The FYPA²C project established an evolution support process which follows the idea of establishing a so called “Anti-Aging Cycle” for production systems. This Anti-Aging Cycle tries to glue the system behavior and system properties regarding non-functional requirements together by constantly comparing system behavior with models acquired at runtime.The process consists of three parts, namely acquisition, representation and processing, as well as appraisal of knowledge.
The knowledge acquisition is based on monitoring events of externally observable signals in event traces during runtime. As known from knowledge management, data (the recorded event traces) can be lifted to knowledge (the required process properties). In order to do so, event traces are enriched by linking the monitored signals with simple semantic annotations to represent an enriched information base for a model generation and interpretation.
Further abstraction is achieved in the representation part by using so called knowledge models. Each of these models represents specific aspects of the monitored system in runtime artefacts. Models are learned from event traces by suitable domain specific learning algorithms in the processing part. These algorithms bring the provided enriched information into relationships and, therefore, allow for reasoning at a more abstract level. The learned models are used to detect evolutionary changes by comparing runtime event traces with the knowledge models.
To give a valuable evaluation support of the detected evolution, the appraisal part raises the abstraction level by automatically analyzing the knowledge models regarding typical system properties of interest. Both, the analysis results as well as the detected changes, are presented to the operator. These high-level properties can then be evaluated even by non-technical staff (e.g. the plant manager) in order to initialize anti-aging countermeasures.