The amount of measured and collected condition monitoring data for complex industrial assets has been recently increasing significantly due to falling costs, improved technology, and increased reliability of sensors and data transmission. However, faults in safety critical systems are rare. The diversity of the fault types and operating conditions makes it often impossible to extract and learn the fault patterns of all the possible fault types affecting a system. Consequently, faulty conditions cannot be used to learn patterns from. Even collecting a representative dataset with all possible operating conditions can be a challenging task since the systems experience a high variability of operating conditions. Therefore, training samples captured over limited time periods may not be representative for the entire operating profile. The collection of a representative dataset may delay the implementation of data-​driven fault detection and isolation systems. Furthermore, domain experts require an interpretability of the obtained results. The talk will give some insights into potential solutions that enable to

1) transfer models and operational experience between different units of a fleet and between different operating conditions also in unsupervised setups where data on faulty conditions is not available; and

2) fuse physical performance models and deep neural networks, thereby not only improving the performance but also improving the interpretability of the developed models.