A FAILURE ISOLATION STRATEGY FOR THERMOFLUID SYSTEM DIAGNOSTICS

The application of microprocessor-based diagnostic strategies to indus trial processes provides improved system reliability while reducing ma intenance costs. The prompt detection and classification of system ano malies enables reduced troubleshooting by technicians and minimizes th e misclassification of system degradations. In this paper, an experime ntal-based multiple hypothesis failure isolation scheme for small-scal e thermofluid systems is presented. The proposed classification strate gy combines dynamic modeling, control theory, multivariate statistics, and pattern recognition to develop a methodology that may be tailored for a variety of applications. The primary goal of this study is the reduction of modeling activities associated with the set of hypothesiz ed failure modes. The overall performance of the isolation scheme is i nvestigated for a series of experimentally simulated heat pump failure s.