Predicting the remaining useful life of a system or a component enables taking preemptive mitigating actions, thereby reducing the downtime and improving the system reliability and availability. Yet, there is a set of different actions that can be taken if the remaining useful life of a system is known, including maintenance actions aiming to prolong the remaining useful life or preemptive replacements of the component. However, a preemptive replacement at the end of the remaining useful life may not be the optimal action in terms of system performance. A more efficient use of the knowledge on the fault evolution in time is to use this information for proactive control of the remaining useful life and the system performance. The project aims at developing reinforcement-learning approaches for optimal fault mitigation and system health management. A collaboration with the Prognostics Center of Excellence (PCoE) at NASA Ames Research Center is foreseen for this project.
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学位类型：
博士
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博士
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0
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开学时间：
秋季
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申请截止时间：
10月20日
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背景偏好：We are looking for a highly motivated candidate holding a Master's Degree in engineering, physics, applied mathematics, control, computer science or related fields with experience in predictive maintenance, machine and deep learning and particularly in reinforcement learning. The successful candidate has strong analytical skills, is proactive, self-driven with strong problem solving abilities and out-of-the-box thinking. Moreover, programming experience, preferably in Python, is expected. Professional command of English (both written and spoken) is mandatory and knowledge in German is beneficial. You enjoy working in an interactive international environment with other doctoral students and post-docs, referring continuously to practical problems and solutions and collaborating with industrial project partners.
招生人：Prof. Dr. Olga Fink
招生邮箱：ofink@ethz.ch
招生网页：https://ims.ibi.ethz.ch/
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