A Rough Set Approach to the Ordering of Basic Events in a Fault Tree for Fault Diagnosis

The performance of manufacturing systems or equipment is, to a great extent, dependent upon the condition of their components. Closely monitoring the condition of the critical components and carrying out timely system diagnosis whenever a fault symptom is detected would help to reduce system downtime and improve overall productivity. Fault tree analysis (FTA) is a powerful tool for reliability studies and risk assessment. However, most research on FTA focuses on the generation of minimum cut sets and how to calculate the probability of main events. As a result, the issue concerning the ordering of basic events in a fault tree has been largely neglected. In this paper, a novel approach based on rough set theory and a pairwise comparison table for fault diagnosis is proposed. The approach attempts to learn from the pattern of decision-making by domain experts from past experience and uses the knowledge acquired, which is in the form of a minimum decision rule set, to determine the ordering of basic events in a fault tree. The details of the approach, together with the basic concepts of rough set theory, are presented. A case study is used to illustrate the application of the proposed approach. Results show that a reasonable ordering of basic events in a fault tree can be generated easily. With the ordering of basic events determined, a maintenance engineer in a manufacturing plant can then carry out fault diagnosis in an efficient and orderly manner.