A Novel Algorithmic Approach to the Integration of Posterior Knowledge into Condition Monitoring Systems

This paper considers the problem of the integration of "posterior Knowledge" into condition monitoring systems from both the theoretical and practical points of view. The work is presented in the context of aircraft engine maintenance. A methodology for updating posterior probabilities is proposed for cases where fault conditions are rejected or retained on the basis of external knowledge supplied by an end user-the posterior knowledge. A possible fault class ranking is generated following the specification of fault class posterior probability functions. Context free simulations are used to show the effect of posterior knowledge as part of a maintenance strategy. The simulations are independent of any specific condition-monitoring situation. Preliminary results indicate that posterior knowledge reduces the number of sub-unit inspections required for isolation of all faults. This has the potential to result in real maintenance cost savings.