Fusing diagnostic information without a priori performance knowledge

Diagnostic information fusion is the method by which one would determine a system´s state for those instances where several different diagnostic tools, and possibly other sources, are used for state estimation. Because system state predictions from different diagnostic tools will disagree to some extent, if not completely contradict one another, a robust fusion tool is necessary to produce a reliable assessment of system state. The paper addresses the need for a reliable solution to the problem of diagnostic information fusion, particularly with the absence of a priori knowledge of diagnostic tool performance. Tool performance specifications are often hard to come by, in particular where data about events are sparse or where a comprehensive evaluation cannot be performed. In response, a fusion process, using a set of neural networks, was developed to distinguish recognizable patterns from the output of the individual diagnostic tools. This fusion concept was applied to data that were gathered from a high-speed milling machine and processed by several previously developed diagnostic tools.