A Conceptual Architecture of Ontology Based KM System for Failure Mode and Effects Analysis
Zobia Rehman, Ștefania Kifor · International Journal of Computers Communications & Control · 2014
Failure Mode and Effects Analysis (FMEA) is a systematic method forprocedure analyses and risk assessment. It is a structured way to identify potentialfailure modes of a product or process, probability of their occurrence, and their overalleffects. The basic purpose of this analysis is to mitigate the risk and the impactassociated to a failure by planning and prioritizing actions to make a product or aprocess robust to failure. Effective manufacturing and improved quality productsare the fruits of successful implementation of FMEA. During this activity valuableknowledge is generated which turns into product or process quality and efficiency. Ifthis knowledge can be shared and reused then it would be helpful in early identificationof failure points and their troubleshooting, and will also help the quality managementto get decision support in time. But integration and reuse of this knowledge is difficultbecause there are number of challenges e.g., unavailability of unified criteria of FMEAknowledge, lack of semantic organization, natural language text based description ofknowledge, most of the times FMEA is started from scratch instead of using existingknowledge that makes it incomplete for larger systems, and above all its successdepends on the knowledge which is stored in the brains of perfectionists in the formof experience which may or may not be available anytime anywhere. In this article weare proposing an Information and Communication Technology (ICT) based solutionto preserve, reuse, and share the valuable knowledge produced during FMEA. Inproposed system existing knowledge available in repositories and experts head will begathered and stored in a knowledge base using an ontology, and at the time of need thisknowledge base will be inferred to make decisions in order to mitigate the probablerisks. Ontology based approaches are best suited for the knowledge managementsystems, in which human experts are required to model and analyze their expertisein order to feed them in a conceptual knowledge base for its preservation and reuse.