A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Fault Diagnosis of Centrifugal Chiller Based on Extreme Gradient Boosting
Centrifugal chillers have been widely used in medium- and large-scale air conditioning projects. However, equipment running with faults will result in additional energy consumption. Meanwhile, it is difficult to diagnose the minor faults of the equipment. Therefore, the Extreme Gradient Boost (XGBoost) algorithm was used to solve the above problem in this article. The ASHRAE RP-1043 dataset was employed for research, utilizing the feature splitting principle of XGBoost to reduce the data dimension to 23 dimensions. Subsequently, the five important parameters of the XGBoost algorithm were optimized using Multi-swarm Cooperative Particle Swarm Optimization (MSPSO). The minor fault diagnosis model, MSPSO-XGBoost, was established. The results show that the ability of the proposed MSPSO-XGBoost model to diagnose eight different states is uniform, and the diagnostic accuracy of the model reaches 99.67%. The accuracy rate is significantly improved compared to that of the support vector machine (SVM) and back propagation neural network (BPNN) diagnostic models.
Fault Diagnosis of Centrifugal Chiller Based on Extreme Gradient Boosting
Centrifugal chillers have been widely used in medium- and large-scale air conditioning projects. However, equipment running with faults will result in additional energy consumption. Meanwhile, it is difficult to diagnose the minor faults of the equipment. Therefore, the Extreme Gradient Boost (XGBoost) algorithm was used to solve the above problem in this article. The ASHRAE RP-1043 dataset was employed for research, utilizing the feature splitting principle of XGBoost to reduce the data dimension to 23 dimensions. Subsequently, the five important parameters of the XGBoost algorithm were optimized using Multi-swarm Cooperative Particle Swarm Optimization (MSPSO). The minor fault diagnosis model, MSPSO-XGBoost, was established. The results show that the ability of the proposed MSPSO-XGBoost model to diagnose eight different states is uniform, and the diagnostic accuracy of the model reaches 99.67%. The accuracy rate is significantly improved compared to that of the support vector machine (SVM) and back propagation neural network (BPNN) diagnostic models.
Fault Diagnosis of Centrifugal Chiller Based on Extreme Gradient Boosting
Yaxiang Liu (author) / Tao Liang (author) / Mengxin Zhang (author) / Nijie Jing (author) / Yudong Xia (author) / Qiang Ding (author)
2024
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
A Novel Strategy for the Fault Detection and Diagnosis of Centrifugal Chiller Systems
| Taylor & Francis Verlag | 2009