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

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

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

Fault data seasonal imbalance and insufficiency impacts on data-driven heating, ventilation and air-conditioning fault detection and diagnosis performances for energy-efficient building operations

The heating, ventilation and air-conditioning fault impacts vary with different seasonal climatic conditions, but the fault data may not be available under some seasons in real buildings due to the frequency and span of fault occurrences. This study evaluates the fault detection and diagnosis (FDD) performance differences of the proposed convolutional and recurrent neural networks under limited seasonal fault data scenarios and an ideal scenario covering climatic conditions from multiple seasons. The fault and normal data were gathered from fault simulations using a verified prototype building EnergyPlus model and two real fault datasets. Four different data experiments based on the simulated dataset were implemented to assess FDD performance differences, and two sets of further experiments based on each real fault dataset were conducted to verify the findings from previous experiments. The results show that the FDD architectures, trained on sufficient fault data under a certain season(s), indicate poor generalization ability to identify faults under unseen seasons. Moreover, the coverage of fault data under different seasons is more crucial in enhancing FDD performances than the amount of fault data under each season. These findings will help researchers consider this practical issue when evaluating new or existing data-driven FDD methods

Healthy and Faulty Experimental Performance of a Typical HVAC System under Italian Climatic Conditions: Artificial Neural Network-Based Model and Fault Impact Assessment

The heating, ventilation, and air conditioning (HVAC) system serving the test room of the SENS i-Lab of the Department of Architecture and Industrial Design of the University of Campania Luigi Vanvitelli (Aversa, south of Italy) has been experimentally investigated through a series of tests performed during both summer and winter under both normal and faulty scenarios. In particular, five distinct typical faults have been artificially implemented in the HVAC system and analyzed during transient and steady-state operation. An optimal artificial neural network-based system model has been created in the MATLAB platform and verified by contrasting the experimental data with the predictions of twenty-two different neural network architectures. The selected artificial neural network architecture has been coupled with a dynamic simulation model developed by using the TRaNsient SYStems (TRNSYS) software platform with the main aims of (i) making available an experimental dataset characterized by labeled normal and faulty data covering a wide range of operating and climatic conditions; (ii) providing an accurate simulation tool able to generate operation data for assisting further research in fault detection and diagnosis of HVAC units; and (iii) evaluating the impact of selected faults on occupant indoor thermo-hygrometric comfort, temporal trends of key operating system parameters, and electric energy consumptions

Maintenance of Smart Buildings using Fault Trees

Timely maintenance is an important means of increasing system dependability and life span. Fault Maintenance trees (FMTs) are an innovative framework incorporating both maintenance strategies and degradation models and serve as a good planning platform for balancing total costs (operational and maintenance) with dependability of a system. In this work, we apply the FMT formalism to a {Smart Building} application and propose a framework that efficiently encodes the FMT into Continuous Time Markov Chains. This allows us to obtain system dependability metrics such as system reliability and mean time to failure, as well as costs of maintenance and failures over time, for different maintenance policies. We illustrate the pertinence of our approach by evaluating various dependability metrics and maintenance strategies of a Heating, Ventilation and Air-Conditioning system.Comment: arXiv admin note: substantial text overlap with arXiv:1801.0426

Framework for Knowledge-Based Fault Detection and Diagnostics in Multi-Domain Systems: Application to HVAC Systems

State-of-the-art fault detection methods are equipment and domain specific and non-comprehensive. As a result, the applicability of these methods in different domains is very limited and they can achieve significant levels of performance by having knowledge of the domain and the ability to mimic human thinking in identifying the source of a fault with a comprehensive knowledge of the system and its surroundings. This technical report presents a comprehensive semantic framework for fault detection and diagnostics (FDD) in systems simulation and control. Our proposed methodology entails of implementation of the knowledge bases for FDD purposes through the utilization of ontologies and offers improved functionalities of such system through inference-based reasoning to derive knowledge about the irregularities in the operation. We exercise the proposed approach by working step by step through the setup and solution of a fault detection and diagnostics problem for a small-scale heating, ventilating and air-conditioning (HVAC) system.NIS