A performance tracking methodology and decision support model

This paper presents a methodology to detect performance degradation with a certain degree of confidence, and a decision support model to help engineers and technicians to solve ongoing diagnostic and repair problems. The method is capable of detecting changes in performance trends when data captured at different times or ages during the system's active life, is compared to estimated performance limits. This paper applies the method to the specific case of aircraft avionic systems and their associated support equipment. The methodology is currently being developed and applied to existing aircraft avionic equipment and maintenance processes. The methodology explores the ability of statistical control process (SPC) applications and expert systems (ES) based technologies to develop trend analyses data and provide performance degradation information to maintenance engineers, technicians, and managers. The paper addresses specific data capture and component identification problems encountered in actual test data, anal discusses automated solutions for these problems. A complete architecture is presented displaying the data capture process, data storage, statistical and expert system processing, and output information display. The solution includes the specific technology used to implement the process and output information samples based on actual test data.