Setting up an efficient data-driven maintenance planning for utility networks poses challenges that require an approach tailored to the problem at hand. These main challenges are (i) data-driven models cannot extrapolate, (ii) available data sets often contain incomplete, sparse, noisy or indirect data, (iii) for safety-critical assets and systems, one needs to demonstrate that the required level of safety is maintained at any time, and (iv) integrating a data-driven model in the decision process and exploiting it to make optimal decisions is not straightforward.
Addressing these challenges, we offer holistic solutions to coherently integrate probabilistic models in decision making for maintenance planning. Information on our solutions to the posed challenges is contained in our Pitch Deck.