基于双图注意力网络和区间预测的智能家电故障检测

doi:10.19784/j.cnki.issn1672-0172.2025.01.007

摘要/Abstract

摘要： 尽管目前机器学习和人工智能技术已经应用到智能家电的故障检测当中,但仍面临传感器之间数据耦合、数据动态变化以及故障数据难以获取等方面的挑战。针对上述问题,提出了一种基于双图注意力网络和区间预测的智能家电故障检测方法。通过同时建模传感器之间的关系以及数据在时间序列上的动态规律,不需要故障数据训练网络,就可以通过无监督学习的方式进行故障检测。在真实家用空调运行数据集上,该方法对正常数据和故障数据分类的准确率可达92.3%,并且在噪声干扰下仍具有较高的准确性,为智能家电故障检测提供了一种有效、准确的方法,对于推动智能家电技术的发展具有重要意义。

关键词: 故障检测, 注意力网络, 区间预测, 智能家电, 空调, 传感器

Abstract: Although machine learning and artificial intelligence technologies have been applied to fault diagnosis in smart home appliances, they still face challenges such as data coupling between sensors, dynamic changes in data, and difficulty in obtaining fault data. To address the above issues, on intelligent famlt detection method for home appliances based on dual graph attention network and interval prediction is proposed. By simultaneously modeling the relationships between sensors and the dynamic patterns of data in time series, fault diagnosis can be performed through unsupervised learning, which does not require fault data for network training. This method achieved an accuracy of 92.3% in classifying normal and fault data on a real-world household air conditioning operation dataset. It maintained high accuracy even under noise interference, providing an effective and precise technical approach for enhancing fault diagnosis of smart home appliances. It is of great significance for promoting the development of smart home technology.

Key words: Fault diagnosis, Graph attention network, Interval prediction, Intelligent household appliances, Air conditioning, Sensor

中图分类号:

TM925
TP391

尚喆, 曹财广, 唐善玄, 刘明, 刘普. 基于双图注意力网络和区间预测的智能家电故障检测[J]. 家电科技, 2025, 0(1): 48-52.

SHANG Zhe, CAO Caiguang, TANG Shanxuan, LIU Ming, LIU Pu. Fault diagnosis of intelligent household appliances based on dual graph attention network and interval prediction[J]. Journal of Appliance Science & Technology, 2025, 0(1): 48-52.

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参考文献

[1] 徐鸿, 石文鹏. 智能家电舒适、健康发展之路[J]. 家电科技, 2020(01): 24-26.
[2] Baudier P, Ammi C, Deboeuf-Rouchon M.Smart home: Highly-educated students' acceptance[J]. Technological Forecasting and Social Change, 2020, 153: 119355.
[3] 王少华, 樊其锋, 张健, 等. 基于随机森林和支持向量机混合模型的空调故障检测[J]. 家电科技, 2022(zk): 774-777.
[4] Sadat Mirnaghi M, Haghighat F.Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: A comprehensive review[J]. Energy and Buildings, 2020, 229: 110492.
[5] Chen Z, O’Neill Z, Wen J, et al. A review of data-driven fault detection and diagnostics for building HVAC systems[J]. Applied Energy, 2023, 339: 121030.
[6] Li T, Zhao Y, Zhang C, et al.A knowledge-guided and data-driven method for building HVAC systems fault diagnosis[J]. Building and Environment, 2021, 198: 107850.
[7] Han H, Gu B, Kang J, et al.Study on a hybrid SVM model for chiller FDD applications[J]. Applied Thermal Engineering, 2011, 31(04): 582-592.
[8] Gajjar S, Palazoglu A.A data-driven multidimensional visualization technique for process fault detection and diagnosis[J]. Chemometrics and Intelligent Laboratory Systems, 2016, 154: 122-136.
[9] Cheng F, Cai W, Zhang X, et al.Fault detection and diagnosis for Air Handling Unit based on multiscale convolutional neural networks[J]. Energy and Buildings, 2021, 236: 110795.
[10] Chen J, Zhang L, Li Y, et al.A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems[J]. Renewable and Sustainable Energy Reviews, 2022, 161: 112395.
[11] Guo Y, Tan Z, Chen H, et al.Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving[J]. Applied Energy, 2018, 225: 732-745.
[12] Fan C, Liu X, Xue P, et al.Statistical characterization of semi-supervised neural networks for fault detection and diagnosis of air handling units[J]. Energy and Buildings, 2021, 234: 110733.
[13] Zhao H, Wang Y, Duan J, et al.Multivariate time-series anomaly detection via graph attention network[C]//2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020: 841-850.
[14] Zhong F, Kaiser Calautit J, Wu Y.Fault data seasonal imbalance and insufficiency impacts on data-driven heating, ventilation and air-conditioning fault detection and diagnosis performances for energy-efficient building operations[J]. Energy, 2023, 282: 128180.
[15] Du Z, Chen K, Chen S, et al.Deep learning GAN-based data generation and fault diagnosis in the data center HVAC system[J]. Energy and Buildings, 2023, 289: 113072.
[16] Veličković P, Cucurull G, Casanova A, et al.Graph attention networks[J]. arXiv preprint arXiv, 2017, 1710: 10903.
[17] 唐善玄, 刘猛, 王少华, 等. 自适应多任务的主动智能控制边缘计算集成框架[J]. 家电科技, 2023(06): 22-26.