A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Ensemble 1-D CNN diagnosis model for VRF system refrigerant charge faults under heating condition
Highlights 1-D CNN is an innovative approach utilized for fault diagnosis in VRF system. 1-D CNN models achieves good accuracy and is capable of fault diagnosis. Enhanced the method to an ensemble model by using parallel 1-D CNNs. The ensemble model’s performance is improved and archived state-of-the-art accuracy.
Abstract Variable refrigerant flow (VRF) systems are widely-adopted air conditioning systems. When system faults occur in VRF systems, the efficiency of VRF system will drop drastically. This paper presents a single 1-D CNN model and an ensemble model with parallel 1-D CNNs for diagnosing VRF system refrigerant charge faults under heating condition. From the cleaned experiment data of a commercial VRF system, 15 features are selected as the input for the proposed model with ReliefF algorithm. After training, the diagnosis accuracy of the single 1-D CNN model and ensemble 1-D CNN models is evaluated and compared with that of BPNN model and DT model. The result shows that both single 1-D CNN and ensemble 1-D CNN model can diagnose VRF system refrigerant charge fault effectively. The fault detection is also achieved in proposed models. The average diagnosis accuracy of 9-level refrigerant charge faults of the ensemble 1-D CNN model is up to 97.4%, surpassing that of BPNN model, SVM model, DT mode and DBN model. 1-D CNN based model is utilized for VRF system fault diagnosis for the first time, which lays a foundation for the expansion of the related researches.
Ensemble 1-D CNN diagnosis model for VRF system refrigerant charge faults under heating condition
Highlights 1-D CNN is an innovative approach utilized for fault diagnosis in VRF system. 1-D CNN models achieves good accuracy and is capable of fault diagnosis. Enhanced the method to an ensemble model by using parallel 1-D CNNs. The ensemble model’s performance is improved and archived state-of-the-art accuracy.
Abstract Variable refrigerant flow (VRF) systems are widely-adopted air conditioning systems. When system faults occur in VRF systems, the efficiency of VRF system will drop drastically. This paper presents a single 1-D CNN model and an ensemble model with parallel 1-D CNNs for diagnosing VRF system refrigerant charge faults under heating condition. From the cleaned experiment data of a commercial VRF system, 15 features are selected as the input for the proposed model with ReliefF algorithm. After training, the diagnosis accuracy of the single 1-D CNN model and ensemble 1-D CNN models is evaluated and compared with that of BPNN model and DT model. The result shows that both single 1-D CNN and ensemble 1-D CNN model can diagnose VRF system refrigerant charge fault effectively. The fault detection is also achieved in proposed models. The average diagnosis accuracy of 9-level refrigerant charge faults of the ensemble 1-D CNN model is up to 97.4%, surpassing that of BPNN model, SVM model, DT mode and DBN model. 1-D CNN based model is utilized for VRF system fault diagnosis for the first time, which lays a foundation for the expansion of the related researches.
Ensemble 1-D CNN diagnosis model for VRF system refrigerant charge faults under heating condition
Cheng, Hengda (author) / Chen, Huanxin (author) / Li, Zhengfei (author) / Cheng, Xiangdong (author)
Energy and Buildings ; 224
2020-06-25
Article (Journal)
Electronic Resource
English
| Taylor & Francis Verlag | 2017
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