Sensor drift fault diagnosis for chiller system using deep recurrent canonical correlation analysis and k-nearest neighbor classifier

Early detection and diagnosis of the chiller sensor drift fault are crucial to maintain normal operation for energy saving. Due to the complex physical structure and operation conditions, sensor drift fault in the chiller system is difficult to discover. To improve the energy efficiency and operation reliability of the chiller system, this paper proposes a novel chiller sensor drift fault diagnosis method using deep recurrent canonical correlation analysis and k-nearest neighbor (KNN) classifier. A deep bidirectional long short-term memory recurrent neural network-based deep recurrent canonical correlation analysis (BLCCA) model is developed, which can automatically extract the nonlinear and temporal features from raw operation data in the chiller system. Based on the proposed BLCCA model, a residual generator is designed to generate the directional residual vector. The cumulative residual vector method is employed to improve the detectability of the sensor drift fault. An efficient KNN-based method is applied to classify the residual vector and judge the faulty sensor. Different distance measures and neighbor numbers are further analyzed to optimize the fault diagnosis performance. The proposed fault detection and diagnosis (FDD) method is validated by using a data set which has been collected from an actual chiller system. Three different state-of-the-art fault diagnosis methods are used for comparison with the proposed method. The comparisons of the experimental results demonstrate that this method achieves significant fault diagnosis performance in terms of diagnosis accuracy, recall, and F measure (F1 score).