A holistic sequential fault detection and diagnostics framework for multiple zone variable air volume air handling unit systems

Abstract

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. Practical application: 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.

Application of Variable Theory Domain Fuzzy Control Algorithm for Room Temperature Control

“…The length 𝑙 of the machine room studied in this paper is 5m, the width 𝑤 is 5m, the height ℎ is 3m, the number of air exchanges 𝑁 is 30 times/h, the temperature of the air supply 𝑡 𝑛 is 25℃, the indoor temperature 𝑡 𝑠 is 15℃, the volume of the air supply 𝐿 is 2250m³/h, and the air supply adopts the side air supply method. According to the literature [6], the calculation methods of each parameter are shown in Table 1.…”

A comparative investigation between rule- and inverse model-based fault detection and diagnostics for HVAC control systems

“…Therefore, the number of daily fan state changes is inspected to detect deviation from the expected mode of operation. A mode of operation sequencing logic fault is detected if the fan experiences more than or less than two daily operating mode changes for more than 30% of the weekdays [11].…”

Fault Detection and Diagnostics in Single-Duct Multiple-Zone Variable Air Volume Air Handling Unit Systems

“…Putting in place an adequate number of sensors in the correct locations, which results in proper readings necessary for indoor climate control, facilitates identifying actuators or programming errors in the operation phase. For instance, locating temperature sensors before and after the coils in AHUs and after the reheat coil in VAV boxes enables the operating staff to detect hardware anomalies related to heating/cooling and reheat coil valves [15]. Moreover, devoting a small number of thermostats to control multiple thermal zones (i.e., having several adjacent spaces share a thermostat, especially when the zones have different orientations and/or window-to-wall ratios) causes discomfort and energy waste [107].…”