Novel chiller fault diagnosis using deep neural network (DNN) with simulated annealing (SA)

Effective chiller fault diagnosis is of great importance for maintaining a better service and energy efficiency. Deep learning proficiently solves some problems challenging Artificial Intelligence and becomes one of the excellent candidates for fault diagnosis recently. This study proposes a novel fault diagnosis strategy for a chiller, which merges simulated annealing (SA) into a deep neural network (DNN) to obtain effective, efficient, and stable performance. The proposed SA-DNN strategy is carefully compared with DNN and back-propagation (BP) network. The results show that SA-DNN enhances the diagnostic accuracy, shortens the running time, and greatly improves the model stability. The optimal network structure has 2 hidden layers (HL) with each layer 64 nodes, and the overall diagnostic accuracy for seven typical faults attains 99.30%. The nodes in the first HL are proved to be dominant over those in the second or behind because the mapping of the second can hardly make corrections if that of the first is deformed already. The global faults are hard to be diagnosed due to the global effect, but the proposed strategy achieves satisfactory results with the highest individual accuracy reaching 99.79% for excess oil and the lowest 97.52% for refrigerant leakage. The features used for diagnosis have an influence on the accuracy of the proposed method.