A Conceptual Architecture of Ontology Based KM System for Failure Mode and Effects Analysis

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.

Automatic Acquisition of Failure Mode and Effect Analysis Ontology for Sustainable Risk Management

“…Authors in [7] presented a conceptual framework for an ontology-based knowledge management system to preserve FMEA knowledge. This conceptual architecture was mainly based on domain-specific ontology to provide basic concepts of FMEA domain and relationship between them, a knowledge base to store instances, a query analyzer to ask knowledge base for a certain decision, and an inference engine to generate decisions from the knowledge base.…”

“…Many authors emphasized on the need of ontology-based content management of FMEA knowledge. Some FMEA ontologies have been designed to support domain-specific knowledge management [7]. However, no one presented any solution to map existing FMEA knowledge to ontologies.…”

Risk Management In Perspective Of Knowledge Management A Brief Survey

“…Risk is categorized as acceptable, if it can be understood and tolerated [6]. Some prominent risk assessment tools are; scenario analysis for event probability and impact, risk assessment matrix, Failure Mode and Effect Analysis, probability analysis; and Semi-quantitative scenario analysis [12]. Information about this phase can be generated from experts' views according to previously prepared analysis documents.…”

“…In this phase an assessed risk is assigned an appropriate response. It can be mitigated, avoided, transferred, or retained [12]. Risk response selection is a conscious decision taken by risk managers after experts' discussions and past experiences.…”

A knowledge representation of decision communications in scheme for automotive troubleshooting service

“…Ontology mentions as a definite description of a conceptualization, 3,8,9 is increasingly utilized in developing and conducting engineering knowledge in recent decade. [10][11][12] There are several critical benefits about ontology: (1) the ascendency in the expression of complicated relationships in a field; (2) information interoperability; (3) data reuse and sharing; and (4) definite description of terminologies to make communication easily.…”