First Approaches to Automatically Diagnose and Reconfigure Hybrid Cyber-Physical Systems
Maintaining modern production machinery requires a significant amount of time and money. Still, plants suffer from expensive production stops and downtime due to faults within individual components. Often, plants are too complex and generate too much data to make manual analysis and diagnosis feasible. Instead, faults often occur unnoticed, resulting in a production stop. It is then the task of highly-skilled engineers to recognise and analyse symptoms and devise a diagnosis. Modern algorithms are more effective and help to detect and isolate faults faster and more precise, thus leading to increased plant availability and lower operating costs.In this paper we attempt to solve some of the described challenges. We describe a concept for an automated framework for hybrid cyberphysical production systems performing two distinct tasks: 1) fault diagnosis and 2) reconfiguration. For diagnosis, the inputs are connection and behaviour models of the components contained within the system and a model describing their causal dependencies. From this information the framework is able to automatically derive a diagnosis provided a set of known symptoms. Taking the output of the diagnosis as a foundation, the reconfiguration part generates a new configuration, which, if applicable, automatically recovers the plant from its faulty state and resumes production. The described concept is based on predicate logic, specifically Satisfiability-Modulo-Theory. The input models are transformed into logical predicates. These predicates are the input to an implementation of Reiter's diagnosis algorithm, which identifies the minimum-cardinality diagnosis. Taking this diagnosis, a reconfiguration algorithm determines a possible, alternative control, if existing. Therefore the current system structure described by the connection and component models is analysed and alternative production plans are searched. If such an alternative plan exists, it is transmitted to the control of the system. Otherwise, an error that the system is not reconfigurable is returned.
Machine Learning for Cyber Physical Systems