Rogue components: their effect and control using logical analysis of data

Abstract

There is a small subset of any repairable component population that can develop a failure mode outside the scope of the standard repair and overhaul procedures, which makes them “rogue”. When this happens, a Darwinian-like “natural selection” phenomenon ensures that they will be placed in the most disadvantageous position in the asset management program, negatively affecting multiple aspects of the operational and maintenance organizations. Rogue components have long plagued the airline industry and created havoc in their asset management programs. In this paper, we describe how these rogues develop, outline the natural selection process that leads to their hampering the asset management program, and examine some of the negative impacts that ensue. Then we propose a Condition based maintenance approach to control the development of these components. We explore the use of a supervised learning data mining technique called Logical analysis of data (LAD) in CBM for the purpose of detecting rogues within a population of repairable components. We apply the resulting LAD based decision model on an inventory of turbo compressors belonging to an airline fleet. Finally, we evaluate the applicability of LAD to the rogue component detection problem and review its efficiency as a decision model for this type of problem.