Bayesian Network Training Method to Produce a Condition-Based Maintenance Strategy in Aviation Maintenance Programs

Abstract

With an understanding of the current industry and organization orientation, the aviation maintenance industry is preparing a new paradigm shift toward a CBM (Condition-Based Maintenance) strategy. However, one challenge the aviation maintenance industry faces is the lack of CBM training support in the current education setting. This study aims to fill the gap in the CBM strategy training in current aviation maintenance programs. The authors propose Condition-Based Maintenance Bayesian Network (CBM-BN) training materials. The BN has a different principal approach than other frequentist principles, which can generate a prediction model concerning all heterogeneous information. In this paper, the authors describe a framework to develop CBM-BN training material that can be performed in aviation maintenance education. The proposed CBM-BN framework has probability concepts and ten steps; each step has three sections, including materials, activities, and examples for instructors and students. The case study demonstrates that the developed CBM-BN framework and training materials could facilitate CBM strategy training in aviation maintenance programs. A mechanic who can do CBM analysis will be more beneficial and demandable in the job market and contribute to full CBM implementation. Moreover, other CBM educational materials would be needed to compensate for the limitations of BN and increase the maturity level of CBM.