Application of LSTM Method Combined with Feature Optimization in Chiller Failure Detection

Abstract

Aiming at the characteristics of a long short-term memory network (LSTM) which is suitable for processing high-dimensional, strongly coupled, and highly time-dependent data, it combines the advantages of feature selection to reduce the difficulty of learning tasks and improve the performance of model fault diagnosis. This paper proposes an LSTM method combining sequential floating forward search with integrated feature selection for chiller sensor deviation fault detection. The detection results of the proposed method are compared with those of the single feature selection method, and it is concluded that the detection efficiency of the proposed method in chiller sensor deviation fault detection is significantly better than that of the single feature selection method.