Due to the development of sensor networks and information technology, data-driven fault detection and diagnosis (FDD) has been made possible with real-time multiple sensor measurements. However, due to inevitable sensor errors or communication failures, the raw data are usually incomplete with corrupted values, lost values, or undetected missing values. In practice, the incomplete data are usually dealt with by directly excluding incomplete measurements and abnormal spikes. In addition, some preprocessing methods, which naively impute data though averaging or smoothing, have also been widely applied. In this article, we address the building FDD problem with incomplete data by proposing a new approach, the adjacent information recovery (AIR) filter. The AIR filter is utilized to deal with the FDD for a typical air handling unit (AHU) system with incomplete data based on the ASHRAE Research Project 1312. Experimental results show that the proposed method improves FDD performance by recovering missing sensor measurements and outperforms the state-of-the-art methods. Note to Practitioners —Fault detection and diagnosis (FDD) for smart buildings by addressing the fact that FDD systems are of great importance for saving energy and improving occupancy comfort levels and building safety levels. Existing FDD methods are mainly based on the assumption that sensor data are complete and reliable, which are rarely true in real practice. To solve the building FDD problem with incomplete data, in this article, the adjacent information recovery (AIR) filter is proposed to recover the missing data before applying FDD methods. The AIR filter takes the time series adjacency information into consideration via hidden Markov model (HMM) and includes the channel adjacency information with the collaborating filtering technique.

中文翻译：

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在建筑物HVAC系统的故障检测和诊断中处理不完整的传感器测量

由于传感器网络和信息技术的发展，数据驱动的故障检测和诊断（FDD）通过实时的多传感器测量成为可能。但是，由于不可避免的传感器错误或通信故障，原始数据通常不完整，其中包含损坏的值，丢失的值或未检测到的丢失值。实际上，通常通过直接排除不完整的测量值和异常峰值来处理不完整的数据。另外，一些通过平均或平滑天真地插补数据的预处理方法也已被广泛应用。在本文中，我们通过提出一种新方法，即相邻信息恢复（AIR）过滤器，来解决数据不完整的建筑FDD问题。执业者注意 通过解决以下事实，即智能建筑的故障检测和诊断（FDD），FDD系统对于节省能源，提高居住舒适度和建筑物安全性水平至关重要。现有的FDD方法主要基于传感器数据完整且可靠的假设，而在实际实践中很少如此。为了解决数据不完整的建筑FDD问题，本文提出了一种相邻信息恢复（AIR）过滤器，用于在应用FDD方法之前恢复丢失的数据。AIR过滤器通过隐马尔可夫模型（HMM）考虑了时间序列邻接信息，并通过协作过滤技术将信道邻接信息包括在内。