When planning maintenance and repair, there are various challenges that have to be reconciled on a daily basis. If a machine is at a standstill, it devours money every minute. However, it is also clear that over-maintenance causes unnecessary costs due to strict maintenance cycles. Consequently, it is necessary to balance high availability with minimum maintenance requirements. This challenge becomes greater the more machines are in operation. This is because the number of influencing factors increases with each plant, some of which are mutually dependent or mutually exclusive (multicriticality). In this balancing act, many companies rely on a forward-looking strategy in which optimized maintenance and servicing decisions are made by continuously monitoring the condition of the machines. Solutions that not only take into account technical data, e.g., pressure, temperature, or hours worked since the last maintenance, but also include business aspects such as adherence to schedules, utilization of resources, state of depreciation, or need for modernization in the decision-making process – in a cumulative and balanced manner – have proven particularly successful. Due to the volume of data and complex interrelationships, this is achieved primarily by AI-based methods.

Qualicision’s field-proven AI-based, self-learning decision support and optimization continuously evaluates different assets on the basis of qualitatively labeled plant data – flexibly scalable and thus suitable for predictive maintenance of a single plant as well as for predictive asset management for geographically distributed plant networks. This creates an additional, AI-independent explanation layer whose simple visualization makes the system’s decisions comprehensible and usable even for non-data analysts. The basis is provided by Qualitative Labeling.