IAI: Industrial Artificial Intelligence for intelligent machines and manufacturing digitalization

IAI is an Innosuisse project, funded under the joint call in which partners from South Korea and Switzerland are invited to collaborate. At the Swiss side of the consortium, our team collaborates with ABB, a pioneering technology leader with a vivid interest and a profound knowledge in predictive maintenance. The South Korean side of the consortium is composed by three partners: The SHRM Lab from the Seoul National University contributes its considerable experience in Prognostic and Health Management, the SMLD team of the Dongguk University contributes with its physical modelling and data cleansing experience and, lastly, OnePredict brings its expertise in predictive maintenance.

The predictive maintenance field has not yet benefited from the recent advances in deep learning. This is mainly because deep learning is rather data hungry, typically needing tens of thousands of training examples. Real-life maintenance data are composed of large quantities of samples referring to normal operating conditions with only very few samples of abnormal or faulty behaviour. In addition, the real-life samples are specific to the particular operating conditions under which they were collected. A change in the operating environment can have an important effect on the behaviour of the equipment and therefore the monitoring data.


In IAI, we build on the recent advances in deep learning and develop methods which are tailored to the particularities of predictive maintenance. To do so, we study regularization techniques to bring in domain knowledge into the modelling and data augmentation relying either on physical models and simulators or on machine learning conditional generative models to complement the scarce real-life data. The tools that will be developed in this project aim to push the state of the art in the field, by offering significant improvements to the predictive performance, resulting in significant savings in maintenance and operational costs.