Torabi Narges, Gunay Huseyin Burak, O'Brien William, Moromisato Ricardo
Department of Civil and Environmental Engineering, Carleton University, Ottawa, ON, Canada.
CopperTree Analytics, Vancouver, BC, Canada.
Build Serv Eng Res Technol. 2022 Sep;43(5):605-625. doi: 10.1177/01436244221097827. Epub 2022 Jun 6.
A holistic fault detection and diagnostics (FDD) method should explicitly consider the dependencies between faults at the system- and zone-level to isolate the root cause. A system-level fault can trigger false alarms at the zone-level, while concealing the presence of a zone-level fault. However, most FDD methods have focused on a single component/equipment without considering the importance of the interactions between zone- and system-level devices. This paper proposes a holistic hierarchical framework for FDD, combining the process of detection and diagnosis of controls hardware and sequencing logic faults affecting the actuators at the system- and zone-level. The proposed framework follows a holistic sequential procedure to diagnose faults and suppress false alarms in this order: hard faults in air handling units (AHUs), hard faults in variable air volume (VAV) zones, sequencing logic faults in AHUs, and sequencing logic faults in VAV zones. The detection of faults is performed by visualizing the discrepancies between the expected and measured operational behaviour of AHUs and VAV boxes. Examples demonstrating the framework are provided with data from 10 different VAV AHU systems. This paper provides a sequential hierarchical FDD framework to address two main issues in VAV AHU systems: detectability and significance. Regarding detectability, the framework prioritizes hard faults over sequencing logic faults to avoid false positives and false negatives; about significance, system-level faults are prioritized over zone-level faults to triage high-impact faults in the system. The detection of faults is performed via visualizing the biases from the expected behaviour of AHU and VAV characteristics to provide an envisioning interpretation for the experts in facilities management in commercial buildings to find the root cause of the fault and fix them on-site.
一种整体故障检测与诊断（FDD）方法应明确考虑系统级和区域级故障之间的依赖性，以隔离根本原因。系统级故障可能会在区域级触发误报，同时掩盖区域级故障的存在。然而，大多数FDD方法都集中在单个组件/设备上，而没有考虑区域级和系统级设备之间相互作用的重要性。本文提出了一种用于FDD的整体分层框架，该框架结合了对影响系统级和区域级执行器的控制硬件和顺序逻辑故障的检测与诊断过程。所提出的框架遵循一个整体顺序过程，按此顺序诊断故障并抑制误报：空气处理机组（AHU）中的硬故障、变风量（VAV）区域中的硬故障、AHU中的顺序逻辑故障以及VAV区域中的顺序逻辑故障。通过可视化AHU和VAV箱的预期运行行为与实测运行行为之间的差异来进行故障检测。利用来自10个不同VAV AHU系统的数据提供了演示该框架的示例。本文提供了一个顺序分层FDD框架，以解决VAV AHU系统中的两个主要问题：可检测性和重要性。关于可检测性，该框架将硬故障优先于顺序逻辑故障，以避免误报和漏报；关于重要性，系统级故障优先于区域级故障，以便对系统中的高影响故障进行分类。通过可视化与AHU和VAV特性的预期行为的偏差来进行故障检测，为商业建筑设施管理中的专家提供直观的解释，以便找到故障的根本原因并在现场进行修复。
