A soft-computing framework for fault diagnosis

Abstract

The field of fault diagnosis (FD) is undergoing rapid change in the methodologies it employs, and the vast array of materials/components/structures where it is applied. The field has grown steadily because of (a) the importance attached to fail-safe mechanisms; (b) ability to detect impending failure so that preventive and corrective action can be taken and (c) the opportunities opened up by automating the diagnosis process itself. FD as a tool has to draw knowledge and expertise from a variety of scientific fields and apply multi-disciplined knowledge to empirical domains in order to achieve its objectives. Application and use of automated FD concepts to empirical domains do not require merely crisp rules, but largely, a flexible and natural framework where "experience" often determines the success or failure of diagnosis. In this paper, we describe a flexible framework, which incorporates the ideas and tools of soft computing in its methods and approach.