Fault diagnosis of centrifugal chillers based on particle swarm optimization-least squares support vector machine

Aiming at the low accuracy rate of traditional fault diagnosis in refrigeration system, presents the least squares support vector machine (LSSVM) algorithm for the fault diagnosis field of refrigeration system. Based on the LSSVM model, obtains the PSO-LSSVM model by combining the particle swarm optimization (PSO) algorithm, the LSSVM8 model by the feature selection method, and the PSO-LSSVM8 model by the combination method. Analyses and compares the diagnostic performance of the four models. The results show that the PSO-LSSVM model and LSSVM8 model can improve the performance of the fault diagnosis of the refrigeration system based on the LSSVM model in different aspects, especially for the failure of the refrigerant leakage/filling, and the accuracy rate increases by 1.04%, 1.24% respectively, and the PSO-LSSVM8 model has better diagnostic performance than the single optimization method, it can overcome the blindness of artificial parameters selection, with great advantages in the comprehensive optimization and convergence speed, which has good feasibility in the fault diagnosis of the refrigeration system.