Machine learning for predictive maintenance applications

Early and reliable detection, isolation and prediction of faulty system conditions enable operators to take corrective actions to prevent critical system failures, ensuring high levels of availability and safety. This is particularly crucial for complex systems such as infrastructures, power plants, and aircraft engines. Consequently, these systems are increasingly monitored by a large number of diverse condition monitoring sensors.

With the increased availability of system condition data and the growing complexity of explicit physics-based system models, the application of data-driven approaches for predictive maintenance has been rising recently.

This course provides insights and hands-on experience in selecting, designing, optimizing and evaluating machine learning algorithms to address the challenges faced by predictive maintenance systems in 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

-Federated learning in predictive maintenance applications

-Graph Neural Networks for predictive maintenance applications

-Phyics-guided machine learning for predictive maintenance applications

-Introduction to decision support systems for maintenance applications

-Benefits and costs of predictive maintenance