Imos Lab - Epfl

The Chair of Intelligent Maintenance & Operations Systems focuses on developing intelligent algorithms to improve the performance, reliability, and availability of complex industrial assets and make maintenance more cost-efficient. 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. The measured condition monitoring signals of complex industrial assets are typically high dimensional, highly redundant, have several interdependencies and prevalent non-linear relationships. 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 and to develop model-based approaches. Even collecting a representative dataset with all possible operating conditions can be a challenging task (depending on the variability of the operating regimes of the assets) and may delay the implementation of data-driven fault detection systems.