A novel temporal convolutional network via enhancing feature extraction for the chiller fault diagnosis

Abstract

The chiller fault diagnosis is important to keep chiller’s normal operation and realize the energy conservation of the heating, ventilation and air conditioning (HVAC) system. However, the conventional data-driven fault diagnosis models not only ignore the dynamic coupling characteristics among process variables, but also have complex structures and large calculations. Therefore, to tackle these problems, this paper proposes a novel feature-enhanced temporal convolutional network (FETCN) method for the chiller fault diagnosis. The proposed method discusses a feature enhancement technique (FET) to extract enhanced features to capture the chiller’s dynamic coupling characteristics and variables’ independent changes. In the FET, the encoder-decoder network (EDN) is constructed to extract residual features that reflect the independent change of each variable, and the statistical pooling method is applied to calculate statistical features revealing dynamic coupling information among different variables. Then the two types of feature matrices are merged to obtain the enhanced features. Afterwards, to reduce the model’s complexity and improve the fault diagnosis performance, the new temporal convolutional network (TCN) classifier is established based on the TCN model and the softmax layer. It can efficiently analyze the enhanced features to diagnose the fault pattern. The experimental results on the ASHRAE Research Project 1043 (RP-1043) dataset show that the enhanced features calculated by the FET are more conducive to solving the chiller’s dynamic coupling problem. Moreover, the FETCN method has a higher fault diagnosis rate in different fault severity levels. And it needs fewer samples and shorter time to complete the model training.