Data-​Driven Intelligent Predictive Maintenance of Industrial Assets

Project Description

The amount of data collected per period unit has recently grown by orders of magnitude, potentially resulting in much better information on system condition. System health condition is increasingly captured by different types of monitoring systems, at different time scales, with different types of data, ranging from vibration signals to video streaming. The large volume and variety of condition monitoring data drive traditional model- and knowledge-based approaches too difficult to implement and too demanding in resource usage.

The research project tackles the challenge of effectively using the diverse, high-dimensional, high-velocity condition monitoring data of industrial assets to improve their availability and resilience. Intelligent adaptive hybrid predictive maintenance algorithms will be developed in this research project based on a combination of physical performance models and deep neural networks learning the hidden information, dependencies, and structure in condition monitoring data. The developed framework aims at extracting high-level knowledge representations from high-dimensional condition monitoring signals with a high volume, variety and velocity. The developed algorithms will enable (a) learning fault signatures from heterogeneous condition monitoring data sources, (b) transferring them between different systems of the same fleet and (c) predicting failures based on the learned fault signatures and their predicted evolution in time. The developed approaches will enable a fast and efficient adaptation of predictive maintenance systems to new operating conditions and newly developed systems.