Diagnosis for multiple faults of chiller using ELM-KNN model enhanced by multi-label learning and specific feature combinations

Abstract

Existing fault detection and diagnosis methods for chillers are usually very effective on single faults diagnosis, but perform poorly while diagnosing multiple faults, and these fault diagnosis models depend on the range of training data. Such models tend to fail when the system encounters a fault that was not included in training data. This paper presents a novel multiple faults diagnosis method for chiller based on multi-label learning and specific feature combinations enhanced ELM-KNN. Firstly, Extreme Learning Machine (ELM) is used for mapping of features to improve the accuracy of K-Nearest Neighbor (KNN) algorithm for multiple faults. Then, through multi-label learning, a model is established for each single fault, and by merging these models a final multiple faults diagnosis model is formed. Finally, by examining the input features, the specific combinations of input features for each single fault are determined and applied to the actual chiller. Experimental results show that the model proposed in this paper has the highest accuracy among the compared methods. The model proposed has achieved better diagnosis performance for the multiple faults in the absence of several multiple faults training data. The specific feature combinations are beneficial to improve the diagnosis performance of the model. The model can have excellent diagnostic performance for both normal operation and multiple faults by only using a small amount of single fault data in training. In addition, the model proposed can also complete the diagnosis of single faults in the absence of some single faults training data.