Important sensors for chiller fault detection and diagnosis (FDD) from the perspective of feature selection and machine learning

This study employs feature selection techniques, such as mutual-information-based filter and genetic-algorithm-based wrapper, to help search for the important sensors in data driven chiller FDD applications, so as to improve FDD performance while saving initial sensor cost.