Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review
Copyright policy )Scalability, Explainability and Performance of Data-Driven Algorithms in Predicting the Remaining Useful Life: A Comprehensive Review
<ul> <li>This work summarizes the state-of-the-art data-driven methods for prediction of the Remaining Useful Life<br> (RUL)<br> </li> <li>It discusses challenges and open problems faced in PdM<br> </li> <li>This study presents a discussion on the new problems that need to be considered towards the Industry 4.0 goals<br> </li> <li>We propose the future direction for each challenge discussed in this article</li> </ul>
- Mississippi State University United States
- Engineer Research and Development Center United States
- Mississippi State University United States
- Mississippi Valley State University United States
predictive maintenance, Data-driven algorithms, explainability, remaining useful life, health index, Electrical engineering. Electronics. Nuclear engineering, industry 4.0, TK1-9971
predictive maintenance, Data-driven algorithms, explainability, remaining useful life, health index, Electrical engineering. Electronics. Nuclear engineering, industry 4.0, TK1-9971
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