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Reliability improvement in the presence of weak fault features using non-Gaussian IMF selection and AdaBoost technique

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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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Acknowledgments

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2020-2020-0-01612) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).

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Authors and Affiliations

  1. Department of Mechanical Engineering (Department of Aeronautics, Mechanical, and Electronic Convergence Engineering), Kumoh National Institute of Technology, Gumi, 39177, Korea

    Tanvir Alam Shifat & Jang Wook Hur

Authors
  1. Tanvir Alam Shifat
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  2. Jang Wook Hur
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Correspondence to Jang Wook Hur.

Additional information

Tanvir Alam Shifat received his M.S. degree in Mechanical Engineering from the Kumoh National Institute of Technology in August 2020. He received his B.Sc. degree in Electrical and Electronic Engineering from East West University, Bangladesh, in 2016. He is currently working as a Full-time Graduate Research Assistant at the Defense Reliability Laboratory. He is primarily focused on advanced signal processing and ML techniques for the condition monitoring of electric motors. His research interests include reliability, maintainability, and condition monitoring of rotating machinery and electric machines.

Jang-Wook Hur is currently serving as a Professor at the Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology. He received his Ph.D. degree in Mechanical Engineering from Tokyo Institute of Technology, Japan, in 1995. He served in Korean Army and ranked colonel in 2011. Professor Hur is the Director of Defense Reliability Lab. at Kumoh National Institute of Technology. His research interests include reliability, maintainability, and condition monitoring of various defense equipment.

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Shifat, T.A., Hur, J.W. Reliability improvement in the presence of weak fault features using non-Gaussian IMF selection and AdaBoost technique. J Mech Sci Technol 35, 3355–3367 (2021). https://doi.org/10.1007/s12206-021-0709-7

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  • DOI: https://doi.org/10.1007/s12206-021-0709-7

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