is used. It considers the autocovariance function to characterize the 2nd order cyclostationary. We deploy the AdaBoost algorithm with a decision tree classifier to gain a better performance compared with other tree-based classifiers. The proposed FDI framework can effectively detect and classify multiple fault features at the incipient failure stage.
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Collections - School of Mechanical System Engineering > 1. Journal Articles
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