Online support vector machine application for model based fault detection and isolation of HVAC system

Abstract—Preventive maintenance plays an important role in Heating, Ventilation and Air Conditioning (HVAC) system. One cost effective strategy is the development of analytic fault detection and isolation (FDI) module by online monitoring the key variables of HAVC systems. This paper investigates realtime FDI for HAVC system by using online Support Vector Machine (SVM), by which we are able to train a FDI system with manageable complexity under real time working conditions. It is also proposed a new approach which allows us to detect unknown faults and updating the classifier by using these previously unknown faults. Based on the proposed approach, a semi unsupervised fault detection methodology has been developed for HVAC system

Past, present and future mathematical models for buildings (i)

This is the first of two articles presenting a detailed review of the historical evolution of mathematical models applied in the development of building technology, including conventional buildings and intelligent buildings. After presenting the technical differences between conventional and intelligent buildings, this article reviews the existing mathematical models, the abstract levels of these models, and their links to the literature for intelligent buildings. The advantages and limitations of the applied mathematical models are identified and the models are classified in terms of their application range and goal. We then describe how the early mathematical models, mainly physical models applied to conventional buildings, have faced new challenges for the design and management of intelligent buildings and led to the use of models which offer more flexibility to better cope with various uncertainties. In contrast with the early modelling techniques, model approaches adopted in neural networks, expert systems, fuzzy logic and genetic models provide a promising method to accommodate these complications as intelligent buildings now need integrated technologies which involve solving complex, multi-objective and integrated decision problems

A review of model based and data driven methods targeting hardware systems diagnostics

System health diagnosis serves as an underpinning enabler for enhanced safety and optimized maintenance tasks in complex assets. In the past four decades, a wide-range of diagnostic methods have been proposed, focusing either on system or component level. Currently, one of the most quickly emerging concepts within the diagnostic community is system level diagnostics. This approach targets in accurately detecting faults and suggesting to the maintainers a component to be replaced in order to restore the system to a healthy state. System level diagnostics is of great value to complex systems whose downtime due to faults is expensive. This paper aims to provide a comprehensive review of the most recent diagnostics approaches applied to hardware systems. The main objective of this paper is to introduce the concept of system level diagnostics and review and evaluate the collated approaches. In order to achieve this, a comprehensive review of the most recent diagnostic methods implemented for hardware systems or components is conducted, highlighting merits and shortfalls

Enabling VOLTTRON: Energy Management of Commercial Buildings at the University of Maryland

Buildings waste approximately 30% of energy they consume due to inefficient HVAC and lighting operation. Building Automation Systems (BAS) can aid in reducing such wasted energy, but 90% of U.S. commercial buildings lack a BAS due to their high capital costs. This thesis demonstrates how VOLTTRON, an open source operating system developed by Pacific Northwest National Laboratory, was used to disable the mechanical cooling of a rooftop unit (RTU) during unoccupied hours, on a building without a BAS. With cooling off, the RTU’s electricity dropped from ~18 kW to ~7kW. These results indicate $450 to$550 can be saved on the monthly electric bill of the building during the summer, compared to when the RTU operated in cooling mode continuously. The installation cost of the equipment that enabled the RTU to be controlled via VOLTTRON was $6,400, thus the project has a payback period of 13 months

Modellistica e controllo di un sistema di condizionamento di tipo VAV

Negli impianti di condizionamento HVAC (heating ventilating and air conditioning), impiegati solitamente in edifici complessi, è assai utile un sistema in grado di rilevare e classificare possibili guasti. Questo ambito di ricerca è noto come "fault detection and diagnosis". L’obiettivo che si intende perseguire in questa tesi è quello di ricavare un modello di un semplice sistema HVAC di tipo VAV (variable air volume), sul quale sia possibile applicare tecniche di fault detection di tipo model-based. Una volta ricavato il plant e testato il suo comportamento in catena aperta, sarà elaborata una legge di controllo basata su una tecnica detta "feedback linearization", utile per disaccoppiare gli ingressi nei sistemi non lineari. Infine, dimostrata la validità del controllo, saranno suggeriti alcuni tipi di fault che possono interessare il modello costruito e come potrebbero essere generat

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

Uso do método de Dempster-Shafer

Uma das primeiras aplicações da Teoria de Dempster-Shafer - DempsterShafer Theory (DST) como método de abdução de sintomas aplicado a falhas em sistemas de refrigeração é apresentada nesta dissertação. A metodologia proporciona uma estrutura de desenvolvimento de novos métodos de Deteção e Diagnóstico de Falhas - Fault Detection and Diagnosis (FDD) apoiados pela DST. O estudo de um sistema de refrigeração, como sistema complexo, permite a aplicação de um algoritmo de FDD. A deteção antecipada de condições de falha é usada em manutenção preventiva. A Análise de Modos de Falha e Efeitos - Failure Mode and Effects Analysis (FMEA) permite discriminar e priorizar possíveis modos de falha de modo a selecionar falhas adequadas à FDD. Os Conjuntos Difusos - Fuzzy Sets (FS) definem funções de pertença que melhor caracterizam o estado de um parâmetro face a uma avaliação em detrimento da definição de um limiar de valores discretos. A quantificação do desvio de um parâmetro analisado é resultado do sistema de inferência construído. A Inteligência Artificial - Artificial Intelligence (AI) é uma ferramenta que permite ajustar modelos de sistemas complexos, suportados por conhecimento especialista conforme novas entradas de dados. A DST é uma teoria para o tratamento de incerteza em ambientes de informação incompleta ou ambígua que considera dois limites para a possibilidade dos acontecimentos: a crença e a plausibilidade. O programa materializa um raciocínio de abdução em vez de dedução. Às causas associam-se sintomas. A aquisição de dados pode evidenciar sintomas. As causas são determinadas com base em valores probabilísticos para cada condição de falha possível. A regra de combinação de evidências da DST permite considerar e combinar duas ou mais fontes de informação. A compilação de pequenos indícios que não dão alarmes ou pré-alarmes podem ser combinados para identificar situações anormais.One of the first applications of the DST as a symptom abduction method applied to failures in refrigeration systems is presented in this dissertation. The methodology provides a framework for developing new FDD methods supported by the DST. The study of a refrigeration system, as a complex system, allows the application of a FDD algorithm. Early detection of fault conditions is used in preventive maintenance. The Failure Mode and Effects Analysis (FMEA) allows the discrimination and prioritization of possible failure modes in order to select faults suitable for FDD. Fuzzy Sets (FS) define membership functions that better characterize the state of a parameter in the face of an evaluation, rather than defining a threshold of discrete values. The quantification of the deviation of an analyzed parameter is a result of the built inference system. Artificial Inteligence (AI) is a tool that allows you to adjust models of complex systems, supported by expert knowledge, according to new data inputs. The Dempster-Shafer Theory (DST) is a theory for the treatment of uncertainty in environments of incomplete or ambiguous information that considers two limits to the possibility of events: belief and plausibility. The program materializes abduction reasoning instead of deduction. The causes are associated with symptoms. Data acquisition may highlight symptoms. Causes are determined based on probabilistic values for each possible failure condition. The DST combination rule of evidence allows the consideration and combination of two or more sources of information. The compilation of small pieces of evidences that do not trigger alarms or pre-alarms can be combined to identify unsual situations

System diagnosis for an auxiliary power unit

Even though the Auxiliary Power Unit (APU) is a widely used system in modern aviation, the existing experimental, simulation and diagnostic studies for this system are very limited. The topic of this project is the System Diagnosis of an APU, and the case study that is used in this research is a Boeing 747 APU. This APU was used to develop an experimental rig in order to collect performance data under a wide range of loading and environmental conditions. The development of the experimental rig consumed considerable time and required the design and installation of structures and parts related with the control of the APU, the adjustment of the electric and pneumatic load and the data acquisition. The validation of the rig was achieved by a repeatability test, which ensures that the collected measurements are repeatable under the same boundary conditions, and by a consistency test, which ensures that the performance parameters are consistent with the imposed ambient conditions. The experimental data that are extracted from the rig were used to calibrate a physics-based (0-D) model for steady-state conditions. Data that correspond to faulty conditions were generated by injecting faults in the simulation model. Based on the most prominent APU faults, as reported by The Boeing Company, six components that belong to different sub-systems were considered in the diagnostic analysis, and for each one of them, a single fault mode was simulated. By using healthy and faulty simulation data, for each component under examination, a classification algorithm that can recognise the healthy and faulty state of the component is trained. A critical part of the diagnostic analysis is that each classifier was trained to recognise the healthy and the faulty state of the corresponding component, while other components can be either healthy or faulty. The test results showed that the proposed technique is able to diagnose both single and multiple faults, even though in many cases different component faults resulted in similar fault patterns.Transport System

Diagnóstico de fallos en sistemas industriales basado en razonamiento borroso y posibilístico

Esta tesis aborda el diagnóstico de fallos en sistemas industriales por técnicas de Inteligencia Artificial, tratando en particular el razonamiento borroso y posibilístico. Inicialmente, se presentan los problemas a resolver en el diagnóstico de sistemas y después se plantean estrategias para abordarlos a partir de diferentes técnicas de Inteligencia Artificial, en donde destacamos los métodos relacionales borrosos que serán la base para nuestra aportación principal. También se han estudiado los sistemas expertos basados en lógica borrosa y que usan tablas de decisión, los sistemas expertos que combinan lógica borrosa con probabilidad y los sistemas de diagnóstico basados en redes Bayesianas. Se experimenta con varias técnicas de diagnóstico descritas en el estado del arte, haciendo combinaciones entre ellas. Una vez experimentadas y evaluadas las anteriores técnicas, vistos los inconvenientes que surgían, se decidió implementar una nueva metodología que diera una mejor solución al problema del diagnóstico. Esta metodología es el diagnóstico posibilístico borroso visto como un problema de optimización lineal. La metodología convierte los enunciados lingüísticos, que componen una base de reglas de un sistema experto borroso, en un conjunto de ecuaciones lineales a través de técnicas relacionales. Luego, estas ecuaciones se utilizan con algoritmos de programación lineal. Algunas modificaciones requieren programación cuadrática. Los resultados obtenidos en esta última aportación en una aplicación de análisis de aceites fueron satisfactorios, presentando al usuario una salida de diagnóstico fácil de interpretar, suficientemente exacta y teniendo en cuenta la incertidumbre en reglas y medidas.Ramírez Valenzuela, JC. (2007). Diagnóstico de fallos en sistemas industriales basado en razonamiento borroso y posibilístico [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1922Palanci