Fault Diagnosis Of Sensor And Actuator Faults In Multi-Zone Hvac Systems

Globally, the buildings sector accounts for 30% of the energy consumption and more than 55% of the electricity demand. Specifically, the Heating, Ventilation, and Air Conditioning (HVAC) system is the most extensively operated component and it is responsible alone for 40% of the final building energy usage. HVAC systems are used to provide healthy and comfortable indoor conditions, and their main objective is to maintain the thermal comfort of occupants with minimum energy usage. HVAC systems include a considerable number of sensors, controlled actuators, and other components. They are at risk of malfunctioning or failure resulting in reduced efficiency, potential interference with the execution of supervision schemes, and equipment deterioration. Hence, Fault Diagnosis (FD) of HVAC systems is essential to improve their reliability, efficiency, and performance, and to provide preventive maintenance. In this thesis work, two neural network-based methods are proposed for sensor and actuator faults in a 3-zone HVAC system. For sensor faults, an online semi-supervised sensor data validation and fault diagnosis method using an Auto-Associative Neural Network (AANN) is developed. The method is based on the implementation of Nonlinear Principal Component Analysis (NPCA) using a Back-Propagation Neural Network (BPNN) and it demonstrates notable capability in sensor fault and inaccuracy correction, measurement noise reduction, missing sensor data replacement, and in both single and multiple sensor faults diagnosis. In addition, a novel on-line supervised multi-model approach for actuator fault diagnosis using Convolutional Neural Networks (CNNs) is developed for single actuator faults. It is based a data transformation in which the 1-dimensional data are configured into a 2-dimensional representation without the use of advanced signal processing techniques. The CNN-based actuator fault diagnosis approach demonstrates improved performance capability compared with the commonly used Machine Learning-based algorithms (i.e., Support Vector Machine and standard Neural Networks). The presented schemes are compared with other commonly used HVAC fault diagnosis methods for benchmarking and they are proven to be superior, effective, accurate, and reliable. The proposed approaches can be applied to large-scale buildings with additional zones

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

Self-Adaptive Model-Based Control for VAV Systems

In North America one of the main users of primary energy are buildings, where HVAC equipment operation is the largest consumer of total energy by end use. This has triggered the need to develop better active strategies and building technologies for the enhancement of HVAC equipment performance. Great examples of solutions that large commercial and institutional buildings adopted, were the widespread use of Building Automation Systems (BAS), and approaches like Variable Air Volume (VAV) systems for ventilation, which allow for better part load regulation, reduction of energy consumption and building operation costs, without compromising occupant comfort or safety. But despite all these improvements, most BAS still rely on conventional control methods like rule based on-off control paired with Proportional Integral Derivative (PID) loops, which are single input single output (SISO) models that are not suitable for the complexities of the multivariable requirements of building systems. These outdated strategies have been estimated to annually waste up to 30% of building’s energy. To mitigate these issues the research community has strongly endorsed the use of more advanced and proven effective control methods such as Model-Based Control (MBC), in which abundant work has been done for the supervisory level control like optimal start/stop, setpoint reset scheduling, etc. However, little attention has been given to local level control where PID control remains the chief workhorse of HVAC systems. Mainly because of the difficulties of creating models, as well as the lack of research regarding the implementation of mechanisms required for continuous calibration (also known as adaptability) of model parameters as they start to drift away from their initial values due to system changes or deterioration, which challenges the reliability of any MBC approach. For such reasons the present body of work was conceived to design a practical methodology for a self-adaptive MBC and field data driven approach to improve VAV systems energy efficiency, based on the Total Air Volume (TAV) control method by modifying the shortcomings of its modeling, adaptability and control strategy procedures. Using a regular VAV system inside a high-rise institutional building as an experimental testbed for the proof of concept of this methodology. The results of the test demonstrated that the self adaptive field calibrated TAV method can match and exceed the capabilities of PID control, by improving response time, offset, and above all energy efficiency, were an average 56% of energy consumption was achieved in contrast to the conventional duct static pressure PID control

Fault “Auto-correction” for HVAC Systems: A Preliminary Study

A Fault Detection and Diagnostics (FDD) tool is a type of energy management and information system that is designed to continuously identify the presence of faults and efficiency improvement opportunities through a 1-way interface to the building automation system and application of automated analytics. It is estimated that 5-30% energy saving can be achieved by employing FDD tools and implementing efficiency measures based on FDD findings. Although the potential of this technology is high, actual savings are only realized when an operator takes an action to fix the problem. There is a subset of faults that can be potentially addressed automatically by the system, without operator intervention. Automating this fault correction can significantly increase the savings generated by FDD tools and reduce the reliance on human intervention. This paper presents preliminary efforts towards delivering automated fault correction. It describes nine fault auto-correction algorithms for heating ventilation and air conditioning (HVAC) systems that were developed to automatically correct faults or improve controls operation. It also presents preliminary testing results of one auto-correction algorithm (improve air handling unit static pressure setpoint reset) in a commercial building, located in Berkeley, California, US. The auto-correction algorithms and implementation frameworks of this initial study provide a foundation for future auto-correction algorithm development and novel schemes for improving building operation performance and reliability

Identification of Key Parameters Affecting Energy Consumption of an Air Handling Unit

Air handling unit system (AHU) is one of the series of mechanical systems that regulate and circulate the air through the ducts inside the buildings. In a commercial setting, air handling units accounted for more than 50% of the total energy cost of the building in 2013. The energy efficiency of the system depends on multiple factors. The set points of discharge air temperature and supply air static pressure are important ones. ASHRAE Standard 90.1-2010 requires multi-zone HVAC systems to implement supply air temperature reset. Energy is wasted if the set points are set constant. However, the waste has never been quantified. The objectives of this study were to (1) develop and validate a mathematical model, which can be used to predict the system performance in response to various controls, specifically the set-point control strategies, and associated energy consumption, and (2) to recommend measures for optimizing the AHU performance by optimizing the setting schedules. In this research, a gray box model was established to evaluate the performance of an AHU. Individual components were modeled using energy and mass balance governing equations that represent the inherent physical processes and interactions with other components. Engineering Equation Solver (EES) was selected for system simulation due to its capabilities of finding the solutions of a large set of complicated equations. The model was validated using two sets of sub hourly real time data. The model performance was evaluated employing Mean Absolute Percentage Error (MAPE) and Root Mean Square Deviation (RMSD). The model was used to create the baseline of energy consumption with constant set points and predict the energy savings using two different reset schedules. The AHU, which serves the entire basement of a campus building on IUPUI campus, was used for this study. It normally has constant set points of discharge air temperature and supply air static pressure. The AHU was monitored using sensors. The data were filtered and transferred to a Building Automation system. Operation information and design specifications of the AHU were collected. Two reset schedules were investigated to determine the better control strategy to minimize energy consumption of the AHU. Discharge air temperature was reset based on return air temperature (RA-T) with a linear reset schedule from March 4 to March 7. Static pressure of the supply air was reset based on the widest open Variable Air Volume (VAV) box damper position from March 20 to March 23. Additionally, uncertainty propagation method was used to identify the dominant parameters affecting the energy consumption. Results indicated that 17% energy savings was achieved using discharge air temperature reset while the energy consumption reduced by 7% using static pressure reset. The results also indicated that outside air temperature, supply airflow rate and return air temperature were the key parameters that impact the overall energy consumption

A Probabilistic Framework To Diagnose Faults in Air Handling Units.

Air handling unit (AHU) is one of the most extensively used equipment in large commercial buildings. This device is typically customized and lacks quality system integration, which can result in, hardwire failures and controller errors. Air handling unit Performance Assessment Rules (APAR) is a fault detection tool that uses a set of expert rules derived from mass and energy balances to detect faults in air handling units. Although APAR has many advantages over other methods, for example, no training data required and easy to implement commercially, most of the time it is unable to provide the diagnosis of the faults. There is no established way to have the correct diagnosis for rule based fault detection system. In this study, we developed a new way to detect and diagnose faults in AHU through combining APAR rules and Bayesian Belief Network. BBN is used as a decision support tool for rule-based expert system. BBN is highly capable to prioritize faults when multiple rules are satisfied simultaneously. The proposed model tested with real time measured data of a campus building at University of Texas at San Antonio (UTSA)

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

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