Data-driven predictive maintenance and time-series applications

Kefalas, M. (2023)

Doctoral Thesis

Predictive maintenance (PdM) is a maintenance policy that uses the past, current, and prognosticated health condition of an asset to predict when timely maintenance should occur. PdM overcomes challenges of more conservative policies, such as corrective or scheduled maintenance. The remaining useful life (RUL) is a critical notion in PdM that determines the time remaining until a system is no longer useful and requires maintenance. Among the approaches employed to estimate the RUL, data-driven PdM methods have shown to be a good candidate due to their (mostly) domain-agnostic nature and broad applicability mos on the asset’s generated data. Nevertheless, there are various challenges to consider in data-driven PdM, such as algorithm selection, hyperparameter optimization, and uncertainty of the RUL estimation. This thesis proposes solutions and frameworks for these challenges using simulated datasets. We furthermore dive into scheduling optimization which is the next step in PdM and point towards the importance of understanding the data generating process in PdM using real-world data. Finally, we show how a method originally developed for PdM in the automotive industry can lend itself to the medical domain, exhibiting the significance of knowledge-transfer and the versatility of data-driven methods.

• Prognostics and Health Management (PHM)
• Predictive Maintenance
• Remaining Useful Life (RUL)
• Time-Series
• Symbolic Regression
• Uncertainty Quantification
• Scheduling Optimization
• Automated Machine Learning (AutoML)
• Hyperparameter Optimization

• All authors: Kefalas, M.
• Supervisor: Bäck, T.H.W.
• Co-supervisor: Baratchi, M.
• Committee: Jumar, U.; Apostolidis, A.; Plaat, A.; Mentens, N.; Stein, B. van
• Qualification: Doctor (dr.)
• Awarding Institution: Leiden Institute of Advanced Computer Science (LIACS), Faculty of Science, Leiden University
• Date: 2023-01-19