Carly Fiorina, Former CEO of HP
Predictive maintenance helps to reduce downtimes and equipment failure with more on-point settings and thus significantly reduce costs. Artificial intelligence based insights and recommendations do not only make maintenance more efficient for personnel, but they can also investigate and understand what leads to downtimes and how they can prevent them in the first place.
Vienna has one of the biggest district heating networks in Europe, consisting of thousands of converter stations cooling down the water before it is delivered to the end customer.
In a first step, the team of the utility company and craftworks were sitting together in order to find out which problems should be tackled and what the goals are. In this case, it was the inefficiency in the maintenance work that should be addressed. After the use case was defined, the teams from both companies checked if there was enough quality data and got the first understanding of processes, parameters, and their values. In certain cases, it might be necessary to use additional sensors from f.e. trusted craftworks partner companies. In a next step, related data and patterns in sensor-, machine, and process data were recognized, before a machine learning model and architecture was developed. At the end of the first phase, a simple software prototype was reviewed verifying the recommendations and identifying enhancements.