A new approach: ordinal predictive maintenance with ensemble binary decomposition (OPMEB)
Turkish Journal of Electrical Engineering and Computer Sciences, ISSN: 1303-6203, Vol: 32, Issue: 4, Page: 534-554
2024
- 532Usage
- 23Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage532
- Downloads321
- Abstract Views211
- Captures23
- Readers23
- 23
Article Description
Predictive maintenance (PdM), a fundamental element of modern industrial systems, employs machine learning to monitor equipment conditions, estimate failure probabilities, and optimize maintenance schedules. Its core objective is to enhance equipment reliability, extend lifespan, and minimize costs through data-driven insights by enabling efficient maintenance scheduling, reducing downtime, and optimizing resource allocation. In this paper, we propose a novel ordinal predictive maintenance with ensemble binary decomposition (OPMEB) method for the PdM domain, considering the hierarchical nature of class labels reflecting the machine’s health status, including categories like healthy, low risk, moderate risk, and high risk. The proposed OPMEB method was validated by executing on the C-MAPSS, AI4I 2020, and a real-world hydraulic system’s condition datasets. The experimental outcomes were evaluated with four distinct metrics: accuracy, recall, precision, and F-measure. The findings showed the improvement in the model’s predictive capabilities achieved by the proposed approach when compared to the traditional ordinal classification algorithm. Furthermore, the experimental results demonstrated the superior performance of the OPMEB method over other ordinal binary decomposition methods, including OneVsAll, OneVsFollowers, and OneVsNext.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85200232308&origin=inward; http://dx.doi.org/10.55730/1300-0632.4086; https://journals.tubitak.gov.tr/elektrik/vol32/iss4/4; https://journals.tubitak.gov.tr/cgi/viewcontent.cgi?article=4086&context=elektrik; https://dx.doi.org/10.55730/1300-0632.4086; https://journals.tubitak.gov.tr/elektrik/vol32/iss4/4/
The Scientific and Technological Research Council of Turkey (TUBITAK-ULAKBIM) - DIGITAL COMMONS JOURNALS
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