Abstract
HVAC system need be quickly diagnosed the location and location of the fault and find the cause of the failure, and reduce the failure to improve the comfort of users, improve the energy efficiency of buildings. Firstly, features of HVAC system failures were introduced in this paper. Failure correlation and transitivity and hierarchy of faults and parameter alignment were analyzed. On this basis, step size selection of depth neural network model was constructed by statistical-based machine learning. Adaptive depth neural network algorithm was built by weight updating formula. Finally, by an application case, multivariable adaptive control of HVAC System can strengthen the fault prediction and monitoring, can reduce the occurrence of fault, and prolong the life of equipment.
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