Robust sensor fault diagnosis and validation in HVAC systems

Abstract

A robust fault diagnosis and validation strategy for temperature sensors and flow meters in central chilling plant is developed, which is based on the first law of thermodynamics. The strategy evaluates soft sensor faults (biases) by examining and minimizing the sum of the squares of concerned mass or steady state energy balance residuals represented by the corrected measurement over a period. It considers systematically all the concerned energy balances and obtains the best estimates of the sensor biases by minimizing the sum of the mean squares of normalized residuals of all energy balances involved. The genetic algorithm technique is employed to determine the global minimal solution to the multimodal objective function, which can be difficult to achieve by traditional gradient-directed search methods. Performance of an advanced robust fault detection, diagnosis and evaluation (FDD&E) scheme is compared with that of a sequential scheme, which was reported earlier, in simulation tests. The robust scheme is superior to the sequential scheme in robustness to abrupt sensor faults, such as biases, etc. The robust scheme is applied to a central chilling plant in an existing commercial building, providing satisfied bias estimates. As a basic method, the sensor FDD&E strategy is of practical value in heating, ventilation and air-conditioning (HVAC) systems as well as in systems where the measurements of liquid flow variables are essential to control and performance monitoring.