The energy consumed by HVAC systems accounts for 50％～60％of total building energy consumption worldwide,and various failures of chillers reduce the efficiency of HVAC systems by 15％～30％,resulting in a considerable amount of energy waste.Therefore,accurate detection of faults in chiller systems can effectively mitigate energy waste and extend the life cycle of the equipment.A fault diagnosis model based on multiblock and self-attention mechanism time convolution network(Multiblock Self-attention Temporal Convolutional Networks,MB-SATCN)architecture is proposed for the problem of difficult extraction of fault sample data feature information with high coupling and time correlation in chiller unit fault diagnosis.The model divides the overall variables into multiple sub-blocks based on the physical relationship between chiller sensors and system structure,and uses the time-convolutional network architecture to mine the feature information of chiller operation data in the sub-blocks.And by introducing the self-attention mechanism to enhance the weight of key features on the fault diagnosis results,the local features output from each sub-block are again weighted and fused using the self-attention mechanism to construct a global feature representation,and the final input global features into the fully connected layer for classification using the softmax function.The simulation results show that the introduction of MB method and SA mechanism effectively improves the feature extraction ability of highly coupled chiller unit fault samples and moreover improves the fault diagnosis performance of the model.Compared with the fault diagnosis performance of three deep learning methods dealing with time series,MB-SACNN,LSTM,and GRU,the MB-SATCN method performs the average accuracy of fault diagnosis under SL1 level of minor faults is up to 98.00％,the average recall rate is up to 97.90％,the average accuracy rate is up to 97.91％,and the Fl-score is up to 98.00％,which verifies the sensitivity and stability of the method.
维普
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