In the refrigeration and air conditioning system

sensors are independent component for physical data measuring and operating state monitoring. Sensor faults

especially sensor biases output will lead to incorrect measurement

inappropriate controlling strategy and further energy consumption rise. Based on the pattern recognition theory

the fault detection task could be considered as a one-class classification problem. Therefore

a powerful pattern recognition-based one-class classification algorithm

Support Vector Data Description (SVDD) was used to detect the sensor biases occurring in a chiller system. The practical fault-free data were used as training dataset to develop the SVDD model so as to detect the sensor faults. The method and its fault detection efficiency were validated by test dataset with different artificially introduced levels of sensor biases. The SVDD-based fault detection method worked well with chiller practical operating measurements

but the fault detection efficiencies of different sensors with different level faults were inconsistent.