Data-driven predictive maintenance for utility networks

Making full use of all available network-specific data/information is the key to a more efficient maintenance planning. In combination with engineering models and expertise, machine learning allows to develop predictive maintenance planning, which can lead to a substantial increase in maintenance efficiency. To make full use of the potential, the predictive models must be integrated with our client’s maintenance planning processes.
Components of utility network have to be maintained. Predictive strategies can help to identifiy the mos critical components.

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.