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.@en

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.@en

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.@en

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.@en

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.@en

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.@en

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.@en

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.@en

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.@en

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.@en

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.@en

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.@en

This paper presents the design of a methodology for distributed detection and isolation of multiple sensor faults in heating, ventilation and air-conditioning (HVAC) systems. The proposed methodology is developed in a distributed framework with the HVAC system modeled as a set of interconnected, nonlinear subsystems. A local sensor fault diagnosis (LSFD) agent is designed for each of the interconnected subsystems. The LSFD agent uses input and sensor output data of its underlying subsystem and it may exchange information with the neighboring agents. The distributed sensor fault detection is conducted using robust analytical redundancy relations, formulated by estimation-based residuals and adaptive thresholds. The distributed sensor fault isolation is carried out by combining the decisions of the LSFD agents and applying a reasoning-based decision logic. Simulation results are used for illustrating the effectiveness of the proposed methodology applied to a two-zone HVAC system.@en

This paper presents a model-based distributed fault-tolerant control (FTC) scheme with emphasis on compensating the effects of sensor faults in multi-zone heating, ventilating and air-conditioning (HVAC) systems. A bank of local adaptive FTC agents are designed to accommodate possible sensor faults in HVAC systems, modeled as a set of interconnected, nonlinear subsystems. In order to compensate the fault effects that may propagate in the neighboring subsystems, each local FTC utilizes the information generated by a local monitoring agent that diagnoses the sensor faults and performs an adaptive estimation of the isolated sensor faults. The local monitoring and control agents are allowed to exchange information with neighboring agents. Simulation results are used to illustrate the effectiveness of the proposed methodology applied to a seven-zone HVAC system.@en