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applied sciences
2020년 10권 21호 p.1 ~ p.18
Prediction of Machine Inactivation Status Using Statistical Feature Extraction and Machine Learning
Prediction of Machine Inactivation Status Using Statistical Feature Extraction and Machine Learning
Taing Borith
Sadirbaev Bakhit, Aziz Nasridinov and Kwan-Hee Yoo
- Abstract -
published: 22 October 2020
Impact Factor 2.474
ISSN 2076-3417

In modern manufacturing, the detection and prediction of machine anomalies, i.e., the inactive state of the machine during operation, is an important issue. Accurate inactive state detection models for factory machines can result in increased productivity. Moreover, they can guide engineers in implementing appropriate maintenance actions, which can prevent catastrophic failures and minimize economic losses. In this paper, we present a novel two-step data-driven method for the non-active detection of industry machines. First, we propose a feature extraction approach that aims to better distinguish the pattern of the active state and non-active state of the machine by multiple statistical analyses, such as reliability, time-domain, and frequency-domain analyses. Next, we construct a method to detect the active and non-active status of an industrial machine by applying various machine learning methods. The performance evaluation with a real-world dataset from the automobile part manufacturer demonstrates the proposed method achieves high accuracy.
- Key Words -
statistical feature extraction, machine learning, machine non-active state
statistical feature extraction, machine learning, machine non-active state
- 논문 구분 -
국외전문학술지, SCI(E)
- 사사 정보 -
This research was supported by the Technology Innovation Program (2004367, development of cloud big data platform for the innovative manufacturing in ceramic industry) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) and by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2020-0-01462) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation)
- Downloads -
applsci-10-07413.pdf