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.

中文翻译：

---

处理建筑 HVAC 系统故障检测和诊断中不完整的传感器测量


由于传感器网络和信息技术的发展，通过实时多传感器测量使数据驱动的故障检测和诊断（FDD）成为可能。然而，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，存在损坏的值、丢失的值或未检测到的缺失值。在实践中，通常通过直接排除不完整的测量和异常尖峰来处理不完整的数据。此外，一些通过平均或平滑来简单地估算数据的预处理方法也得到了广泛的应用。在本文中，我们通过提出一种新方法——相邻信息恢复（AIR）过滤器来解决不完整数据的构建 FDD 问题。基于 ASHRAE 研究项目 1312，利用 AIR 过滤器来处理具有不完整数据的典型空气处理单元 (AHU) 系统的 FDD。实验结果表明，该方法通过恢复丢失的传感器测量值来提高 FDD 性能，并且优于状态最先进的方法。