Data Science for Vibration Heteroscedasticity and Predictive Maintenance of Rotary Bearings

Chia-Yen Lee, Ting-Syun Huang, Meng-Kun Liu and Chen-Yang Lan
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Chia-Yen Lee: Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan City 701, Taiwan
Ting-Syun Huang: Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan City 701, Taiwan
Meng-Kun Liu: Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan
Chen-Yang Lan: Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan

Energies, 2019, vol. 12, issue 5, 1-18

Abstract: 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.

Keywords: data mining; electric motor; remaining useful life; predictive maintenance; feasible generalized least squares regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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