Acquiring and Sharing Tacit Knowledge in Failure Diagnosis Analysis Using Intuitionistic and Pythagorean Assessments

Abstract

Nowadays knowledge management has received a considerable attention from both academics and industrial sectors, and expert knowledge is recognized as the most important resource of enterprises, particularly in the knowledge-intensive organizations. Dealing with knowledge creation, transfer, and utilization is increasingly critical for the long-term sustainable competitive advantage and success of any organization. Thus, a lot of efforts have been required from companies and researchers in developing and supporting knowledge management in different organizations. In industrial sectors as the highly competitive environment, capturing and disseminating of tacit knowledge are significant to an organization’s success with the development of knowledge-based systems. Safety and reliability analysis is an important issue to prevent an event which may be the occurrence of catastrophic accident in process industries. In this context, conventional safety and reliability assessment techniques like fault tree analysis have been widely used in this regard; however, in practical knowledge acquisition process, domain experts tend to express their judgments using multi-granularity linguistic term sets, and there usually exists uncertain and incomplete information since expert knowledge is experience-based and tacit. In addition, although the technical capabilities of expert systems based on fuzzy set theory are expanding, they still fall short of meeting the increasingly complex knowledge demands and still suffer in subjective uncertainty processing and dynamic structure representation which are important in risk assessment procedure. In this paper, a new framework based on 2-tuple intuitionistic fuzzy numbers, Pythagorean fuzzy sets, and Bayesian network mechanism is proposed to evaluate system reliability, to deal with mentioned drawbacks, and to recognize the most critical system components which affect the system reliability.