基于双图注意力网络和区间预测的智能家电故障检测  

Fault diagnosis of intelligent household appliances based on dual graph attention network and interval prediction

作　　者：尚喆 曹财广 唐善玄 刘明 刘普 SHANG Zhe;CAO Caiguang;TANG Shanxuan;LIU Ming;LIU Pu(Guangdong Midea Refrigeration Equipment Limited Company,Foshan 528000)

机构地区：[1]广东美的制冷设备有限公司,广东佛山528000

出　　处：《家电科技》2025年第1期48-52,共5页Journal of Appliance Science & Technology

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。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.

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 

分 类 号：TM925[电气工程—电力电子与电力传动] TP391[自动化与计算机技术—计算机应用技术]