Fault Impact Analysis Research Articles

Fault prioritisation for Air Handling units using fault modelling and actual fault occurrence data

Fault impact analysis is an important step in developing Fault Detection and Diagnosis (FDD) tools for Heating, Ventilation and Air-Conditioning (HVAC) systems. It provides an insight into how HVAC systems react in the presence of operational faults and helps to prioritise which faults to focus on. The research in the existing literature has mostly focused on modelling several faults occurring in various HVAC systems and components to evaluate the effect of the fault on energy performance and the predicted thermal comfort of occupants. However, the real frequency of fault occurrence, an important factor which affects the overall impact a fault has on the system’s performance, has barely been addressed in the reviewed literature. This paper addresses this gap by considering real fault occurrence frequency data in addition to the effect of the faults on the energy performance of the building to assess their total energy impact. A real office building in the Netherlands was modelled using DesignBuilder and its energy performance was simulated using EnergyPlus. Air Handling Unit (AHU) faults were introduced using the native fault object and Energy Management System (EMS) features available in EnergyPlus. The fault occurrence data was extracted from AHU work orders using text mining. The fault modelling and text mining results were combined to get the total energy impact of the fault and, subsequently, the faults were prioritised using the Pareto principle. The research identified that fan failure, Heat Recovery Wheel (HRW) failure, fan stuck at 50% and Heating Coil Valve (HCV) stuck at 0% are the priority faults for winter and fan failure is the priority fault for summer.

A new integrated framework to fault detection and diagnosis of air handling unit: Emphasizing the impact of symptoms

This study proposes a comprehensive framework for Fault Detection and Diagnosis (FDD) in Air Handling Units (AHU), emphasizing the impact of symptoms associated with various faults. The aim is to address an important limitation in FDD model development where detection is based simply on fault intensity without considering symptom impact or subjective severity criteria across faults. Instead, a new approach is introduced that factors in fault impact, utilizing impact analysis to enhance FDD accuracy and efficiency. The methodology involves three interconnected components: First, a fault impact analysis categorizes 18 fault types into stages per intensity and assesses resulting symptom impacts. Second, symptom-based intensity thresholds are established to categorize fault severity into three levels based on symptom severity. Finally, the goal is to integrate these findings into the FDD process for optimized fault detection and diagnosis. The study successfully categorized 18 faults into three severity levels, with thresholds identified for each. Furthermore, Tree-based models demonstrated effective performance when conducting FDD based on these levels. The approach provides a clear framework through three linked processes. This research combines comprehensive impact analysis with sophisticated classification methods, presenting a more defined, systematic, and efficient FDD approach. This novel methodology substantially advances fault detection and diagnosis, especially for building energy systems.

Fault impact analysis of ventilation systems in residential buildings: A simulation-based case study in Denmark

The spread of intelligent buildings and advanced HVAC systems controls emphasizes the necessity of evaluating the impact of malfunctions caused by errors in the design, installation, or maintenance of these systems. In this work, the effect of the most diffused faults in ventilation systems is studied for an air handling unit installed in a Danish residential apartment. For this purpose, a dynamic model was built in Modelica, representing the building and the ventilation system. Different faults with varying intensities were implemented in the model. The impacts were evaluated by comparing the results of the obtained “with-fault” models with the “fault-free” model used as a benchmark. The comparison involved thermal and electrical energy use, thermal comfort and indoor air quality. Results showed that, for the studied system, the increase in thermal energy use was significant for the offset of the sensor controlling the bypass (up to + 27%), the bypass damper leakage and stuck (up to + 90%) and ducts’ thermal losses (up to + 48%). In the latter case, an increase in thermal discomfort was observed (+7% of hours with operative temperature below 18.5 °C).

Estimating energy savings from HVAC controls fault correction through inverse greybox model-based virtual metering

Fault impact analysis is an important phase of fault detection and diagnosis (FDD), enabling prioritization of the faults detected and allocation of resources to procure services and materials. However, many existing fault impact analysis workflows rely on building performance simulation (BPS) models with high engineering cost, limiting their use in FDD software. To this end, this paper introduces an inverse greybox model-based fault impact analysis method using building automation system (BAS) trend data from variable air volume (VAV) air handling unit (AHU) systems to quantify the heating and cooling energy impact of control system faults. It trains inverse models of a VAV AHU system by employing a sequential parameter estimation algorithm, formulates virtual meters quantifying the sensible heat transfer rate from AHU heating and cooling coils and the perimeter heating devices, and employs the virtual meters to estimate heating and cooling energy use of an existing VAV AHU system and the same system after fault correction. The method was used with a synthetic BAS dataset generated by using an EnergyPlus model of a 26-zone VAV AHU system in Ottawa, Canada. The results indicate that the virtual meters could estimate the monthly energy use for heating and cooling at a coefficient of variation of the root mean squared error (CV(RMSE)) of 4–6%. The method was demonstrated with measured BAS data from a 72-zone VAV AHU system, revealing an energy savings potential of 43% for heating and 22% for cooling through fault correction.

Fault Identification and Fault Impact Analysis of The Vapor Compression Refrigeration Systems in Buildings: A System Reliability Approach

The Vapor Compression Refrigeration System (VCRS) is one of the most critical systems in buildings typically used in Heating, Ventilation, and Air Conditioning (HVAC) systems in residential and industrial sections. Therefore, identifying their faults and evaluating their reliability are essential to ensure the required operations and performance in these systems. Various components and subsystems are included in the VCRS, which need to be analyzed for system reliability. This research’s objective is conducting a comprehensive system reliability analysis on the VCRS by focusing on fault identification and determining the fault impacts on these systems. A typical VCRS in an office building is selected for this research regarding this objective. The corresponding reliability data, including the probability distributions and parameters, are collected from references to perform the reliability evaluation on the components and subsystems of the VCRS. Then the optimum distribution parameters have been obtained in the next step as the main findings. Additionally, by applying optimization techniques, efforts have been taken to maximize the system’s reliability. Finally, a comparison between the primary and the optimized systems (with new distribution parameters) has been performed over their lifetime to illustrate the system’s improvement percentage.

Faults in Modular Multilevel Cascade Converters—Part I: Reliability, Failure Mechanisms, and Fault Impact Analysis

Modular Multilevel Cascade Converters (MMCCs) are considered a promising power electronics topology in industry. Their scalability allows to reach (ultra/very) high voltage levels with low harmonic content and high efficiency and makes MMCCs an ideal solution for high-power applications; such as electrical drives, solid-state transformers and high-voltage direct-current (HVDC) transmission systems. However, the high levels of thermal, electrical and mechanical stress on the power electronics devices and the large number of components (e.g. capacitors or semiconductors) make MMCCs prone to faults reducing its reliability. In this first part of the paper, a comprehensive overview of reliability on MMCCs, failure mechanisms and fault impact analysis in MMCCs, including failure rates and fault modes is presented. Also a set of tables which collect all information to easily detect and identify faults in MMCCs is presented.

A holistic fault impact analysis of the high-performance sequences of operation for HVAC systems: Modelica-based case study in a medium-office building

ASHRAE Guideline 36: High-performance sequences of operation (SOO) for Heating, Ventilation, and Air-conditioning (HVAC) Systems has been demonstrated to save 17%-30% energy under ideal simulation environments. However, HVAC systems are susceptible to various types of faults in a real building operation. There are no existing studies that pertain to a comprehensive fault impact analysis of the high-performance control sequences suggested by ASHRAE Guideline 36 for HVAC systems. How these sequences handle and adapt to the various types of faults is still largely unknown. In this context, a comprehensive fault impact analysis and robustness assessment of the high-performance control sequences is conducted. A Modelica-based medium office virtual testbed is developed following the air-side and the plant-side SOO. A total of 359 fault scenarios in three different seasonal operating conditions (cooling, shoulder, and heating seasons) are injected into the baseline model. The evaluated key performance indexes (KPIs) include the operational cost, source energy, site energy, control loop quality, thermal comfort, ventilation, and power system metrics. The faults of the most negative impact are identified for different seasonal operating conditions over all the KPIs. The results also show that high-performance control sequences are well adapted for the vast majority (∼90%) of all the fault scenarios over all the KPIs in this study.

An innovative fault impact analysis framework for enhancing building operations

The primary objective of this paper is to rank building faults based on their impacts on the building energy penalty and occupant thermal comfort penalty considering multiple faults and fault occurrence rates. A fault impact analysis framework is created by incorporating the fault model library with the whole building energy performance simulation (e.g., EnergyPlus used in this study). The fault occurrence rate is introduced as a “meta” parameter in the simulation. This framework involves three essential aspects of conducting a fault impact analysis: fault constructing, fault simulation, and fault impact analysis.A parametric sensitivity analysis was used to determine and rank the criticality of the faults considering the fault concurrence frequency, by using the deep-learning based response surface model (i.e., the multi-layer perceptron regression). The proposed fault analysis framework with ranking was tested and demonstrated for the DOE's prototype medium-sized office in four different climate zones (i.e., Atlanta, Chicago, Miami, and San Francisco) with 12,000 EnergyPlus fault simulations. A total of 129 fault modes from 41 groups of fault models were simulated for the medium-sized office case.The results demonstrate the proposed framework is robust and scalable for the fault impact analysis. The top critical fault for the medium-sized office is the fault of HVAC-Left-ON for the packaged rooftop unit, regarding the site energy, source energy, and HVAC energy. Excluding the fault of HVAC-Left-ON, the top critical faults vary significantly among the four climate zones.

A critical review of fault modeling of HVAC systems in buildings

Buildings consumed about 40% of primary energy and 70% of the electricity in the U.S. It is well known that most buildings lose a portion of their desired and designed energy efficiency in the years after they are commissioned or recommissioned. Majority of the Heating, Ventilation, and Air-Conditioning (HVAC) systems have multiple faults residing in the systems causing either energy, thermal comfort, or indoor air quality penalties. There are hundreds of fault detection and diagnostics (FDD) algorithms available, but there is lacking a common framework to assess and validate those FDD algorithms. Fault modeling is one of the key components of such a framework. In general, fault modeling has two purposes: testing and assessment of FDD algorithms, and fault impacts analysis in terms of building energy consumption and occupants’ thermal comfort. It is expected that fault ranking from the fault impact analysis can facilitate building facility managers to make decisions. This paper provides a detailed review of current state-of-the-art for the fault modeling of HVAC systems in buildings, including fault model, fault occurrence probability, and fault simulation platform. Fault simulations considering fault occurrence probability can generate realistic faulty data across a variety of faulty operating conditions, and facilitate testing and assessment of different FDD algorithms. They can also help the fault impact study. Three research gaps are identified through this critical literature review: (1) The number of available fault models of HVAC systems is still limited. A fault model library could be developed to cover all common HVAC faults for both traditional and non-traditional HVAC systems. (2) It is imperative to include the fault occurrence probability in fault simulations for a realistic fault impacts analysis such as fault ranking. (3) Fault simulation platforms need further improvements to better facilitate the fault impact analysis.

EFT Fault Impact Analysis on Performance of Critical Tasks in Intravehicular Networks

Electrical fast transients (EFT) represent an important problem to intravehicular networks. Concerning this problem, this paper reports a study about the impact of EFT to the performance of communication protocols used in intravehicular networks. A method of fast transient injection has been applied to an experimental CAN network with the objective of verifying the EFT impact on jitter and how it influences the communication process. An experiment with a simulated control system distributed over a real CAN network was carried out. For this experiment, an active suspension system was chosen as a case study and a sequence of EFT injections was performed. As a result, it was verified that injected EFT bursts increase jitter of safety-critical control messages and may effectively influence and produce faults during the communication process.

Fault Impact Analysis and Improvements for a Large-scale Offshore Wind Farm Connected to Grid by Long HVAC Cables

A large-scale offshore wind power system with a capacity 200MW will be erected in the Peng-Hu islands, located about 60 km off the west coast of Taiwan. The system will be connected to Taiwan’s main grid by long high-voltage alternating-current (HVAC) submarine cables. The system impact of three phase short circuit faults are analyzed based on PSS/E simulations of type-B, C and D turbines. The system framework, associated parameters, and the features of different types of wind turbines are described. Responses of voltages, frequencies and powers of each turbine type due to fault disturbance are analyzed. In addition, the voltage and frequency variations of the Taiwan and Peng-Hu grids are compared in terms of different turbine types. Finally, improvements based on static VAr compensator (SVC) and static synchronous compensator (STATCOM) technologies are presented and their fault impact mitigation capacities are analyzed. Analysis results show that fault impact is significantly dependent on turbine type and the presented compensators provide useful impact mitigation.