Explicit Model for Chiller Fault Diagnosis Based on Multi-objective Regression with Different Weights

Based on the cross-entropy loss function and stochastic gradient descent algorithm, a weight regression fault diagnosis model was established for seven common faults in a chiller. The weighted regression model was slightly more complex than the pure linear regression model; however, the fault diagnosis performance was clearly better, and the minimum performance was improved by 40.50% under different feature sets. When comparing the effects of feature sets from various sources in this model and introducing a new feature set, the accuracy reached 89.83%. Notably, the diagnostic accuracy for local faults exceeded 98%. The explicit model for chiller fault diagnosis is summarized, and by examining the parameter weights in the visual diagnosis model, it was determined that the oil supply pressure, oil supply temperature, and degree of subcooling were the most crucial parameters for diagnosing three types of system faults. Conversely, the refrigerant pressure in the condenser, temperature difference in the condenser, and water flow parameters between the evaporator and condenser were identified as the most important parameters for diagnosing four local faults.