Data science for vibration heteroscedasticity and predictive maintenance of rotary bearings

Chia Yen Lee, Ting Syun Huang, Meng Kun Liu, Chen Yang Lan

研究成果: Article

3 引文 (Scopus)

摘要

Electric motors are widely used in our society in applications like cars, household appliances, industrial equipment, etc. Costly failures can be avoided by establishing predictive maintenance (PdM) policies or mechanisms for the repair or replacement of the components in electric motors. One of key components in the motors are bearings, and it is critical to measure the key features of bearings to support maintenance decision. This paper proposes a data science approach with embedded statistical data mining and a machine learning algorithm to predict the remaining useful life (RUL) of the bearings in a motor. The vibration signals of the bearings are collected from the experimental platform, and fault detection devices are developed to extract the important features of bearings in time domain and frequency domain. Regression-based models are developed to predict the RUL, and weighted least squares regression (WLS) and feasible generalized least squares regression (FGLS) are used to address the heteroscedasticity problem in the vibration dataset. Support vector regression (SVR) is also applied for prediction benchmarking. Case studies show that the proposed data science approach handled large datasets with ease and predicted the RUL of the bearings with accuracy. The features extracted from time domain are more significant than those extracted from frequency domain, and they benefit engineering knowledge. According to the RUL results, the PdM policy is developed for component replacement at the right moment to avoid the catastrophic equipment failure.

原文English
文章編號801
期刊Energies
12
發行號5
DOIs
出版狀態Published - 2019 二月 28

指紋

Bearings (structural)
Heteroscedasticity
Maintenance
Vibration
Electric Motors
Maintenance Policy
Least Squares Regression
Electric motors
Replacement
Frequency Domain
Time Domain
Predict
Knowledge Engineering
Knowledge engineering
Domestic appliances
Generalized Least Squares
Vibration Signal
Support Vector Regression
Weighted Least Squares
Benchmarking

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

引用此文

Lee, Chia Yen ; Huang, Ting Syun ; Liu, Meng Kun ; Lan, Chen Yang. / Data science for vibration heteroscedasticity and predictive maintenance of rotary bearings. 於: Energies. 2019 ; 卷 12, 編號 5.
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Data science for vibration heteroscedasticity and predictive maintenance of rotary bearings. / Lee, Chia Yen; Huang, Ting Syun; Liu, Meng Kun; Lan, Chen Yang.

於: Energies, 卷 12, 編號 5, 801, 28.02.2019.

研究成果: Article

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