暖通空调
故障检测与隔离
计算机科学
支持向量机
断层(地质)
算法
可扩展性
实时计算
理论(学习稳定性)
人工智能
工程类
机器学习
空调
机械工程
数据库
地质学
地震学
执行机构
作者
Hui Zhu,Wen Yang,Shihong Li,Aiping Pang
出处
期刊:Buildings
[MDPI AG]
日期:2022-02-20
卷期号:12 (2): 246-246
被引量:7
标识
DOI:10.3390/buildings12020246
摘要
Fault detection in heating, ventilation and air-conditioning (HVAC) systems can effectively prevent equipment damage and system energy loss, and enhance the stability and reliability of system operation. However, existing fault detection strategies have not realized high effectiveness, mainly due to the time-delay characteristics of HVAC system faults and the lack of system-fault operation data. Therefore, aiming at the time delay of system faults and the lack of actual system-fault operation data, this paper proposes a fault detection method that combines a system simulation model and an intelligent detection algorithm. The method first uses the Modelica modeling language to build a scalable simulation model of the system to obtain fault data that are not easily accessible in practice. The long short-term memory-support vector data description (LSTM-SVDD) algorithm is then applied to detect faults in real time by dynamically adjusting the fault residuals according to the absolute difference between the predicted and actual values. The experimental results show that the LSTM-SVDD method improves the average detection accuracy by 9.675% and 9.85% over the classical LSTM network and the extreme gradient boosting (XGBoost) method, respectively, under different fault levels.
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