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

Investigation of the Prevalence of Faults in the Heating, Ventilation, and Air-Conditioning Systems of Commercial Buildings

This dissertation describes a large-scale investigation of heating, ventilation, and air-conditioning (HVAC) fault prevalence in commercial buildings in the United States. A multi-year dataset with 36,556 pieces of HVAC equipment including air handling units (AHUs), air terminal units (ATUs), and packaged rooftop units (RTUs) was analyzed to determine values for several HVAC fault prevalence metrics. The primary source of data for this study comes from three commercial fault detection and diagnostics (FDD) providers. Since each FDD provider uses different terms to refer to the same fault in an HVAC system, a mapping function was created for each FDD provider’s dataset, to convert the fault reports to a single standardized fault identifier. The fault identifier is taken from a standard taxonomy that was created for this purpose. Since the commercial FDD software outputs are inherently subject to some level of error, i.e., they could have false negatives and false positives, a field study was conducted to gain greater insight into the commercial FDD software results. Two buildings from among the buildings of one of the FDD providers were selected. The RTUs serving these two buildings were monitored for about two weeks using our installed data loggers. The actual faults in these buildings were identified using methods that we developed or selected from the literature. The results of the field study were compared with the FDD provider fault reports. This study also proposes a data-driven FDD strategy for RTUs, using machine learning classification methods. The FDD task is formulated as a multi-class classification problem. Seven typical RTU faults are discriminated against one another as well as the normal condition. Nine classification methods were applied to a dataset of simulation data, which was split into a training set and a test set. The performance of the classifiers for individual faults was characterized using true positive rate and false positive rate statistical measures. The relative importance of input variables was analyzed, and is also discussed in the dissertation. Advisor: David Yuil

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Advanced energy management strategies for HVAC systems in smart buildings

The efficacy of the energy management systems at dealing with energy consumption in buildings has been a topic with a growing interest in recent years due to the ever-increasing global energy demand and the large percentage of energy being currently used by buildings. The scale of this sector has attracted research effort with the objective of uncovering potential improvement avenues and materializing them with the help of recent technological advances that could be exploited to lower the energetic footprint of buildings. Specifically, in the area of heating, ventilating and air conditioning installations, the availability of large amounts of historical data in building management software suites makes possible the study of how resource-efficient these systems really are when entrusted with ensuring occupant comfort. Actually, recent reports have shown that there is a gap between the ideal operating performance and the performance achieved in practice. Accordingly, this thesis considers the research of novel energy management strategies for heating, ventilating and air conditioning installations in buildings, aimed at narrowing the performance gap by employing data-driven methods to increase their context awareness, allowing management systems to steer the operation towards higher efficiency. This includes the advancement of modeling methodologies capable of extracting actionable knowledge from historical building behavior databases, through load forecasting and equipment operational performance estimation supporting the identification of a building’s context and energetic needs, and the development of a generalizable multi-objective optimization strategy aimed at meeting these needs while minimizing the consumption of energy. The experimental results obtained from the implementation of the developed methodologies show a significant potential for increasing energy efficiency of heating, ventilating and air conditioning systems while being sufficiently generic to support their usage in different installations having diverse equipment. In conclusion, a complete analysis and actuation framework was developed, implemented and validated by means of an experimental database acquired from a pilot plant during the research period of this thesis. The obtained results demonstrate the efficacy of the proposed standalone contributions, and as a whole represent a suitable solution for helping to increase the performance of heating, ventilating and air conditioning installations without affecting the comfort of their occupants.L’eficàcia dels sistemes de gestió d’energia per afrontar el consum d’energia en edificis és un tema que ha rebut un interès en augment durant els darrers anys a causa de la creixent demanda global d’energia i del gran percentatge d’energia que n’utilitzen actualment els edificis. L’escala d’aquest sector ha atret l'atenció de nombrosa investigació amb l’objectiu de descobrir possibles vies de millora i materialitzar-les amb l’ajuda de recents avenços tecnològics que es podrien aprofitar per disminuir les necessitats energètiques dels edificis. Concretament, en l’àrea d’instal·lacions de calefacció, ventilació i climatització, la disponibilitat de grans bases de dades històriques als sistemes de gestió d’edificis fa possible l’estudi de com d'eficients són realment aquests sistemes quan s’encarreguen d'assegurar el confort dels seus ocupants. En realitat, informes recents indiquen que hi ha una diferència entre el rendiment operatiu ideal i el rendiment generalment assolit a la pràctica. En conseqüència, aquesta tesi considera la investigació de noves estratègies de gestió de l’energia per a instal·lacions de calefacció, ventilació i climatització en edificis, destinades a reduir la diferència de rendiment mitjançant l’ús de mètodes basats en dades per tal d'augmentar el seu coneixement contextual, permetent als sistemes de gestió dirigir l’operació cap a zones de treball amb un rendiment superior. Això inclou tant l’avanç de metodologies de modelat capaces d’extreure coneixement de bases de dades de comportaments històrics d’edificis a través de la previsió de càrregues de consum i l’estimació del rendiment operatiu dels equips que recolzin la identificació del context operatiu i de les necessitats energètiques d’un edifici, tant com del desenvolupament d’una estratègia d’optimització multi-objectiu generalitzable per tal de minimitzar el consum d’energia mentre es satisfan aquestes necessitats energètiques. Els resultats experimentals obtinguts a partir de la implementació de les metodologies desenvolupades mostren un potencial important per augmentar l'eficiència energètica dels sistemes de climatització, mentre que són prou genèrics com per permetre el seu ús en diferents instal·lacions i suportant equips diversos. En conclusió, durant aquesta tesi es va desenvolupar, implementar i validar un marc d’anàlisi i actuació complet mitjançant una base de dades experimental adquirida en una planta pilot durant el període d’investigació de la tesi. Els resultats obtinguts demostren l’eficàcia de les contribucions de manera individual i, en conjunt, representen una solució idònia per ajudar a augmentar el rendiment de les instal·lacions de climatització sense afectar el confort dels seus ocupant

Advanced energy management strategies for HVAC systems in smart buildings

The efficacy of the energy management systems at dealing with energy consumption in buildings has been a topic with a growing interest in recent years due to the ever-increasing global energy demand and the large percentage of energy being currently used by buildings. The scale of this sector has attracted research effort with the objective of uncovering potential improvement avenues and materializing them with the help of recent technological advances that could be exploited to lower the energetic footprint of buildings. Specifically, in the area of heating, ventilating and air conditioning installations, the availability of large amounts of historical data in building management software suites makes possible the study of how resource-efficient these systems really are when entrusted with ensuring occupant comfort. Actually, recent reports have shown that there is a gap between the ideal operating performance and the performance achieved in practice. Accordingly, this thesis considers the research of novel energy management strategies for heating, ventilating and air conditioning installations in buildings, aimed at narrowing the performance gap by employing data-driven methods to increase their context awareness, allowing management systems to steer the operation towards higher efficiency. This includes the advancement of modeling methodologies capable of extracting actionable knowledge from historical building behavior databases, through load forecasting and equipment operational performance estimation supporting the identification of a building’s context and energetic needs, and the development of a generalizable multi-objective optimization strategy aimed at meeting these needs while minimizing the consumption of energy. The experimental results obtained from the implementation of the developed methodologies show a significant potential for increasing energy efficiency of heating, ventilating and air conditioning systems while being sufficiently generic to support their usage in different installations having diverse equipment. In conclusion, a complete analysis and actuation framework was developed, implemented and validated by means of an experimental database acquired from a pilot plant during the research period of this thesis. The obtained results demonstrate the efficacy of the proposed standalone contributions, and as a whole represent a suitable solution for helping to increase the performance of heating, ventilating and air conditioning installations without affecting the comfort of their occupants.L’eficàcia dels sistemes de gestió d’energia per afrontar el consum d’energia en edificis és un tema que ha rebut un interès en augment durant els darrers anys a causa de la creixent demanda global d’energia i del gran percentatge d’energia que n’utilitzen actualment els edificis. L’escala d’aquest sector ha atret l'atenció de nombrosa investigació amb l’objectiu de descobrir possibles vies de millora i materialitzar-les amb l’ajuda de recents avenços tecnològics que es podrien aprofitar per disminuir les necessitats energètiques dels edificis. Concretament, en l’àrea d’instal·lacions de calefacció, ventilació i climatització, la disponibilitat de grans bases de dades històriques als sistemes de gestió d’edificis fa possible l’estudi de com d'eficients són realment aquests sistemes quan s’encarreguen d'assegurar el confort dels seus ocupants. En realitat, informes recents indiquen que hi ha una diferència entre el rendiment operatiu ideal i el rendiment generalment assolit a la pràctica. En conseqüència, aquesta tesi considera la investigació de noves estratègies de gestió de l’energia per a instal·lacions de calefacció, ventilació i climatització en edificis, destinades a reduir la diferència de rendiment mitjançant l’ús de mètodes basats en dades per tal d'augmentar el seu coneixement contextual, permetent als sistemes de gestió dirigir l’operació cap a zones de treball amb un rendiment superior. Això inclou tant l’avanç de metodologies de modelat capaces d’extreure coneixement de bases de dades de comportaments històrics d’edificis a través de la previsió de càrregues de consum i l’estimació del rendiment operatiu dels equips que recolzin la identificació del context operatiu i de les necessitats energètiques d’un edifici, tant com del desenvolupament d’una estratègia d’optimització multi-objectiu generalitzable per tal de minimitzar el consum d’energia mentre es satisfan aquestes necessitats energètiques. Els resultats experimentals obtinguts a partir de la implementació de les metodologies desenvolupades mostren un potencial important per augmentar l'eficiència energètica dels sistemes de climatització, mentre que són prou genèrics com per permetre el seu ús en diferents instal·lacions i suportant equips diversos. En conclusió, durant aquesta tesi es va desenvolupar, implementar i validar un marc d’anàlisi i actuació complet mitjançant una base de dades experimental adquirida en una planta pilot durant el període d’investigació de la tesi. Els resultats obtinguts demostren l’eficàcia de les contribucions de manera individual i, en conjunt, representen una solució idònia per ajudar a augmentar el rendiment de les instal·lacions de climatització sense afectar el confort dels seus ocupantsPostprint (published version

Fault detection and diagnosis of low delta-T syndrome in air handling unit cooling coils

In the built environment, most energy is used to promote well-being, health, and comfort. The demand for cooling will increase sharply as a result of global warming, better thermal insulation, and the heat island effect. It is therefore increasingly important that cooling installations function optimally. Currently, there are many chilled water installations that suffer from the so-called low delta-T syndrome. The return water temperature from the installations is lower than predetermined and the difference with the supply temperature is smaller, low delta-T. This has adverse consequences for the efficiency of the chillers and/or heat pump and for the energy consumption of the pumps. The result is an energy consumption that is 20-40% higher for cooling than calculated in advance. It is important to be able to detect and analyze the low-dT syndrome properly. Based on this, a software module has been developed that can use the data from a building management system to determine the low-dT syndrome and identify possible causes. Building management systems can be equipped with fault detection and diagnosis module for continuous monitoring of the performance of installations, and continuous commissioning (Cx). This would ensure that the energy consumption of the cooling installations remains as low as possible. Within the project, the first prototype of such a module was built. This will be further refined and expanded in ongoing future projects of other PDEng trainees

Semi-supervised transfer learning methodology for fault detection and diagnosis in air-handling units

Heating, ventilation and air-conditioning (HVAC) systems are the major energy consumers among buildings’ equipment. Reliable fault detection and diagnosis schemes can effectively reduce their energy consumption and maintenance costs. In this respect, data-driven approaches have shown impressive results, but their accuracy depends on the availability of representative data to train the models, which is not common in real applications. For this reason, transfer learning is attracting growing attention since it tackles the problem by leveraging the knowledge between datasets, increasing the representativeness of fault scenarios. However, to date, research on transfer learning for heating, ventilation and air-conditioning has mostly been focused on learning algorithmic, overlooking the importance of a proper domain similarity analysis over the available data. Thus, this study proposes the design of a transfer learning approach based on a specific data selection methodology to tackle dissimilarity issues. The procedure is supported by neural network models and the analysis of eventual prediction uncertainties resulting from the assessment of the target application samples. To verify the proposed methodology, it is applied to a semi-supervised transfer learning case study composed of two publicly available air-handling unit datasets containing some fault scenarios. Results emphasize the potential of the proposed domain dissimilarity analysis reaching a classification accuracy of 92% under a transfer learning framework, an increase of 37% in comparison to classical approaches.Objectius de Desenvolupament Sostenible::11 - Ciutats i Comunitats SosteniblesObjectius de Desenvolupament Sostenible::12 - Producció i Consum ResponsablesPostprint (published version

New methods and models for the ongoing commissioning of HVAC systems in commercial and institutional buildings

The performance of the HVAC systems in buildings tends to decrease after few years of operation. Equipment and sensors degradation lead to remarkable wastes of energy and money, as well as to the increase of building occupants thermal discomfort. HVAC ongoing commissioning (OCx), the continuation of HVAC commissioning well into the occupancy and operation phase of a building life, has been recognized as a cost-effective strategy to reduce energy wastes, equipment degradation and thermal discomfort. Building Automation Systems (BAS) collect and store huge amount of data for the purpose of building systems control. Those data represent a golden mine of information that can be used for the OCx of the building HVAC systems. This research work develops and validates new methods and models to be used for the OCx of HVAC systems using BAS measurements from commonly installed sensors. A Fault Detection and Identification (FD&I) method for chillers operation, and several virtual sensor models for variables of interest in Air Handling Units (AHUs) are presented. A FD&I method based on Principal Components Analysis (PCA) has been developed and used to detect abnormal operation conditions in an existing chiller operation and identify the responsible variables. The proposed FD&I method has been trained using measurements from summer 2009, and then used to detect abnormal observations from the following seven summer seasons (2010-2016). When the detected abnormal observations were replaced with artificially generated fault-free data, the proposed FD&I method did not detect any abnormal value along those artificially faulty-free variables. In summer 2016 the building operators changed several HVAC system operation set points, the FD&I method was effective in detecting almost 100% of the observations and properly identifying those variables whose set point was changed. For two different operation modes of an AHU several virtual outdoor air flow meters have been developed and the predictions have been compared against short-term measurements using uncertainty analysis and statistical indices. Three models have been investigated when the heat recovery coil was off. Results showed that the model with the simplest mathematical formulation was the most accurate, with the lowest value of uncertainty. When a heat recovery coil at the fresh air intake was on, two virtual flow meters have been developed to predict the outdoor air flow rate without the need of additional sensors. Both the models predicted the outdoor air ratio with good statistical indices: the Mean Absolute Error (MAE) was 0.015 for model a and 0.016 for model b. Three methods for the virtual measurement and/or calibration of air temperature and relative humidity have been developed for different AHU operation modes. These methods are different in terms of modelling strategy, information needed and technical knowledge required for implementation. For instance, results from the correction of the faulty measurements of the outdoor air temperature along a 24 hours period using Method A showed a high virtual calibration capability: MAE = 0.2°C and the Coefficient of Variation, CV-RMSE = 1.7%. A new definition of virtual sensor is proposed at the end of this research work. From a review of publications on virtual sensors for building application, the two most recurrent reason for the implementation of virtual sensor models (costs and practical issues) have been highlighted and integrated into the proposed new definition

Low delta-T syndrome in cooling systems:A systematic review of the signs, symptoms, and causes

Return water temperature and flow rate are indicators of the energy efficiency of chilled water systems. Since the late 1980s, the return water temperature has deviated from the designed value, resulting in an increased flow rate. Such deviations have been recognized as a persistent ‘disease’ named low delta-T syndrome. Based on a medical approach, this study aimed to categorise the key signs and symptoms, and causes to classify low delta-T syndrome into subclasses with individual properties; to connect individual causes to the subclasses; and to identify disagreements on individual causes. Through a systematic review of the literature, over 190 papers published since the late 1980s were identified and studied. By combining different return water temperature profiles and flow rates, low delta-T syndrome was classified into four subclasses with severities ranging from 1 (mild) to 4 (extreme). These subclasses were described with 12 signs and symptoms, each characterised by 19 (from a total of 52) individual or combined causes, to provide an improved overview and a fundamental basis for developing treatments. A fundamental analysis of low delta-T syndrome on a cooling coil revealed that cooling coils with a high chilled water temperature difference and a high chilled water supply temperature at design conditions have a higher risk of developing it. This literature review provides an improved understanding of as well as considerations regarding how to prevent, resolve, mitigate, and handle low delta-T syndrome during design and operation