Faults in air handling units (AHUs) in commercial buildings often waste energy and/or cause discomfort. Intermediate AHU sensors are dedicated merely for diagnostics and are essential to isolate faults rather than for controls; However, in many AHUs, such sensors are either missing, misplaced, or uncalibrated. This paper investigates three model-based methods to make up for the lack of reliable intermediate sensor data. To this end, trend data from five AHUs in five different buildings in Ottawa, Canada, are extracted for 2019. The accuracy of three different model forms is compared - artificial neural network (ANN), genetic algorithm (GA), and multiple linear regression (MLR) - are applied to model the supply air temperature of the AHUs. The behaviour of AHU heating and cooling coil valves and outside air dampers with and without the intermediate sensors is studied in this paper. Although installing temperature sensors before and after the heating and cooling coils facilitates detection of the faults occurring in AHUs, the authors showed the generated inverse models can act as virtual temperature sensors to estimate the intermediate measurements and isolate hard faults in AHU outside air dampers in addition to heating/cooling coil valves.

商业建筑中空气处理装置 (AHU) 的故障通常会浪费能源和/或引起不适。中间 AHU 传感器仅专用于诊断，对于隔离故障而非控制至关重要；但是，在许多 AHU 中，此类传感器要么丢失、放错位置或未校准。本文研究了三种基于模型的方法，以弥补可靠的中间传感器数据的不足。为此，提取了加拿大渥太华五座不同建筑物的五台 AHU 的 2019 年趋势数据。 比较了三种不同模型形式的准确率——人工神经网络 (ANN)、遗传算法 (GA) 和多元线性回归(MLR) - 用于模拟 AHU 的送风温度。本文研究了带和不带中间传感器的 AHU 加热和冷却盘管阀和外部空气阻尼器的行为。虽然在加热和冷却盘管前后安装温度传感器有助于检测 AHU 中发生的故障，但作者表明，生成的逆模型可以作为虚拟温度传感器来估计中间测量值并隔离 AHU 外部空气阻尼器中的硬故障加热/冷却盘管阀。