Machine learning for predictive maintenance applications

Early and reliable detection, isolation and prediction of faulty system conditions enables the operators to take recovery actions to prevent critical system failures and ensure a high level of availability and safety. This is particularly crucial for complex systems such as infrastructures, power plants and aircraft engines. Therefore, their system condition is increasingly tightly monitored by a large number of diverse condition monitoring sensors. With the increased availability of data on system condition on the one hand, and the increased complexity of explicit physics-based system models on the other hand, the application of data-driven approaches for predictive maintenance has been recently increasing.
This course provides insights and hands-on experience in selecting, designing, optimizing and evaluating machine learning algorithms to tackle the challenges faced by maintenance systems of complex engineered systems.

Specific topics include:

-Introduction to condition monitoring and predictive maintenance systems

-Feature extraction and selection methodology

-Machine learning algorithms for fault detection and fault diagnostics

-End-to-end learning architectures for fault detection and fault diagnostics

-Unsupervised and semi-supervised learning algorithms for predictive maintenance

-Deep learning algorithms for predicting the remaining useful lifetime

-Performance evaluation of the algorithms

-Predictive maintenance systems at fleet level

-Domain adaptation for fault diagnostics

-Explainable machine learning algorithms for predictive maintenance

-Introduction to decision support systems for maintenance applications

-Benefits and costs of predictive maintenance