Abstract
Accurate fault diagnosis of heating, ventilation, and air conditioning (HVAC) systems is of significant importance for maintaining normal operation, reducing energy consumption, and minimizing maintenance costs. However, in practical applications, it is challenging to obtain sufficient fault data for HVAC systems, leading to imbalanced data, where the number of fault samples is much smaller than that of normal samples. Moreover, most existing HVAC system fault diagnosis methods heavily rely on balanced training sets to achieve high fault diagnosis accuracy. Therefore, to address this issue, a composite neural network fault diagnosis model is proposed, which combines SMOTETomek, multi-scale one-dimensional convolutional neural networks (M1DCNN), and support vector machine (SVM). This method first utilizes SMOTETomek to augment the minority class samples in the imbalanced dataset, achieving a balanced number of faulty and normal data. Then, it employs the M1DCNN model to extract feature information from the augmented dataset. Finally, it replaces the original Softmax classifier with an SVM classifier for classification, thus enhancing the fault diagnosis accuracy. Using the SMOTETomek-M1DCNN-SVM method, we conducted fault diagnosis validation on both the ASHRAE RP-1043 dataset and experimental dataset with an imbalance ratio of 1:10. The results demonstrate the superiority of this approach, providing a novel and promising solution for intelligent building management, with accuracy and F1 scores of 98.45% and 100% for the RP-1043 dataset and experimental dataset, respectively.
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Abbreviations
- 1DCNN:
-
one-dimensional convolutional neural network
- ACGAN:
-
auxiliary classifier generative adversarial network
- BP-SMOTE:
-
bi-phasic synthetic minority oversampling technique
- CLCGAN:
-
cycle-consistent and latent-consistent generative adversarial network
- CNN:
-
convolutional neural network
- CWGAN:
-
conditional Wasserstein generative adversarial network
- DCE:
-
dynamic cross-entropy
- FDD:
-
fault detection and diagnosis
- GP:
-
gradient penalty
- GRU:
-
gated recurrent units
- HVAC:
-
heating, ventilation and air conditioning
- KNN:
-
k-nearest neighbors
- LSTM:
-
long short-term memory network
- M1DCNN:
-
multi-scale one-dimensional convolutional neural network
- RF:
-
random forest
- SMOTE:
-
synthetic minority oversampling technique
- SP-CNN:
-
self-production of data and deep convolutional neural network
- SSMOTE:
-
stable synthetic minority oversampling technique
- STCN:
-
self-attention mechanism-based temporal convolutional network
- SVM:
-
support vector machine
- VAE:
-
variational autoencoder
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Acknowledgements
The authors of this paper acknowledge the support from the National Natural Science Foundation of China (No. 51975191) and the Funds for Science and Technology Creative Talents of Hubei, China (No. 2023DJC048). This work was supported by the Xiangyang Hubei University of Technology Industrial Research Institute Funding Program (No. XYYJ2022B01).
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Rouhui Wu, Yizhu Ren, Mengying Tan and Lei Nie. The first draft of the manuscript was written by Rouhui Wu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Wu, R., Ren, Y., Tan, M. et al. Fault diagnosis of HVAC system with imbalanced data using multi-scale convolution composite neural network. Build. Simul. 17, 371–386 (2024). https://doi.org/10.1007/s12273-023-1086-1
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DOI: https://doi.org/10.1007/s12273-023-1086-1