The application of microprocessor-based diagnostic strategies to industrial processes provides improved system reliability while reducing maintenance costs. The prompt detection and classification of system anomalies enables reduced troubleshooting by technicians and minimizes the misclassification of system degradations. In this paper, an experimental-based multiple hypothesis failure isolation scheme for small-scale thermofluid systems is presented. The proposed classification strategy 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 hypothesized failure modes. The overall performance of the isolation scheme is investigated for a series of experimentally simulated heat pump failures.

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