Domain adaptation deep learning and its T-S diagnosis networks for the cross-control and cross-condition scenarios in data center HVAC systems

Deep learning has the inspiring potential for artificial intelligence (AI) control in data centers of smart city. However, deep learning-based method is limited when the application scenarios are cross-control or cross-conditions. This paper proposes a novel domain adaptation deep learning model and Teacher-Student (T-S) networks to diagnose the faults of Heating, Ventilation and Air Conditioning (HVAC) systems under cross-control modes and cross-conditions. Firstly, domain adaptation deep belief network (DBN) model is presented to enhance the cross-control generalization performance of extreme learning machine (ELM) and K-nearest neighbor (KNN) diagnosis model. Secondly, the Teacher-Student networks are constructed. With the weights learned from the DBN model in Teacher Network, the cross-conditions capacity is improved for DELM model in the Student Network. Moreover, both the single and multiple faults are experimented and validated, which includes refrigerant leakage, reduced condenser water flow and reduced evaporator flow. The experimental results show that the domain adaptation deep learning model and its T-S diagnosis networks can reach the satisfied generalization performance. For the cross-control tests, the Accuracy of proposed method can reach 91%, 89% and 80% for fault-free, REW and RCW faults. For the cross-temperature and cross-load tests, the proposed method also obtained the high Accuracy of 98% and 88%, respectively. •Domain adaptation deep learning model is presented to diagnose the faults of data centers.•Teacher-Student network is constructed and teacher network is used to enhance the capacity of diagnosis in student network.•The diagnosis model is effective for the untrained cross-control modes in HVAC systems.•The generalization performance is improved by solving the cross-temperature and cross-load conditions.