A Quantitative Model-Based Fault Detection &amp; Diagnostics (FDD) System for Zone-Level Inefficiencies

Berquist, Justin

doi:10.22215/etd/2017-12135

Cited by 3 publications

(7 citation statements)

References 31 publications

(184 reference statements)

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“…The gray-box model was trained using two weeks of data from the physical model, and it predicted an indoor air temperatures close to the physical model under fault-free conditions. Gray-box models also have been used as a basis in FD or FDD schemes for providing the reference model needed for residual comparison [42][43][44][45][46]. et al [12] were not included due to similar definitions and not the focus of these reviews.…”

Section: Methods Categorizations For Fddmentioning

confidence: 99%

Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

et al. 2022

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This review aims to provide an up-to-date, comprehensive, and systematic summary of fault detection and diagnosis (FDD) in building systems. The latter was performed through a defined systematic methodology with the final selection of 221 studies. This review provides insights into four topics: (1) glossary framework of the FDD processes; (2) a classification scheme using energy system terminologies as the starting point; (3) the data, code, and performance evaluation metrics used in the reviewed literature; and (4) future research outlooks. FDD is a known and well-developed field in the aerospace, energy, and automotive sector. Nevertheless, this study found that FDD for building systems is still at an early stage worldwide. This was evident through the ongoing development of algorithms for detecting and diagnosing faults in building systems and the inconsistent use of the terminologies and definitions. In addition, there was an apparent lack of data statements in the reviewed articles, which compromised the reproducibility, and thus the practical development in this field. Furthermore, as data drove the research activity, the found dataset repositories and open code are also presented in this review. Finally, all data and documentation presented in this review are open and available in a GitHub repository.

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Section: Methods Categorizations For Fddmentioning

confidence: 99%

Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

et al. 2022

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“…The deviation between measured and predicted model data sets is an indication of fault existence. 15 The grey-and black-box models are data-driven models that employ data inherent in BAS gathered by sensors, actuators, and meters to predict the system's characteristics. The discrepancy between the predicted and measured model parameters of a system is an indication of fault occurrence.…”

Section: Background and Previous Work On Fdd For Vav Ahu System Actua...mentioning

confidence: 99%

“…The deviation between measured and predicted model data sets is an indication of fault existence. 15 …”

Section: Background and Previous Work On Fdd For Vav Ahu System Actua...mentioning

confidence: 99%

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

Torabi

Gunay

O’Brien

et al. 2022

Building Services Engineering Research and Technology

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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.

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“…Cotrufo and Zmeureanu [28] developed models to predict the outdoor airflow rate based on the outdoor, mixed, and return air temperatures in AHUs when the heat recovery units are used in heating mode. Cotrufo et al [29] developed three different methods based on physics-based and data-driven models to expand the application of virtual sensors in AHUs. The proposed methods can be used for the virtual measurement of outdoor air temperature (OAT) when it is not recorded from a physical sensor and for the virtual re-calibration of humidistat when a physical sensor is found to be faulty.…”

Section: Virtual Sensors For Hvac Systemsmentioning

confidence: 99%

“…A number of system quantities are measured and then compared to the predicted values. The deviation between measured and predicted model data sets is an indication of fault existence [29].…”

Section: Background and Previous Work On Fdd For Vav Ahu System Actua...mentioning

confidence: 99%

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

Torabi

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Variable air volume (VAV) air handling unit (AHU) systems are the most common heating, ventilation, and air conditioning (HVAC) configuration in North America's commercial buildings. They account for a large fraction of sensors and actuators and are prone to faults due to their control systems' complexity. This research fills the gap in VAV AHU fault detection and diagnostics (FDD) through four parts. First, inverse model-based virtual sensors are developed to detect actuator faults in AHUs. Three algorithms are explored to compensate for the lack of reliable intermediate sensor data for five AHUs. Results indicate that although generated models can act as virtual sensors to identify hard faults, they cannot isolate faults' root causes. To address this uncertainty, the second part introduces a holistic sequential hierarchical FDD framework considering the significance of zone-to-system hard and sequencing logic fault interactions in AHUs and VAVs. The proposed framework follows this fault-checking order: AHU hard faults, VAV hard faults, AHU sequencing logic faults, and VAV sequencing logic faults. Faults are detected by visualizing the discrepancies between the expected and measured operational behaviour. The third part focuses on VAV AHU control systems (sensors, actuators, and sequencing logic), studying human errors during design, installation, and operation. Human errors are identified through a literature review and interviews with control specialists. The most common errors are classified, and interview examples are listed for different building life cycle phases. Since a commonly reported error by interviewees is thermostat misplacement, resulting in negative impacts on AHU supply air temperature (SAT) setpoint, the last step of this research investigates the robustness of sequences of operation to such errors. Fault tolerance of the most vulnerable sequences: SAT setpoint, duct static pressure (DSP) ix

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A Quantitative Model-Based Fault Detection & Diagnostics (FDD) System for Zone-Level Inefficiencies

Cited by 3 publications

References 31 publications

Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

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

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

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