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首页> 外文期刊>Advanced engineering informatics >Fault detection for non-condensing boilers using simulated building automation system sensor data
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Fault detection for non-condensing boilers using simulated building automation system sensor data

机译：使用模拟楼宇自动化系统传感器数据的非冷凝锅炉的故障检测

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摘要

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.
机译：在调试后，建筑性性能显着降低，导致能耗增加和相关的温室气体排放。使用现有传感器网络和IOT设备的故障检测和诊断（FDD）协议具有可能通过不断识别系统的降级和重新调整控制策略来最小化此废物，以适应真实的建筑性能。由于其对温室气体排放的重大贡献，燃气锅炉系统的建筑加热的性能至关重要。对锅炉性能研究的综述已被用于开发一组常见的故障和降级性能条件，这些故障已集成到Matlab / Simcape仿真器中。这导致标记的数据集具有大约10,000个模拟的14个非冷凝锅炉的稳态性能。收集的数据用于使用K-Collect邻居，决策树，随机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提供高预测精度，始终超过95％，并且由于低分类精度，多种锅炉的泛化是不可能的。

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