An automatic and efficient fault diagnosis strategy for air conditioning units by combining attention mechanisms

The air conditioning system is a very important energy-consuming device in the field of construction. Timely troubleshooting of air conditioning chillers is an important aspect of reducing building energy consumption. Aiming at the problems of many model parameters, large amount of calculation and strong dependence of diagnostic performance on input features in data-driven diagnostic methods, this paper proposes an efficient fault diagnosis strategy combining RepVGG network and SENet attention mechanism. The multi-parameter feature signal is converted into a two-dimensional matrix to obtain multiple feature images. Based on the SENet attention mechanism, the multi-channel weights of the images are extracted, and the weighted feature image is input into the RepVGG network for feature depth mining and classification. Therefore, the method proposed in this paper greatly reduces the calculation amount of parameters, improves the representativeness of signal features to faults and the efficiency of model training. The effectiveness of the method is validated using the ASHRAE RP-1043 chiller unit dataset. The results show that the accuracy of this method reaches 96.55%, which is significantly better than the comparison methods PSO-ELM(96.4%), DT(95.1%), RepVGG(91.6%), ELM(87.6%) and BP(80.8%).