N.N. Akimov - Chief Designer of SCADA Nuclear Energy Objects and Energy Industry - Deputy Director for Innovative Development, FSUE «FRPS NIIIS named after Yu.Ye. Sedakov» (Nizhny Novgorod) A.V. Bukhnin - Ph.D. (Eng.), Associate Professor, Nizhny Novgorod State Technical University n.a. R.E. Alekseev V.R. Milov - Dr.Sc. (Eng.), Professor, Head of the Department, Nizhny Novgorod State Technical University n.a. R.E. Alekseev А.V. Koltcov - Deputy Chief Designer, FSUE «FRPS NIIIS named after Yu.Ye. Sedakov» (Nizhny Novgorod) А.А. Kuranov - Student, Nizhny Novgorod State Technical University n.a. R.E. Alekseev

Drastic complication of modern control systems leads to the increased demand for improved verification procedures during the system integration phase. That is especially relevant to distributed process control systems. The verification complexity is even higher for hybrid systems. In this case, physical processes in the controlled objects and elements of the control system are continuous. This paper presents an approach to verification and validation of hybrid technical system models using process-mining techniques. Currently, these techniques are mainly applied to business-process optimization, although there are successful examples of their application to technical systems. Process-mining techniques allow automatic process model generation as Petri nets, heuristics nets, or in BPMN (Business Process Model and Notation) format. Such models can be used for technical system model verification in order to reveal unwanted behavior. For that, the target process model created by the expert is compared with the model automatically generated basing on the event log produced during system model operation. Differences between the models are represented with a difference graph. Another approach to verification is conformance checking where the event log itself is matched to the target model. When the technical system is put to operation, the process model can be validated in order to reveal divergence from the observed behavior. In that case, the event log is produced during system operation. The log itself can be matched to the process model created for the system model, or it can be used for generating the actual process model, which is compared with the process model created for the system model. The proof of concept is demonstrated with the example of simulating a simple hydraulic system in SimInTech (a technical system simulator), and model verification in ProM (a process-mining framework) where the target and the actual process models are compared. Development of the process mining methods should allow solving the verification problem during design of safety-critical distributed control systems.

 

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