A Conceptual Architecture of Ontology Based KM System for Failure Mode and Effects Analysis
Keywords:
Knowledge Management (KM), ontology, Failure Mode and Effects Analysis (FMEA)Abstract
Failure Mode and Effects Analysis (FMEA) is a systematic method for
procedure analyses and risk assessment. It is a structured way to identify potential
failure modes of a product or process, probability of their occurrence, and their overall
effects. The basic purpose of this analysis is to mitigate the risk and the impact
associated to a failure by planning and prioritizing actions to make a product or a
process robust to failure. Effective manufacturing and improved quality products
are the fruits of successful implementation of FMEA. During this activity valuable
knowledge is generated which turns into product or process quality and efficiency. If
this knowledge can be shared and reused then it would be helpful in early identification
of failure points and their troubleshooting, and will also help the quality management
to get decision support in time. But integration and reuse of this knowledge is difficult
because there are number of challenges e.g., unavailability of unified criteria of FMEA
knowledge, lack of semantic organization, natural language text based description of
knowledge, most of the times FMEA is started from scratch instead of using existing
knowledge that makes it incomplete for larger systems, and above all its success
depends on the knowledge which is stored in the brains of perfectionists in the form
of experience which may or may not be available anytime anywhere. In this article we
are proposing an Information and Communication Technology (ICT) based solution
to preserve, reuse, and share the valuable knowledge produced during FMEA. In
proposed system existing knowledge available in repositories and experts head will be
gathered and stored in a knowledge base using an ontology, and at the time of need this
knowledge base will be inferred to make decisions in order to mitigate the probable
risks. Ontology based approaches are best suited for the knowledge management
systems, in which human experts are required to model and analyze their expertise
in order to feed them in a conceptual knowledge base for its preservation and reuse.
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