Fault Detection for Systems with Multiple Unknown Modes and Similar Units

This dissertation considers fault detection for large-scale practical systems with many nearly identical units operating in a shared environment. A special class of hybrid system model is introduced to describe such multi-unit systems, and a general approach for estimation and change detection is proposed. A novel fault detection algorithm is developed based on estimating a common Gaussian-mixture distribution for unit parameters whereby observations are mapped into a common parameter-space and clusters are then identified corresponding to different modes of operation via the Expectation- Maximization algorithm. The estimated common distribution incorporates and generalizes information from all units and is utilized for fault detection in each individual unit. The proposed algorithm takes into account unit mode switching, parameter drift, and can handle sudden, incipient, and preexisting faults. It can be applied to fault detection in various industrial, chemical, or manufacturing processes, sensor networks, and others. Several illustrative examples are presented, and a discussion on the pros and cons of the proposed methodology is provided. The proposed algorithm is applied specifically to fault detection in Heating Ventilation and Air Conditioning (HVAC) systems. Reliable and timely fault detection is a significant (and still open) practical problem in the HVAC industry { commercial buildings waste an estimated 15% to 30% ($20.8B -$41.61B annually) of their energy due to degraded, improperly controlled, or poorly maintained equipment. Results are presented from an extensive performance study based on both Monte Carlo simulations as well as real data collected from three operational large HVAC systems. The results demonstrate the capabilities of the new methodology in a more realistic setting and provide insights that can facilitate the design and implementation of practical fault detection for systems of similar type in other industrial applications

Fault detection and diagnostics in ventilation units using linear regression virtual sensors

Buildings represent a significant portion of global energy consumption. Ventilation units are one of the largest components in buildings systems and are responsible for large part of energy consumption. Ventilation units are complex components, often customized for the specific building. Their faults impact buildings' energy efficiency and occupancy comfort. In order to ensure their correct operation, proper Fault Detection and Diagnostics methods must be applied. Hardware redundancy, an effective approach to detect faults, leads to increased costs and space requirements. We propose to exploit physical relations inside the unit to create virtual sensors from other sensors' readings, introducing redundancy in the system. We create linear regression models for three sensors using other sensors related through physical laws as inputs. We use two different measures to detect when a virtual sensor deviates from the actual one: coefficient of determination and acceptable range. We test our method on a real building at the University of Southern Denmark. Our method detects a fault in temperature sensor, where its readings have an abnormal trend and fall outside acceptable ranae for one day.Postprint (author's final draft

A method for fault detection and diagnostics in ventilation units using virtual sensors

Buildings represent a significant portion of global energy consumption. Ventilation units are complex components, often customized for the specific building, responsible for a large part of energy consumption. Their faults impact buildings’ energy efficiency and occupancy comfort. In order to ensure their correct operation, proper fault detection and diagnostics methods must be applied. Hardware redundancy, an effective approach to detect faults, leads to increased costs and space requirements. We propose exploiting physical relations inside ventilation units to create virtual sensors from other sensors’ readings, introducing redundancy in the system. We use two different measures to detect when a virtual sensor deviates from the physical one: coefficient of determination for linear models, and acceptable range. We tested our method on a real building at the University of Southern Denmark, developing three virtual sensors: temperature, airflow, and fan speed. We employed linear regression models, statistical models, and non-linear regression models. All models detected an anomalous strong oscillation in the temperature sensors. Readings fell outside the acceptable range and the coefficient of determination dropped. Our method showed promising results by introducing redundancy in the system, which can benefit several applications, such as fault detection and diagnostics and fault-tolerant control. Future work will be necessary to discover thresholds and set up automatic fault detection and diagnostics.Peer ReviewedPostprint (published version

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 for Residential HVAC Systems using Transient Cloud-based Thermostat Data

Fault detection and diagnosis (FDD) using aggregated smart thermostat data is a relatively new research field, but one with immediate practical application to residential indoor climate control. This paper analyzes a cloud-based dataset which contains thermostat history records of nearly 370,000 distinct residential HVAC systems in the U.S. The large, diverse, and growing dataset enables novel methods for detecting and diagnosing faults on systems with limited sensor data. This paper proposes a statistics-based FDD method for non-variable speed heat pump and air conditioning units, and demonstrates the effectiveness with several case studies. The proposed method identifies systems within a similar climate region, and then segments and classifies the time series data based on operational mode and behavior. Various data features are then extracted from the time series segments to identify systems that exhibit poor transient behavior. Additional features are used to refine and classify the problem severity. Statistical methods are then used to compare system performance to the entire population and identify outlier behavior due to operational faults that affect system efficiency and occupancy comfort. The resulting algorithm demonstrates the potential of big data fault detection for air conditioning systems using limited cloud-based sensor information

Parameter-Invariant Monitor Design for Cyber Physical Systems

The tight interaction between information technology and the physical world inherent in Cyber-Physical Systems (CPS) can challenge traditional approaches for monitoring safety and security. Data collected for robust CPS monitoring is often sparse and may lack rich training data describing critical events/attacks. Moreover, CPS often operate in diverse environments that can have significant inter/intra-system variability. Furthermore, CPS monitors that are not robust to data sparsity and inter/intra-system variability may result in inconsistent performance and may not be trusted for monitoring safety and security. Towards overcoming these challenges, this paper presents recent work on the design of parameter-invariant (PAIN) monitors for CPS. PAIN monitors are designed such that unknown events and system variability minimally affect the monitor performance. This work describes how PAIN designs can achieve a constant false alarm rate (CFAR) in the presence of data sparsity and intra/inter system variance in real-world CPS. To demonstrate the design of PAIN monitors for safety monitoring in CPS with different types of dynamics, we consider systems with networked dynamics, linear-time invariant dynamics, and hybrid dynamics that are discussed through case studies for building actuator fault detection, meal detection in type I diabetes, and detecting hypoxia caused by pulmonary shunts in infants. In all applications, the PAIN monitor is shown to have (significantly) less variance in monitoring performance and (often) outperforms other competing approaches in the literature. Finally, an initial application of PAIN monitoring for CPS security is presented along with challenges and research directions for future security monitoring deployments