An integrated robust strategy for diagnosing sensor faults in building chilling systems

An integrated robust strategy for the detection, diagnosis, and validation of the soft sensor faults commonly found in building chilling systems is presented. The integrated strategy, which extends the works of Wang and Wang (1999), is based on the universal conservation relationships for mass and energy. Biases in temperature sensors and flow meters are estimated by minimizing the sums of the squares of the mass and energy balance residuals. A robust approach using a genetic algorithm is used to systematically minimize the sums of the squares of the associated heat balance residuals so that the biases are estimated more reliably and accurately. A correlation cancellation method is developed to estimate the cooling water flow meter bias, under conditions in which the cooling water temperature sensor biases exist and are unknown. The correlation cancellation method uses a derived characteristic quantity to estimate the water flow meter bias. Validation tests are performed using a dynamic simulation together with afield case study conducted in an existing building chilling system. Even in unfavorable conditions, the integrated robust strategy can estimate the biases of the chilled and cooling water flow meters and the relative biases of the chilled and cooling water temperature sensors accurately and robustly.