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Cited by 72Machine Fault Diagnosis TechniquesReliability and Maintenance OptimizationFault Detection and Control Systems

The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.

Time Series Prediction Algorithm for Intelligent Predictive Maintenance

Lin, Chin-Yi, Hsieh, Yu-Ming, Cheng, Fan-Tien, Huang, Hsien-Cheng, Adnan, Muhammad·2019·IEEE Robotics and Automation Letters
DOI 10.1109/lra.2019.2918684
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Abstract

Predictive maintenance aims to find out when the target device (TD) is in the sick state and almost entering the dead state before its actual occurrence to conduct just-in-time maintenance, so as to avoid unexpected TD down time. In this way, not only tool availability and manufacturing quality are improved, but the additional cost of excessive maintenance in preventive maintenance strategy can also be reduced. Among the predictive maintenance technologies proposed by many scholars, exponential model was commonly applied to predict the remaining useful life (RUL) of TD. However, due to the algorithm limitations, when TD is about to die, whether the TD's aging feature suddenly rises or becomes smooth, the exponential model may not be able to keep up with the real-time prediction or even falsely predicts long RUL. To solve the problem of inaccurate RUL prediction, the authors propose the time series prediction (TSP) algorithm. TSP applies the time series analysis model built by information criterion to adapt to the complicated future trend of solving TD fault prediction. Also, the Pre-Alarm Module (PreAM) to make alert of immediate maintenance when a TD is likely to shut down shortly as well as the Death Correlation Index (DCI) to reveal the possibility of entering the dead state are proposed in this work. How to select the most effective predictors and adjust the predictor weights to construct high-performance prediction model are also illustrated in this letter with the tools in various industries (such as solar-cell manufacturing and machine tool industry) being the examples of the TSP algorithm.

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Metadata

Authors
Lin, C.-Y., Hsieh, Y.-M., Cheng, F.-T., Huang, H.-C., & Adnan, M.
Affiliations
National Cheng Kung University (TW)
ORCID
Venue
IEEE Robotics and Automation Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2019-07
Volume
4
Issue
3
Pages
2807-2814
ISSN
  • 2377-3766 (ISSN-L)
  • 2377-3774
DOI
10.1109/lra.2019.2918684
OpenAlex
W2944921687
Cited by
72
References
24
Citations by year
2026: 10 · 2025: 12 · 2024: 6 · 2023: 12 · 2022: 13 · 2021: 13 · 2020: 5 · 2019: 1
FWCI
4.43
Type
journal article
Language
en
Open access
closed
Licence
Locations
IEEE Robotics and Automation Letters (publishedVersion)
Funders
  • Intelligent Manufacturing Research Center
  • Ministry of Education (MOE) in Taiwan
  • Ministry of Science and Technology of Taiwan, R.O.C. (107-2218-E-006-055, 107-2622-8-006-015, 107-2622-E-006-002-CC2, 105-2221-E-006-255-MY3)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Reliability and Maintenance Optimization
  • Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
Keywords
predictive maintenance, time series forecasting, remaining useful life prediction, fault prediction, information criteria, predictor selection, solar cell fabrication, machine tools, machine downtime

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