Authors:
Alexandre Sarazin
1
;
2
;
Sébastien Truptil
2
;
Aurélie Montarnal
2
;
Jacques Lamothe
2
;
Julien Commanay
1
and
Laurent Sagaspe
1
Affiliations:
1
Digital & Software Department, APSYS, Blagnac, France
;
2
Industrial Engineering Center, IMT Mines Albi, Albi, France
Keyword(s):
Maintenance, Knowledge Base, Model Transformation.
Abstract:
Over the past decades, the development of predictive maintenance strategies, like Prognostics and Health Management (PHM), have brought new opportunities to the maintenance domain. However, implementing such systems addresses several challenges. First, all information related to the system description and failure definition must be collected and processed. In this regard, using an expert system (ES) seems interesting. The second challenge, when monitoring complex systems, is to deal with the high volume and velocity of the input data. To reduce them, Complex Event Processing (CEP) can be used to identify relevant events, based on predefined rules. These rules can be extracted from the ES knowledge base using model transformation. This process consists in transforming some concepts from a source to a target model using transformation rules. In this paper, we propose to transform a part of the knowledge from a condition-based maintenance (CBM) model into CEP rules. After further explai
ning the motivations behind this work and defining the principles behind model-driven architecture and model transformation, the transformation from a CBM model to a “generic rules” model will be proposed. This model will then be transformed into an Event Processing Language (EPL) model. Examples will be given as illustrations for each transformation.
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