Model Transformation from CBM to EPL Rules to Detect Failure Symptoms
Résumé
The increasing complexity of modern systems, cost reduction policies and ever increasing safety requirements are bringing new challenges to the maintenance domain. In many fields, periodic maintenance actions become either insufficient or too expensive. In this context, Condition-Based Maintenance (CBM) strategies, and Prognostics and Health Management (PHM) in particular, are offering an interesting alternative by allowing systems to be maintained only when needed. These strategies rely on a constant monitoring and analysis of the systems operating conditions in order to detect and identify a failure when it occurs and even sometimes beforehand.
Nowadays, two main approaches are explored to detect failures in PHM solutions: one based on machine learning, the other based on expertise and capitalised system knowledge. This work proposes to combine a Complex Event Processing (CEP), to manage incoming data’s volumetry and velocity, with an Expert System (ES) in charge of exploiting the capitalized knowledge. This paper focuses on the configuration of a CEP from rules contained in a CBM ES using a Model Driven Architecture (MDA). This configuration is a challenge, especially regarding the management of rules with temporal parameters and the need for intermediate results to deal with the rule’s complexity.
Mots clés
Knowledge base
Maintenance
Model transformation
Cost reduction
Expert systems
Information management
Safety engineering
Software architecture
Complex event processing (CEP)
Condition-based maintenance
Intermediate results
Model driven architectures
Monitoring and analysis
Periodic maintenance
Prognostics and health managements
Software design