Abstract

The catastrophic failure of many physical systems is often precipitated by degradation of minor system components. A real time microprocessor based diagnostic scheme may detect system abnormalities and apply various strategies to determine whether compensatory action is required. In this paper, a robust innovations-based failure detection methodology for improved heat pump performance is presented. A sixth-order nonlinear model and an extended Kalman filter were used to reconstruct the system's state based upon limited sensor feedback. The estimated mass flow rates and pressures were statistically monitored and failures predicted based upon the repeated violation of an alarm threshold. A series of heat pump failures are experimentally induced and representative results presented and analyzed to demonstrate the potential advantages of this diagnostic approach.

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