Fault Diagnosis of Central Air-Conditioning Chiller Based on AdaBoost-PSO-SVM

To address the challenge that faults in central air-conditioning chillers are difficult to detect in time and the types of faults are easily misidentified, an ensemble fault diagnosis model based on AdaBoost-PSO-SVM is proposed in this paper. Firstly, the Support Vector Machine (SVM) model, which is widely applied to nonlinear problems, is chosen as the foundational model for fault diagnosis. Subsequently, the Particle Swarm Optimization algorithm (PSO) is used to find the best of the penalty factor C and the kernel parameter g of the SVM within a given range, resulting in the creation of the base learner PSO-SVM of the ensemble model. Finally, the AdaBoost-PSO-SVM fault diagnosis model integrates multiple PSO-SVM learners by updating the sample weights and assigning different weights to the base learners. The effectiveness of the proposed AdaBoost-PSO-SVM model is evaluated using experimental data from ASHRAE RP-1043. Results demonstrate its ability to accurately identify chiller faults, even including low severity levels, with an average diagnosis accuracy rate of 90.69%.