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... Show morePredictive 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. Show less
The research topic of the thesis is the extension of evolutionary multi-objective optimization for real-world scheduling problems. Several novel algorithms are proposed: the diversity indicator... Show moreThe research topic of the thesis is the extension of evolutionary multi-objective optimization for real-world scheduling problems. Several novel algorithms are proposed: the diversity indicator-based multi-objective evolutionary algorithm (DI-MOEA) can achieve a uniformly distributed solution set; the preference-based MOEA can obtain preferred solutions; the edge-rotated cone can improve the performance of MOEAs for many-objective optimization; and dynamic MOEA takes the stability as an extra objective. Besides the classical flexible job shop scheduling, the thesis proposes solutions for the novel problem domain of vehicle fleet maintenance scheduling optimization (VFMSO). The problem originated from the CIMPLO (Cross-Industry Predictive Maintenance Optimization Platform) project and the project partners Honda and KLM. The VFMSO problem is to determine the maintenance schedule for the vehicle fleet, meaning to find the best maintenance order, location and time for each component in the vehicle fleet based on the predicted remaining useful lifetimes of components and conditions of available workshops. The maintenance schedule is optimized to bring business advantages to industries, i.e., to reduce maintenance time, increase safety and save repair expenses. After formulating the problem as a scalable benchmark in an industrially relevant setting, the proposed algorithms have been successfully used to solve VFMSO problem instances. Show less
