Over recent decades computer and software systems become more and more complex because of the applications and user requirements. The complexity makes the software systems more vulnerable to the error and bugs. Also, environmental situations affect software systems which do not react to the environmental activities. Self-healing architectures have been proposed in order to make systems defeat these problems and to make systems capable of reacting to the environmental activity. Hence, these architectures help system to become dynamic and more robust, but finding a proper architecture which can support and cover system requirements is an issue.
This is particularly true in industrial environments, which consist of some known and some unknown parameters.
This paper presents an architecture that can be used in some industrial environment to facilitate the process of adapting the system to unpredicted situations. This architecture has been developed over the base of RAINBOW infrastructure and it is compliant to the MAPE control loop (Autonomic Computing control loop). The paper reports also about the practical experience of implementing this architecture for a painter robot in an automotive factory, which deals with problems in painted part by itself. The proposed architecture uses rule-based reasoning and it actualizes the method of environmental modeling by using a rule-based system as the model extractor. The results of the implementation shows huge benefits in reusability and even in the quality of painting process.