A hybrid data-driven simultaneous fault diagnosis model for air handling units

• Main Authors: Wu, Binjie, Cai, Wenjian, Chen, Haoran, Zhang, Xin
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Electrical and electronic engineering; Energy Conservation; Classifier Chains
• Online Access: https://hdl.handle.net/10356/160417
• Institution: Nanyang Technological University

Simultaneous faults are situations where two or more faults occur at the same time, which are difficult to be diagnosed by simple and stand-alone standard machine learning methods as a multi-label problem. Simultaneous faults for HVAC systems are not given enough attention under the challenges of insufficient sensors, coupled faults, and sophisticated mathematical models. A novel simultaneous fault diagnosis model based on a hybrid method of classifier chains integrated with random forest (CC-RF) is proposed in this study. On-site experiments involving six single fault cases and seven simultaneous fault cases for an air handling unit (AHU) system are conducted to verify this model. The results demonstrate a satisfactory performance with the test accuracy of 99.50% and F1 score of 99.66% for the fault diagnosis model. The model is proven to be neither underfitting nor overfitting and can be scalable with a reasonable training time. Through online analysis, the proposed method demonstrates a good competence of diagnosing not only single faults but also simultaneous fault. The CC-RF method has a better performance compared with classifier chains with logistic regression and support vector machine. Besides, the proposed method of classifier chains outperforms binary relevance due to the benefitting of label relevance.