Download or read book Detection and Diagnosis of Faults and Energy Monitoring of Heating Ventilating and Air Conditioning Systems with Least intrusive Power Analysis written by Dong Luo and published by . This book was released on 2001 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Faults indicate degradation or sudden failure of equipment in a system. Widely existing in heating, ventilating, and air conditioning (HVAC) systems, faults always lead to inefficient energy consumption, undesirable indoor air conditions, and even damage to the mechanical components. Continuous monitoring of the system and analysis of faults and their major effects are therefore crucial to identifying the faults at the early stage and making decisions for repair. This requires the method of fault detection and diagnosis (FDD) not only to be sensitive and reliable but also to cause minimal interruption to the system's operation at low cost. However, based on additional sensors for the specific information of each component or black-box modeling, current work of fault detection and diagnosis introduces too much interruption to the system's normal operation associated with sensor installation at unacceptable cost or requires a long time of parameter training. To solve these problems, this thesis first defines and makes major innovations to a change detection algorithm, the generalized likelihood ratio (GLR), to extract useful information from the system's total power data. Then in order to improve the quality of detection and simplify the training of the power models, appropriate multi-rate sampling and filtering techniques are designed for the change detector. From the detected variations in the total power, the performance at the system's level is examined and general problems associated with unstable control and on/off cycling can be identified. With the information that are basic to common HVAC systems, power functions are established for the major components, which help to obtain more reliable detection and more accurate estimation of the systems' energy consumption. In addition, a method for the development of expert rules based on semantic analysis is set up for fault diagnosis . Power models at both system and component levels developed in this thesis have been successfully applied to tests in real buildings and provide a systematic way for FDD in HVAC systems at low cost and with minimal interruption to systems' operation.
Download or read book Applied Change of Mean Detection Techniques for HVAC Fault Detection and Diagnosis and Power Monitoring written by Roger Owen Hill and published by . This book was released on 1995 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: A signal processing technique, the detection of abrupt changes in a time-series signal, is implemented with two different applications related to energy use in buildings. The first application is a signal pre-processor for an advanced electric power monitor, the Nonintrusive Load Monitor (NILM), which is being developed by researchers at the Massachusetts Institute of Technology. A variant form of the generalized likelihood ratio (GLR) change-detection algorithm is determined to be appropriate for detecting power transients which are used by the NILM to uniquely identify the start-up of electric end-uses. An extension of the GLR change-detection technique is used with a second application, fault detection and diagnosis in building heating ventilation and air-conditioning (HVAC) systems. The method developed here analyzes the transient behavior of HVAC sensors to define conditions of correct operation of a computer simulated constant air volume HVAC sub-system. Simulated faults in a water-to-air heat exchanger (coil fouling and a leaky valve) are introduced into the computer model. GLR-based analysis of the transients of the faulted HVAC system is used to uniquely define the faulty state. The fault detection method's sensitivity to input parameters is explored and further avenues for research with this method are suggested.
Download or read book Fault Detection in Heating Ventilation and Air conditioning Systems written by Robert W. Lanoue and published by . This book was released on 1991 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Automated Diagnostics and Analytics for Buildings written by Barney L. Capehart and published by CRC Press. This book was released on 2021-01-07 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the widespread availability of high-speed, high-capacity microprocessors and microcomputers with high-speed communication ability, and sophisticated energy analytics software, the technology to support deployment of automated diagnostics is now available, and the opportunity to apply automated fault detection and diagnostics to every system and piece of equipment in a facility, as well as for whole buildings, is imminent. The purpose of this book is to share information with a broad audience on the state of automated fault detection and diagnostics for buildings applications, the benefits of those applications, emerging diagnostic technology, examples of field deployments, the relationship to codes and standards, automated diagnostic tools presently available, guidance on how to use automated diagnostics, and related issues.
Download or read book Non Intrusive Load Monitoring of HVAC Components Using Signal Unmixing written by and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Heating, Ventilating and Air Conditioning units (HVAC) are a major electrical energy consumer in buildings. Monitoring of the operation and energy consumption of HVAC would increase the awareness of building owners and maintenance service providers of the condition and quality of performance of these units, enabling conditioned-based maintenance which would help achieving higher energy efficiency. In this paper, a novel non-intrusive load monitoring method based on group constrained non-negative matrix factorization is proposed for monitoring the different components of HVAC unit by only measuring the whole building aggregated power signal. At the first level of this hierarchical approach, power consumption of the building is decomposed to energy consumption of the HVAC unit and all the other electrical devices operating in the building such as lighting and plug loads. Then, the estimated power signal of the HVAC is used for estimating the power consumption profile of the HVAC major electrical loads such as compressors, condenser fans and indoor blower. Experiments conducted on real data collected from a building testbed maintained at the Oak Ridge National Laboratory (ORNL) demonstrate high accuracy on the disaggregation task.
Download or read book Optimal Control and Fault Detection in Heating Ventilating and Air conditioning Systems written by Florian F. L. Pape and published by . This book was released on 1989 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Minimally Intrusive Strategies for Fault Detection and Energy Monitoring written by Robert Williams Cox and published by . This book was released on 2006 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: (Cont.) In particular, two such models are described. The first of these is intended to be applied in systems in which an electromechanical actuator cycles its operation according to the value of some other variable, such as a pressure or a temperature. Examples include compressed air and vacuum systems. The other model is used to diagnose the impending failure of the mechanical coupling through which a motor drives an inertial load such as a pump impeller. This thesis also describes the development of a minimally intrusive airflow monitoring system that uses ozone as a tracer gas. This system fits easily into the "multi-use" framework because it relies on a network of distributed ozone generators and detectors whose operation is coordinated via power line communications. Finally, this thesis also presents and demonstrates a method for detecting the operation of various electrical loads using transient changes in the measured line voltage. This technique makes it possible to use "plug-in" sensors to determine the operating schedule of each of the various loads in a home or commercial facility. All of the techniques and methods described here are demonstrated experimentally.
Download or read book System level Monitoring and Diagnosis of Building HVAC System written by and published by . This book was released on 2005 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Heating, ventilation, and air conditioning (HVAC) is an indoor environmental technology that is extensively instrumented for large-scale buildings. Among all subsystems of buildings, the HVAC system dominates the energy consumption and accounts for 57% of the energy used in U.S. commercial and residential buildings. Unfortunately, the HVAC system may fail to meet the performance expectations due to various faults, including not only complete hardware failures, but also non-optimal operations. These faults waste more than 20% of the energy HVAC consumes. Therefore, it is of great potential to develop automatic, quick-responding, intelligent, and reliable monitoring and diagnosis tools to ensure the normal operations of HVAC and increase the energy efficiency of buildings. To achieve these goals, increasing attentions have been attracted to two research areas, i.e., models that monitor the indoor thermal environment, and fault detection and diagnosis (FDD) tools that capture abnormal HVAC performance. Despite contributions of the existing works, there are still many challenges in these two areas. For the thermal models, the major concerns lie in 1) most of the models are determined empirically, 2) optimal structures and orders of the models are often determined through simulations, 3) the predictions of the models degrade quickly over longer time intervals, and 4) a lack of studies to incorporate architectural parameters and control variables into the models. For the FDD, we face the challenges of 1) the inherent complexity, coupled hardware and software, and increasing scale of HVAC significantly complicate the nature of faults, 2) faults occur at different levels with various degrees of impacts on upper-level HVAC units, 3) practical FDD tools at the system-level are scarce, and 4) the computational efficiency and calibration onerousness of the simulation-based FDD is a concern. In this thesis, we address these challenges by innovating a system-level monitoring and diagnosis tool for HVAC. For the monitoring, we study and establish a parametric modeling approach to present indoor air temperature and thermal comfort. The resulting models take advantages of both analytical and numerical modeling techniques. These models have a two-stage regression structure, and explicitly include both architectural parameters and control variables as its predictors. As a result, they allow parametric studies of influence of the building envelope on indoor thermal behavior, serve as an efficient foundation for intelligent HVAC control design, and help optimize the design of and the material selection for office buildings. For the diagnosis, we innovate and develop a system-level FDD architecture for detecting faults across different levels of the HVAC system. Specifically, this architecture monitors and detects faulty HVAC units in a top-down manner. By monitoring HVAC units at higher level, instead of lower level components, the proposed FDD strategy reduces the computational effort in real-time monitoring of the HVAC system, obtains a system-level view of the HVAC operation, and provides a way to integrate the existing methods for component fault detection when needed. Based on extensive data collected from an office building on the campus of the University of California at Merced, numerical validations of the models, and examples of detected faults demonstrate the effectiveness of the proposed monitoring and diagnosis tool.
Download or read book Barriers to Broader Utilization of Fault Detection Technologies for Improving Residential HVAC Equipment Efficiency written by and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Faults in residential heating, ventilating, and air conditioning (HVAC) equipment may occur due to poor installation practices or develop over time, and these faults can negatively impact system efficiency, thermal comfort, and equipment lifespan. Automated fault detection and diagnostic (AFDD) technologies identify energy wasting HVAC faults, such as low indoor airflow and improper refrigerant charge, and guide technicians in improving system efficiency. For residential HVAC, AFDD consists of a range of fault detecting and diagnostic capabilities, sensor configurations, and target applications. AFDD technology can either be permanently installed by the original equipment manufacturer (OEM) using embedded sensors or as an add-on product either during or after installation. Additionally, several advanced installation tools and refrigerant gauge sets include AFDD features for temporary use during equipment installation and tune-ups. Some technologies can detect a fault but have limited diagnostic capabilities. For example, a single-point measurement from the home's thermostat or energy monitor can provide certain fault detection capability by analyzing the equipment runtime or energy consumption. These technologies, though limited at determining the cause of a given fault, may have significant energy savings potential due to their low cost and prevalence in the residential HVAC market. Despite the potential benefits, fault detection technologies face many technical and market barriers preventing broad adoption. Beyond the cost barriers due to the added sensor requirements and technology development, fault detection technologies face many implementation and adoption barriers such as installer training, customer awareness, standardized communication protocols, and methods of test for evaluating accuracy. The purpose of this whitepaper is to characterize market and technical barriers impeding broader utilization of fault detection technology for residential HVAC energy efficiency applications.
Download or read book Online learning based Fault Detection and Diagnosis for HVAC Systems in Commercial Buildings written by Majid Karami and published by . This book was released on 2019 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Heating, ventilation, and air conditioning (HVAC) systems account for a significant portion of the energy consumption in buildings. Faults in HVAC systems, such as equipment degradation, failure in sensors and controllers, if not detected at early stages, can raise the maintenance costs, occupant discomfort, and a significant amount of wasted energy, around 15% to 30% of the total energy consumed in the building. Such a significant energy impact introduced by various faults demonstrates substantial potential for energy saving in buildings by implementing automatic fault detection and diagnosis (AFDD) systems. Despite the extensive research on AFDD of HVAC systems, there is a lack of an AFDD method which is capable of handling the unexplored states in systems. The unexplored states may arise in HVAC systems as the data for training the AFDD algorithm of such complicated nonlinear systems is usually limited. Most of the conventional AFDD methods are only capable of diagnosing the faults for which the prior information is available during the training process, but cannot diagnose an unseen fault in systems. Other possibilities of unexplored states are a new operational mode in the system, change in the control setpoints, and change in the system components due to retrofit and maintenance. The challenge is how to evolve the AFDD algorithm to learn the information about the new faults or new dynamics in the HVAC systems. In this study, to address the problems above, the online-learning-based AFDD algorithm is developed which allows the adaptation of both the structure and the parameters of the AFDD algorithm when a new state in the system is recognized. The proposed AFDD algorithm relies upon an evolving Gaussian mixture modeling approach and has the ability to diagnose any of the already-known faults in the system, reveal an unknown state in the system, and learn the information of the new states. The performance evaluation of the proposed evolving AFDD algorithm is illustrated in detection and diagnosis of various faults in a chiller plant and a variable air volume (VAV) system as they are two common HVAC systems in commercial buildings. The AFDD algorithm is evaluated using both simulation studies and an experiment using an actual VAV system. The results demonstrate the effectiveness of the proposed AFDD algorithm in detecting and diagnosing common faults as well as unseen states in the HVAC systems.
Download or read book Control Systems for Heating Ventilating and Air Conditioning written by Roger W. Haines and published by Springer Science & Business Media. This book was released on 2006-01-19 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Control Systems for Heating, Ventilating and Air Conditioning, Sixth Edition is complete and covers both hardware control systems and modern control technology. The material is presented without bias and without prejudice toward particular hardware or software. Readers with an engineering degree will be reminded of the psychrometric processes associated with heating and air conditioning as they learn of the various controls schemes used in the variety of heating and air conditioning system types they will encountered in the field. Maintenance technicians will also find the book useful because it describes various control hardware and control strategies that were used in the past and are prevalent in most existing heating and air conditioning systems. Designers of new systems will find the fundamentals described in this book to be a useful starting point, and they will also benefit from descriptions of new digital technologies and energy management systems. This technology is found in modern building HVAC system designs.
Download or read book Data driven Whole Building Fault Detection and Diagnosis written by Yimin Chen and published by . This book was released on 2019 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Residential and commercial buildings are responsible for more than 40% of the primary energy consumption in the United States. Energy wastes are estimated to reach 15% to 30% of total energy consumption due to malfunctioning sensors, components, and control systems, as well as degrading components in Heating, Ventilation, Air-conditioning (HVAC) systems and lighting systems in commercial buildings in the U.S. Studies have demonstrated that a large energy saving can be achieved by automated fault detection and diagnosis (AFDD) followed by corrections. Field studies have shown that, AFDD tools can help to reach energy savings by 5-30% from different systems such as HVAC systems, lighting systems, and refrigeration systems. At the same time, the deployment of AFDD tools can also significantly improve indoor air quality, reduce peak demand, and lower pollution. In buildings, many components or equipment are closely coupled in a HVAC system. Because of the coupling, a fault happening in one component might propagate and affect other components or subsystems. In this study, a whole building fault (WBF) is defined as a fault that occurs in one component or equipment but causes fault impacts (abnormalities) on other components and subsystems, or causes significant impacts on energy consumption and/or indoor air quality. Over the past decades, extensive research have been conducted on the development of component AFDD methods and tools. However, whole building AFDD methods, which can detect and diagnose a WBF, have not been well studied. Existing component level AFDD solutions often fail to detect a WBF or generate a high false alarm rate. Isolating a WBF is also very challenging by using component level AFDD solutions. Even with the extensive research, cost-effectiveness and scalability of existing AFDD methods are still not satisfactory. Therefore, the focus of this research is to develop cost-effective and scalable solutions for WBF AFDD. This research attempts to integrate data-driven methods with expert knowledge/rules to overcome the above-mentioned challenges. A suite of WBF AFDD methods have hence been developed, which include: 1) a weather and schedule based pattern matching method and feature based Principal Component Analysis (WPM-FPCA) method for whole building fault detection. The developed WPM-FPCA method successfully overcome the challenges such as the generation of accurate and dynamic baseline and data dimensionality reduction. And 2) a data-driven and expert knowledge/rule based method using both Bayesian Network (BN) and WPM for WBF diagnosis. The developed WPM-BN method includes a two-layer BN structure model and BN parameter model which are either learned from baseline data or developed from expert knowledge. Various WBFs have been artificially implemented in a real demo building. Building operation data which include baseline data, data that contain naturally-occurred faults and artificially implemented faults are collected and analyzed. Using the collected real building data, the developed methods are evaluated. The evaluation demonstrates the efficacy of the developed methods to detect and diagnose a WBF, as well as their implementation cost-effectiveness.
Download or read book Fault Detection and Diagnosis in Building HVAC Systems written by Massieh Najafi and published by . This book was released on 2010 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building HVAC systems account for more than 30% of annual energy consumption in United States. However, it has become apparent that only in a small percentage of buildings do HVAC systems work efficiently or in accordance with design intent. Studies have shown that operational faults are one of the main reasons for the inefficient performance of these systems. It is estimated that an energy saving of 5 to 15 percent is achievable simply by fixing faults and optimizing building control systems. In spite of good progress in recent years, methods to manage faults in building HVAC systems are still generally undeveloped; in particular, there is still a lack of reliable, affordable, and scalable solutions to manage faults in HVAC systems. Modeling limitations, measurement constraints, and the complexity of concurrent faults have made the diagnosis of these problems as much an art as a science. The challenge is how to evaluate system performance within the boundaries defined by such limitations. This thesis focuses on a number of issues that, in our opinion, are crucial to the development of reliable and scalable diagnostic solutions for building HVAC systems. Diagnostic complexity due to modeling and measurement constraints, the pro-activeness of diagnostic mechanisms, bottom-up versus top-down diagnostic perspectives, diagnosis-ability, and the correlation between measurement constraints and diagnostic capability will be discussed in detail. We will develop model-based and non-model-based diagnostic algorithms that have the capability of dealing with modeling and measurement constraints more effectively. We will show how the effect of measurement constraints can be traced to the information entropy of diagnostics assessments and how this can lead to a framework optimizing the architecture of sensor networks from the diagnostic perspective. In another part of this study, we focus on proactive diagnostics. In the past, the topic of proactive fault diagnostics has not been given enough attention, even though the capability of conducting and supervising automated proactive testing is essential in terms of being able to replace manual troubleshooting with automated solutions. We will show how a proactive testing problem can be formulated as a decision making problem coupled with a Bayesian network diagnostic model. The algorithms presented in this thesis have been implemented and tested in the Lawrence Berkeley National Laboratory (LBNL) using real and synthetic data.
Download or read book Fault Detection and Diagnostics Modeling for Air handling Units on the Main Campus of The University of Texas at Austin written by Megan Kellie McHugh and published by . This book was released on 2018 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: Heating, ventilation, and air conditioning accounts for about 44% of energy usage in commercial buildings. In HVAC systems, air handling units are used to condition air based on comfort for occupants or controlled environmental requirements. Faults in AHUs can occur due to failures in equipment, actuators, or sensors and feedback controllers. Leakage typically occurs due to faults in ducts, faults with valves that are stuck, broken, or in the incorrect operating position, faults in measurements of state variables, or faults with the controllers maintaining the setpoint from sensor feedback. The variety faults that can occur in AHUs can lead to increased energy consumption, especially when it remains undetected. AHU faults can also lead to uncomfortable conditions for building occupants or impact research and other special facilities as the campus building types include classroom/academic, hospital/clinic, housing, office/administrative, parking/garage, public assembly/multipurpose, and research laboratories. The building automation systems on the main campus of The University of Texas at Austin manage over 100 buildings each with multiple AHUs in different working conditions. In this paper, a methodology is proposed for the fault detection of AHU steam and chilled water valve leakage and for general fault detection and diagnoses of other common AHU faults on the UT campus. The approach is based on supervised machine learning classification models and compared to the ASHRAE fundamentals expert rule-set models. BAS data trended at 15-minute intervals for periods up to 400 days were used. Faults detected through these methods have been validated by UT Facilities Services upon inspection of the faulty AHUs. A dashboard web application was developed for the interactive use and visualization of the fault detection models by UTFS for continuous maintenance prioritization. A classification analysis allows for the prediction of leakage and provides UTFS a priority ranking of AHUs to address for maintenance in the future. The rule-set models provide a method for continuous tracking of AHU features for faults. Identifying and addressing valve leakage and other faults is expected to reduce energy usage and contribute to reduction in average annual energy use intensity in order to improve demand side energy efficiency while maintaining indoor environmental quality. This will contribute to reach the 2020 energy savings targets set in the 2012 UT Austin Campus Master Plan, which outlines a variety of initiatives for sustainable growth through 2030
Download or read book Heating Ventilation and Air Conditioning Fault Detection Using the Fuzzy JESS Toolkit written by Peter Knall and published by . This book was released on 2014 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research into automated methods for detecting and diagnosing faults in heating, ventilating, and air conditioning systems has been an ongoing process for many years and, as a result, there have been many different methods developed for that purpose. A basic fault detection system is presented based on aspects of several of those approaches using performance index calculations, statistical process control methods, fuzzy logic, and rule-based inference. Factors that drive research in fault detection and diagnosis in the Heating, Ventilation, and Air Condition (HVAC) industry are discussed. A simple HVAC controller is presented with a discussion of the faults the control may experience. These faults are classified into categories, which are then used to develop a test procedure for the fault detection system. The fault detection system is then presented in three modules: preprocessing of sensor data, conversion to fuzzy values, and detection using the JESS inference engine. Sensor data is preprocessed into a time-based performance index based on a departure from setpoint and an exponentially weighted moving average calculation. The conversion of the error values into fuzzy values is then discussed. Once the error values are calculated, the fuzzy error values and controller data are applied to expert rules through the JESS inference engine to detect control faults. This model is tested in two phases. First, data obtained from simulated faults is used during phase one. Phase two applies the fault detection system to a small office building. Finally, the results of the two tests are discussed.
Download or read book Field Testing of Automated Fault Detection and Diagnosis AFDD Tools for Commercial Rooftop Heating Ventilation Air conditioning and Refrigeration Systems written by Annika Hacker and published by . This book was released on 2019 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rooftop units make up more than 60% of cooling systems in commercial buildings. However, though expected to be efficient, majority have at least one, often more, faults, such as valve leakage, incorrect refrigerant charge, fouled condenser/evaporator, or expansion faults. Most faults are never detected until a major malfunction or a failure of the entire system. Automated fault detection and diagnosis (AFDD) tools constantly monitor system's performance and detect anomalies in the sensed data, allowing early problem detection. Using AFDD extends the life and health of the system and helps save energy. While there is an increased interest in implementing these tools, their technical performance has not been verified in the field. This thesis is the first karge scale field test and evaluation of AFDD tools, determining their technical performance. The scope of this work includes the development of specifications for evaluation and selection of AFDD products to be verified, development of a site selection criteria for site host requirements, and development of a measurement and verification plan, installation, and base line evaluation of AFDD products at 5 sites.
