Auteur : Asma ABU SAMAH
Directeur de thèse : Eric ZAMAÏ
Date : 8 novembre 2016
Directeur de thèse : Eric ZAMAÏ
Date : 8 novembre 2016
Event based probabilistic approach for proactive
maintenance to improve production capacities in HMLV industries
maintenance to improve production capacities in HMLV industries
This research is carried out in the context of high-mix low-volume (HMLV) production environment. It is a production environment characterized by increasing demand volumes with product diversity and reducing product life cycles. This is a highly competitive production environment which require frequent changes in equipment settings and process recipes for product diversity and continuous introduction of new technologies to cope with increasing demand for more functional products. Moreover, ramp up production becomes highly critical because of reducing product life cycles. Therefore, to be successful in this highly competitive environment, we need stabilized and optimized production capacities to cope up with these challenges.
In such context, our objective are set as:
Avoiding and minimizing corrective maintenance.
2 integrated approaches was developed and presented as follows,
The first approach focuses on stopping the production equipment as a result of unscheduled breakdown if and only if it is the source of breakdown. This approach also concludes that to avoid confusion in failures and causes diagnosis, module must be benchmarked at sub-equipment level instead of at the equipment level. This approach is based on Bayesian Network (BN) and uses contextual data instead of sensors data.
The second approach focuses on the extraction of time bound failure signatures which guarantees to generate online failure alerts prior to specified time (time to repair and time to next production ste) such that proactive measures can be taken to minimize the repair time and to avoid failure/faults propagation.
This work is a part of the European INTEGRATE project.
In such context, our objective are set as:
Avoiding and minimizing corrective maintenance.
2 integrated approaches was developed and presented as follows,
The first approach focuses on stopping the production equipment as a result of unscheduled breakdown if and only if it is the source of breakdown. This approach also concludes that to avoid confusion in failures and causes diagnosis, module must be benchmarked at sub-equipment level instead of at the equipment level. This approach is based on Bayesian Network (BN) and uses contextual data instead of sensors data.
The second approach focuses on the extraction of time bound failure signatures which guarantees to generate online failure alerts prior to specified time (time to repair and time to next production ste) such that proactive measures can be taken to minimize the repair time and to avoid failure/faults propagation.
This work is a part of the European INTEGRATE project.