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.