Qualicision Predictive Maintenance

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 (see Figure 1). Using a corresponding labeling function, the software observes, for example, which temperature ranges of the sensor data provided indicate a need for maintenance and differentiates between positive, i.e. more desirable, machine states and negative value ranges, i.e. undesirable machine states. It then assigns positive and negative connotations - the so-called labels - to the corresponding sensor data.

Anyone who operates machinery and plant parks must find a good balance between the highest possible availability and the lowest possible maintenance costs. This can be achieved by holistic and consolidated asset management. Optimized, the relevant interactions can be managed by using artificial intelligence methods, especially if these show those people in charge of the process recommendations for action, the evaluation of which does not require any knowledge of AI. Software solutions for optimized maintenance and repair management are also practical if, in addition to suitable scaling options, such solutions can also map the entire process from maintenance recommendations and concrete planning of maintenance operations to continuous monitoring of the processes (Figure 3, left), e.g., by means of messages on the processing status of maintenance and repair operations, and are therefore suitable for both predictive maintenance and predictive asset management scenarios. In the example, this is done by taking into account other influencing factors that are processed using the same systematics and the principle of Qualitative Labeling. Likewise, the learning logic that can be used in the background can learn interactions and systematics at a high scaling level. Consequently, the only change concerns scaling, e.g., with regard to the use of databases and further maintenance management tools such as PSIcommand. On this basis, companies can also gradually approach predictive asset management with predictive maintenance for individual machines and plants and implement a holistic strategy for asset management (Figure 3, right) in the sense of a rolling intelligent process.