The enormous energy use of the building sector and the requirements for indoor living quality that aim to improve occupants'productivity and health, prioritize Smart Buildings as an emerging technology. The Heating, Ventilation and Air-Conditioning ( HVAC ) system is considered one of the most critical and essential parts in buildings since it consumes the largest amount of energy and is responsible for humans comfort. Due to the intermittent operation of HVAC systems, faults are more likely to occur, possibly increasing eventually building's energy consumption and - or downgrading indoor living quality. The complexity and large scale nature of HVAC systems complicate the diagnosis of faults in a centralized framework. This paper presents a distributed intelligent fault diagnosis algorithm for detecting and isolating multiple sensor faults in large-scale HVAC systems. Modeling the HVAC system as a network of interconnected subsystems allows the design of a set of distributed sensor fault diagnosis agents capable of isolating multiple sensor faults by applying a combinatorial decision logic and diagnostic reasoning. The performance of the proposed method is investigated with respect to robustness, fault detectability and scalability. Simulations are used to illustrate the effectiveness of the proposed method in the presence of multiple sensor faults applied to a 83-zone HVAC system and to evaluate the sensitivity of the method with respect to sensor noise variance.

Indoor Air Quality monitoring is an essential ingredient of intelligent buildings. The release of various airborne contaminants into the buildings, compromises the health and safety of occupants. Therefore, early contaminant detection is of paramount importance for the timely activation of proper contingency plans in order to minimize the impact of contaminants on occupants health. The objective of this work is to enhance the performance of a distributed contaminant detection methodology, in terms of the minimum detectable contaminant release rates, by considering the joint problem of partitioning selection and observer gain design. Towards this direction, a detectability analysis is performed to derive appropriate conditions for the minimum guaranteed detectable contaminant release rate for specific partitioning configuration and observer gains. The derived detectability conditions are then exploited to formulate and solve an optimization problem for jointly selecting the partitioning configuration and observer gains that yield the best contaminant detection performance.

This paper presents a distributed methodology for controlling multi-zone Heating, Ventilation and Air-Conditioning (HVAC) systems and a fault accommodation scheme for reconfiguring the distributed controller in the presence of unknown sensor faults. The multi-zone HVAC system is modelled as a network of interconnected subsystems representing the temperature dynamics of the storage tank and the various building zones. The distributed control scheme for each subsystem is based on local measurements, as well as measurements from neighboring subsystems. In the presence of a sensor fault, an accommodation scheme is designed by adaptively estimating and compensating the effect of the sensor fault. The estimation of the local sensor fault is exploited not only by the local but also by neighboring controllers to reduce fault propagation effects resulting from the distributed control architecture. Under certain conditions, the closed-loop stability of the multi-zone HVAC system is analyzed in the presence of modeling uncertainty and measurement noise, under both healthy conditions and faulty sensor measurements. Simulation results are used to illustrate the proposed distributed sensor fault accommodation scheme.

This paper develops a performance index that can be used to find the optimal design parameters of the observer-based residual generator and adaptive threshold of a fault detection scheme for a class of nonlinear systems. The performance of the fault detection scheme is analyzed with respect to the fault detectability of incipient sensor faults, which depends on the dynamic behavior of the residual and the adaptive threshold generator. The proposed performance index is based on the distance between two limit sets that are guaranteed to include the residual under fault-free and faulty conditions. The novelty of this distance metric stems from the parametrization of the limit sets in relation to (i) the design parameters of both the residual generator and adaptive threshold, (ii) the bounds on the system disturbances and measurement noise, and (iii) the fault function and evolution rate. An optimization problem is formulated for finding the design parameters of the fault detection scheme such that the set of guaranteed strongly detectable faults is maximized, where this set is defined based on the distance between the separated fault-free and faulty limit set.

The automatic preservation of the indoor air quality (IAQ) is an important task of the intelligent building design in order to ensure the health and safety of the occupants. The IAQ, however, is often compromised by various airborne contaminants that penetrate the indoor environment as a result of accidents or planned attacks. In this paper, we provide the detailed analysis, implementation, and evaluation of a distributed methodology for detecting and isolating multiple contaminant events in large-scale buildings. Specifically, we consider the building as a collection of interconnected subsystems, and we design a contaminant event monitoring software agent for each subsystem. Each monitoring agent aims to detect the contaminant and isolate the zone where the contaminant source is located, while it is allowed to exchange information with its neighboring agents. For configuring the subsystems, we implement both exact and heuristic partitioning solutions. A main contribution of this paper is the investigation of the impact of the partitioning solution on the performance of the distributed contaminant detection and isolation (CDI) scheme with respect to the detectability and isolability of the contaminant sources. The performance of the proposed distributed CDI methodology is demonstrated using the models of real building case studies created on CONTAM.^{1 1}CONTAM is a multizone simulation program developed by the U.S. National Institute of Standards and Technology.

This paper proposes a distributed fault-tolerant control (FTC) scheme that can preserve thermal comfort conditions in a multi-zone building despite the presences of faulty temperature sensors. The proposed methodology exploits the networked structure of a Heating, Ventilation and Air-Conditioning (HVAC) system controlling the temperature of physically interconnected zones in order to design a distributed FTC control scheme comprised of a set of dedicated control agents. For each control agent, two adaptive bounds on the tracking error are derived, taking into account: (i) healthy sensor measurements and (ii) a single sensor fault. Each adaptive bound constitutes a condition that allows the selection of an appropriate local control gain such that the thermal comfort conditions are satisfied. By utilizing the decisions of a sensor fault diagnosis scheme, the controller gain can be reconfigured to compensate the effects of sensor faults. The proposed methodology is illustrated by simulating a sensor fault in a 3-zone HVAC system.

This paper presents an optimization methodology for the design of an observer-based sensor fault detection scheme for a class of nonlinear systems. Taking into account bounded system disturbances and measurement noise, we design an observer aiming at maximizing the set of faults that are guaranteed to be strongly detectable. Strong fault detectability conditions are derived based on the limit sets that bound the residual under healthy and faulty conditions. A novel optimization method is designed based on the separation of the healthy and faulty limit sets. The distance between these sets represents the trade-off between robustness and sensor fault sensitivity. Simulation results are used to show the effectiveness of the proposed methodology applied to a simple example of a flexible link robot.

This paper presents a model-based methodology for diagnosing actuator and sensor faults affecting the temperature dynamics of a multi-zone heating, ventilating and air-conditioning (HVAC) system. By considering the temperature dynamics of the HVAC system as a network of interconnected subsystems, a distributed fault diagnosis architecture is proposed. For every subsystem, we design a monitoring agent that combines local and transmitted information from its neighboring agents in order to provide a decision on the type, number and location of the faults. The diagnosis process of each agent is realized in three steps. Firstly, the agent performs fault detection using a distributed nonlinear estimator. After the detection, the local fault identification is activated to infer the type of the fault using two distributed adaptive estimation schemes and a combinatorial decision logic. In order to distinguish between multiple local faults and propagated sensor faults, a distributed fault isolation is applied using the decisions of the neighboring agents. Simulation results of a 5-zone HVAC system are used to illustrate the effectiveness of the proposed methodology.

This tutorial investigates the problem of the occurrence of multiple faults in the sensors used to monitor and control a network of cyberphysical systems. The goal is to formulate a general methodology, which will be used for designing sensor fault diagnosis schemes with emphasis on the isolation of multiple sensor faults, and for analyzing the performance of these schemes with respect to the design parameters and system characteristics. The backbone of the proposed methodology is the design of several monitoring and aggregation cyber agents (modules) with specific properties and tasks. The monitoring agents check the healthy operation of sets of sensors and infer the occurrence of faults in these sensor sets based on structured robustness and sensitivity properties. These properties are obtained by deriving analytical redundancy relations of observer-based residuals sensitive to specific subsets of sensor faults, and adaptive thresholds that bound the residuals under healthy conditions, assuming bounded modeling uncertainty and measurement noise. The aggregation agents are employed to collect and process the decisions of the agents, while they apply diagnostic reasoning to isolate combinations of sensor faults that have possibly occurred. The design and performance analysis methodology is presented in the context of three different architectures: for cyber-physical systems that consist of a set of interconnected systems, a distributed architecture and a decentralized architecture, and for cyber-physical systems that are treated as monolithic, a centralized architecture. For all three architectures, the decomposition of the sensor set into subsets of sensors plays a key role in their ability to isolate multiple sensor faults. A discussion of the challenges and benefits of the three architectures is provided, based on the system scale, the type of system nonlinearities, the number of sensors and the communication needs. Lastly, this tutorial concludes with a discussion of open problems in fault diagnosis.

This paper presents a model-based distributed scheme with emphasis on the isolation of sensor faults in multi-zone heating, ventilating and air-conditioning (HVAC) systems. A bank of local sensor fault detection and isolation agents are designed to diagnose sensor faults in a HVAC system, modeled as a set of interconnected, nonlinear subsystems. Each agent consists of the local sensor fault detection and adaptive estimation scheme for isolation of sensor faults. Detection and isolation signals are generated based on analytical redundancy relations. These signals are provided to a local decision logic in order to distinguish between local and propagated sensor faults. Simulation results are used to illustrate the effectiveness of the proposed methodology applied to a four-zone HVAC system.

This paper presents the design and analysis of a methodology for detecting and isolating multiple sensor faults in large-scale interconnected nonlinear systems. The backbone of the proposed decentralized methodology is the design of a local sensor fault diagnosis agent dedicated to each interconnected subsystem, without the need to communicate with neighboring agents. Each local sensor fault diagnosis agent is responsible for detecting and isolating multiple faults in the local set of sensors. The local sensor fault diagnosis agent consists of a bank of modules that monitor smaller groups of sensors in the corresponding local sensor set. The detection of faults in each of the sensor groups is conducted using robust analytical redundancy relations, formulated by structured residuals and adaptive thresholds. The multiple sensor fault isolation in each local sensor fault diagnosis agent is realized by aggregating the decisions of the modules and applying a diagnostic reasoning-based decision logic. The performance of the proposed diagnostic scheme is analyzed with respect to sensor fault detectability and multiple sensor fault isolability. A simulation example of two interconnected robot manipulators is used to illustrate the application of the multiple sensor fault detection and isolation methodology.

This paper proposes a distributed methodology for detecting and isolating multiple sensor faults in interconnected cyberphysical systems. The distributed sensor fault detection and isolation process is conducted in the cybersuperstratum, in two levels. The first-level diagnosis is based on the design of monitoring agents, where every agent is dedicated to a corresponding interconnected subsystem. The monitoring agent is designed to isolate multiple sensor faults occurring in the sensor set of the physical part, while it is allowed to exchange information with its neighboring monitoring agents. The second-level diagnosis is realized by applying a global decision logic designed to isolate multiple sensor faults that may propagate in the cybersuperstratum through the exchange of information between monitoring agents. The decision-making process, executed in both levels of diagnosis, relies on a multiple sensor fault combinatorial logic and diagnostic reasoning. The performance of the proposed methodology is analyzed with respect to the sensor fault propagation effects and the distributed sensor fault detectability.

This paper presents a design and analysis methodology for detecting and isolating multiple sensor faults in heating, ventilation, and air-conditioning (HVAC) systems. The proposed methodology is developed in a distributed framework, considering a multizone HVAC system as a set of interconnected nonlinear subsystems. A dedicated local sensor fault diagnosis (LSFD) agent is designed for each subsystem, while it may exchange information with other LSFD agents. Distributed sensor fault detection is conducted using robust analytical redundancy relations of estimation-based residuals and adaptive thresholds. The distributed sensor fault isolation procedure is carried out by combining the decisions of the LSFD agents and applying a reasoning-based decision logic. The performance of the proposed methodology is analyzed with respect to robustness, sensor fault detectability, and isolability. Simulation results are used for illustrating the effectiveness of the proposed methodology applied to an eight-zone HVAC system.

This paper presents an adaptive approximation-based design methodology and analytical results for distributed detection and isolation of multiple sensor faults in a class of nonlinear uncertain systems. During the initial stage of the nonlinear system operation, adaptive approximation is used for online learning of the modeling uncertainty. Then, local sensor fault detection and isolation (SFDI) modules are designed using a dedicated nonlinear observer scheme. The multiple sensor fault isolation process is enhanced by deriving a combinatorial decision logic that integrates information from local SFDI modules. The performance of the proposed diagnostic scheme is analyzed in terms of conditions for ensuring fault detectability and isolability. A simulation example of a single-link robotic arm is used to illustrate the application of the adaptive approximation-based SFDI methodology and its effectiveness in detecting and isolating multiple sensor faults.

An intelligent building is required to provide safety to its occupants against any possible threat that may affect the indoor air quality, such as accidental or malicious airborne contaminant release in the building interior. In this work, we design a distributed methodology for detecting and isolating multiple contaminant events in a large-scale building. Specifically, we consider the building as a collection of interconnected subsystems and we design a contaminant event monitoring agent for each subsystem. Each monitoring agent aims to detect the contamination of the underlying subsystem and isolate the zone where the contaminant source is located, while it is allowed to exchange information with its neighboring agents. The decision logic implemented in the contaminant event monitoring agent is based on the generation of observerbased residuals and adaptive thresholds. We demonstrate our proposed formulation using a 14-zone building case study.

The dispersion of contaminants from sources (events) inside a building can compromise the indoor air quality and influence the occupants' comfort, health, productivity and safety. Such events could be the result of an accident, faulty equipment or a planned attack. Under these safety-critical conditions, immediate event detection should be guaranteed and the proper actions should be taken to ensure the safety of the people. In this paper, we consider an event as a fault in the process that disturbs the normal system operation. This places the problem of contaminant event monitoring in the fault diagnosis framework of detection and isolation. A main contribution of this work is the development of the state-space method, based on multi-zone building models, that enables the use of advanced fault diagnosis tools for contaminant event monitoring. Specifically, in this paper, we develop estimator schemes with adaptive thresholds for the detection and isolation of a single contaminant source under conditions of noise and modeling uncertainty. We demonstrate our proposed formulation using a 2-zone illustration example and a more realistic 14-zone building setting.

This paper presents the design of a methodology for diagnosing sensor faults in heating, ventilation and air-conditioning (HVAC) systems, and compensating their effects on the distributed control architecture. The proposed methodology is developed in a distributed framework, considering a multi-zone HVAC system as a set of interconnected, nonlinear subsystems. For each of the interconnected subsystems, we design a local virtual sensor agent that can detect and isolate faults in its monitored sensors and provide sensor fault estimations for correcting the faulty measurements. Adaptive estimation schemes are implemented in each local virtual sensor agent, using adaptive approximation models for learning the unknown fault function. Simulation results are used for illustrating the effectiveness of the proposed methodology applied to a two-zone HVAC system.

This paper presents the design of a methodology for detecting and isolating multiple sensor faults in large-scale interconnected nonlinear systems. For each of the interconnected subsystems, we design a local sensor fault diagnosis (LSFD) agent responsible for multiple sensor fault detection and isolation in the local sensor set. The multiple sensor fault detection is realized through a bank of modules, monitoring smaller groups of sensors that belong to the local sensor set. The detection of faults in sensor groups is conducted using robust analytical redundancy relations, formulated by structured residuals and adaptive thresholds. The isolation of multiple faulty sensors in the local sensor set is realized by integrating the decisions of the LSFD agent's modules and applying a reasoning-based combinatorial decision logic. The simulation example of an automated highway system is used to illustrate the application of the multiple SFDI methodology.

This paper presents a distributed methodology for detecting and isolating multiple sensor faults in interconnected nonlinear systems. For each of the interconnected subsystems, a corresponding Local Sensor Fault Diagnosis (LSFD) agent is designed, which is allowed to exchange information with neighboring LSFD agents. The decisions of the LSFD agents are integrated and processed by a Global Sensor Fault Diagnosis (GSFD) agent in order to isolate multiple sensor faults propagating between neighboring LSFD agents through information exchange. The combined local and global isolation decision logic is designed based on diagnostic reasoning, taking into account the effects of sensor faults in the local sensor set and the transmitted faulty sensor information on the agents' decisions.