A new structural framework for parity equation-based failure detection and isolation

A new framework for developing parity equations that prevent incorrect isolation decisions under marginal size failures in a decision process that tests each residual independently is described.

Abstract

The paper describes a new framework for developing parity equations that prevent incorrect isolation decisions under marginal size failures in a decision process that tests each residual independently. Test thresholds that take the noise conditions into account are set high to reduce the occurrence of false alarms while maintaining the algorithm's ability to detect and isolate larger failures. The method is applicable to additive failures on the measured input and output variables and to additive plant disturbances. A transformation algorithm provides a multitude of models that satisfy the isolability requirements. A search procedure utilizing this model redundancy integrates model robustness considerations into the design.