Center for Energy Informatics (CFEI), Maersk Mc-Kinney Moller Institute (MMMI), University of Southern Denmark (SDU), 5230 Odense, Denmark.
Center for Supervision, Security and Automatic Control (CS²AC), Polytechnic University of Catalonia (UPC), 08034 Barcelona, Spain.
Sensors (Basel). 2018 Nov 14;18(11):3931. doi: 10.3390/s18113931.
Buildings represent a significant portion of global energy consumption. Ventilation units are complex components, often customized for the specific building, responsible for a large part of energy consumption. Their faults impact buildings' energy efficiency and occupancy comfort. In order to ensure their correct operation, proper fault detection and diagnostics methods must be applied. Hardware redundancy, an effective approach to detect faults, leads to increased costs and space requirements. We propose exploiting physical relations inside ventilation units to create virtual sensors from other sensors' readings, introducing redundancy in the system. We use two different measures to detect when a virtual sensor deviates from the physical one: coefficient of determination for linear models, and acceptable range. We tested our method on a real building at the University of Southern Denmark, developing three virtual sensors: temperature, airflow, and fan speed. We employed linear regression models, statistical models, and non-linear regression models. All models detected an anomalous strong oscillation in the temperature sensors. Readings fell outside the acceptable range and the coefficient of determination dropped. Our method showed promising results by introducing redundancy in the system, which can benefit several applications, such as fault detection and diagnostics and fault-tolerant control. Future work will be necessary to discover thresholds and set up automatic fault detection and diagnostics.
建筑物在全球能源消耗中占很大比例。通风设备是复杂的组件，通常根据特定建筑物进行定制，负责消耗大量能源。其故障会影响建筑物的能源效率和居住舒适度。为了确保其正常运行，必须应用适当的故障检测和诊断方法。硬件冗余是一种有效的故障检测方法，但会导致成本和空间需求增加。我们提出利用通风设备内部的物理关系，从其他传感器的读数中创建虚拟传感器，从而在系统中引入冗余。我们使用两种不同的方法来检测虚拟传感器何时偏离物理传感器：线性模型的确定系数和可接受范围。我们在丹麦南部大学的一栋实际建筑上测试了我们的方法，开发了三个虚拟传感器：温度、气流和风扇速度。我们使用了线性回归模型、统计模型和非线性回归模型。所有模型都检测到温度传感器中异常强烈的振荡。读数超出了可接受范围，确定系数下降。我们的方法通过在系统中引入冗余取得了有希望的结果，这可以使许多应用受益，例如故障检测和诊断以及容错控制。未来需要进一步研究来发现阈值并建立自动故障检测和诊断。
