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Fault detection for non-condensing boilers using simulated building automation system sensor data
Advanced Engineering Informatics ( IF 8.8 ) Pub Date : 2020-10-07 , DOI: 10.1016/j.aei.2020.101176
Rony Shohet , Mohamed S. Kandil , Yidan Wang , J.J. McArthur

Building performance has been shown to degrade significantly after commissioning, resulting in increased energy consumption and associated greenhouse gas emissions. Fault Detection and Diagnosis (FDD) protocols using existing sensor networks and IoT devices have the potential to minimize this waste by continually identifying system degradation and re-tuning control strategies to adapt to real building performance. Due to its significant contribution to greenhouse gas emissions, the performance of gas boiler systems for building heating is critical. A review of boiler performance studies has been used to develop a set of common faults and degraded performance conditions, which have been integrated into a MATLAB/Simscape emulator. This resulted in a labeled dataset with approximately 10,000 simulations of steady-state performance for each of 14 non-condensing boilers. The collected data is used for training and testing fault classification using K-nearest neighbour, Decision tree, Random Forest, and Support Vector Machines. The results show that the Decision Tree, Random Forest, and Support Vector Machines method provide high prediction accuracy, consistently exceeding 95%, and generalization across multiple boilers is not possible due to low classification accuracy.



中文翻译:

使用模拟的楼宇自动化系统传感器数据检测非冷凝锅炉的故障

事实表明,调试后建筑性能会显着下降,从而导致能耗增加和相关的温室气体排放。使用现有的传感器网络和物联网设备的故障检测与诊断(FDD)协议有潜力通过不断识别系统性能下降并重新调整控制策略以适应实际建筑物的性能来最大程度地减少浪费。由于其对温室气体排放的重大贡献,用于建筑物供暖的燃气锅炉系统的性能至关重要。对锅炉性能研究的回顾已用于开发一组常见故障和性能下降的条件,这些故障已集成到MATLAB / Simscape仿真器中。这样得出的标记数据集大约有10个,对14个非冷凝锅炉中的每个锅炉进行000次稳态性能模拟。所收集的数据用于使用K最近邻,决策树,随机森林和支持向量机进行训练和测试故障分类。结果表明,决策树,随机森林和支持向量机方法可提供较高的预测精度,始终超过95%,并且由于分类精度低,无法在多个锅炉之间进行通用化。

更新日期:2020-10-08
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