Sensor faults occur unavoidably but cannot be detected easily. Sensor measurement is fundamental for safe operation and optimal energy conservation of refrigeration and air-conditioning systems. After analyzed procedure for sensor fault detection based on the principal component analysis (PCA) with Q-statistics as fault detection boundary

blind zone prediction was established as an index to estimate sensor fault detectability. The index was used to assess fault detectability of each sensor in training dataset and to analyze

evaluate

and optimize dataset quality of training model. Results of analyzing in-site and laboratory datasets of water-cooled chillers at different introduced fault levels show that the blind zone can effectively predict fault detection outcome for sensors by selected datasets.