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Diagnosis of Actuator Faults in VAV-HVAC system using a Bilinear Observer
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Matlab/Simulink is known in a large number of fields as a powerful and modern simulation tool. In the field of building and HVAC simulation its use is also increasing. However, it is still believed to be a tool for small applications due to its graphical structure and not to fit well for the simulation of multizone buildings. This paper presents the development of a new multizone building model for Matlab/Simulink environment, implemented into the SIMBAD Building and HVAC Toolbox. It's general enough to model a variety of useful cases. Conforming to the Simulink philosophy, the model is modular and structured into blocks to represent the modelled phenomena. To simplify the description of the simulated building, a graphical user interface SIMBDI is developed in parallel, generating an xml building description file. This file can be read directly by the SIMBAD multizone building model. Finally, a simulation case is presented in order to compare the new model with the Trnsys multizone building model.
The book introduces novel algorithms for designing fault-tolerant control (FTC) systems using the behavioral system theoretic approach, and presents a demonstration of successful novel FTC mechanisms on several benchmark examples. The authors also discuss a new transient management scheme, which is an essential requirement for the implementation of active FTC systems, and two data-driven methodologies that are broadly classified as active FTC systems: the projection-based approach and the online-redesign approach. These algorithms do not require much a priori information about the plant in real-time, and in addition this novel implementation of active FTC systems circumvents various weaknesses induced by using a diagnostic module in real-time. The book provides graduate students taking masters and doctoral courses in mathematics, control, and electrical engineering an excellent stepping-stone for their research. It also appeals to practitioners interested to apply innovative fail-safe control techniques.
This work presents an integrated framework for fault detection and isolation (FDI) and fault tolerant control (FTC) of variable air volume (VAV) boxes, a common component of heating, ventilation and air conditioning (HVAC) systems. To this end, first a statistical model based FDI framework is designed using existing techniques such as principal component analysis (PCA) and joint angle analysis as a benchmark for comparison. Then a novel linear causal model based framework for FDI of multiple actuator and multiple sensor faults is designed and implemented and shown to possess superior FDI capabilities compared to the statistical model based framework. Finally, a safe parking strategy is designed and the ensuing energy savings for the case of stuck dampers demonstrated.
This paper presents a critical review of the automated on-going commissioning (AOGC) methods for air-handling units (AHU) and variable air volume terminal (VAV) units in commercial buildings. The common faults studied in the literature were identified. The diagnostic approaches taken and the characteristics of the fault-symptom datasets utilized were categorized. It was found that the diagnostics methods were vastly fragmented, and most of them employed pure-statistical approaches. Only a few studies attempted to assimilate the automation data within the underlying physical processes. In addition, a large fraction of the reviewed literature has been devoted to physical faults in AHUs. Only a few studies were conducted to diagnose faults-related with controls programming and faults at the zone level. Upon the literature survey findings, an inverse greybox modelling-based AOGC approach was put forward. Its strengths and weaknesses were demonstrated through a case study conducted using the archived building automation system (BAS) data of an office building in Ottawa, Canada. The results of this case study indicate that inverse greybox modelling-based AOGC is a promising method to diagnose both physical and controls programming related faults at AHUs and VAVs.
In a heating, ventilation, and air conditioning system, an air-handling system is a key module. Its components (e.g., air handling unit, air-mixing box, and fans), linked through airflows, condition air to a desired temperature and/or humidity based on comfort or controlled environment requirements. Identifying failure modes and estimating their severities allow maintenance crews to know which faults have occurred, how critical they are, and be guided in the repair process to improve the system availability. The problem of fault detection and diagnosis in air-handling systems is complex because of fault propagation across components, and high false alarm rates caused by uncertainties in system and measurement dynamics. In this paper, to capture fault propagation impacts in an efficient manner, dynamic hidden Markov models are developed to identify failure modes, since they contain state transition matrices depending on other components and do not generate joint states. To filter out false alarms, "coupled statistical process control" techniques are developed by using state transitions matrices representing coupling among components. Experimental results show that the method can effectively diagnose faults with high-diagnosis accuracy.
Variable air volume (VAV) boxes are used in multi-zone buildings, which varies the volume of constant temperature air separately supplied by an air-handling unit to meet challenging thermal load demands of individual zones. A fault appearing in the VAV damper severely affects the control performance and consequently, the energy efficiency of the overall system. To address this issue, we present a novel online redesign based fault-tolerant control strategy in this paper for a VAV thermal system in a multi-zone building. The novelty of the proposed strategy is that no a-priori information about the process is used in real-time. In fact, no online identification of the thermal dynamical model of the building process or estimation of its operating status (healthy or faulty) is done. The design of the fault accommodation scheme is solely based on the real-time trajectories generated by the process. In order to demonstrate the effectiveness of the developed strategy, we consider a case-study of three-zones in a one-story building.
This paper presents an automatic commissioning tool based on a FDD (fault detection and diagnosis) strategy for typical VAV (variable air volume) air distribution systems. The framework of the FDD strategy is established based on system knowledge, qualitative states and object-oriented SPC (statistical process control) models. Eight FDD schemes are set up to detect the eleven VAV root faults within the qualitative/quantitative FDD framework to form the FDD strategy consisting of two steps. Ten faults, which would affect the system operation, are detected at the first step. The eleventh fault, which would not affect the basic system operation but would lead to imperfection under advanced supervisory control, is detected at the second step. The FDD strategy is tested and validated on typical VAV air-conditioning systems involving multiple faults both in simulation and in-situ tests. A software package is developed as a BMS-assisted automatic commissioning tool.
Automatic fault detection and diagnosis (FDD) can help enhance building energy efficiency by facilitating early detection of occurrence of system faults, especially those of air-conditioning systems, thus enabling rectification of the faults before much energy is wasted due to such faults. However, building owners may not invest in FDD unless they are convinced of the energy cost savings that can be achieved. This paper presents the results of a study on the energy cost impacts of a range of common system faults in variable air volume (VAV) air-conditioning systems, which are widely adopted for their good part-load energy efficiency. The faults studied include room air temperature sensor offset, stuck VAV box damper, supply air temperature sensor offset, stuck outdoor air damper and stuck/leaking cooling coil valve. The simulation results indicate that some faults may significantly increase energy use in buildings, for example, negative room air temperature sensor offset, stuck open VAV box damper, negative supply air temperature sensor offset, stuck open outdoor air damper and stuck open and leaking cooling coil valve. Since building occupants may adapt to the symptoms of these faults, such as reduced room air temperature, and thus may not complain about them, the occurrence of such faults are not immediately apparent unless a FDD system is available. Some other faults, e.g. positive supply air temperature sensor offset, positive room air temperature sensor offset, stuck closed cooling coil valve and stuck closed VAV box damper, may allow less energy to be used but will lead to unbearable indoor environmental conditions, such as high indoor temperature. Such faults, therefore, can easily be detected even without a FDD system, as there will be feedback from the building occupants.
An air handling unit’s energy usage can vary from the original design as components fail or fault – dampers leak or fail to open/close, valves get stuck, and so on. Such problems do not necessarily result in occupant complaints and, consequently, are not even recognized to have occurred. In spite of recent progress in the research and development of diagnostic solutions for air handling units, there is still a lack of reliable, scalable, and affordable diagnostic solutions for such systems. Modeling limitations, measurement constraints, and the complexity of concurrent faults are the main challenges in air handling unit diagnostics. The focus of this paper is on developing diagnostic algorithms for air handling units that can address such constraints more effectively by systematically employing machine-learning techniques. The proposed algorithms are based on analyzing the observed behavior of the system and comparing it with a set of behavioral patterns generated based on various faulty conditions. We show how such a pattern-matching problem can be formulated as an estimation of the posterior distribution of a Bayesian probabilistic model. We demonstrate the effectiveness of the approach by detecting faults in commercial building air handling units.
Detection and diagnosis of multiple faults in variable air volume (VAV) systems using principal component analysis (PCA) and joint angle analysis (JAA) are presented. Multi-level principal component analysis models including system- and local-level are built to detect the multiple faults occurred in VAV systems simultaneously. As the initial detection, system-level principal component analysis model is used to discover the abnormities in view of the whole systems. And two local-level principal component analysis models are used to further confirm the occurrence of the faults. With the multiple faults separated into different locations, joint angle analysis is used to isolate the faults one by one according to corresponding local models.
This paper presents the results of a site survey study on the faults in variable air volume (VAV) terminals and an automatic fault detection and diagnosis (FDD) strategy for VAV air-conditioning systems using a hybrid approach. The site survey study was conducted in a commercial building. 20.9% VAV terminals were ineffective and 10 main faults were identified in the VAV air-conditioning systems. The FDD strategy adopts a hybrid approach utilizing expert rules, performance indexes and statistical process control models to address these faults. Supported by a pattern recognition method, expert rules and performance indexes based on system physical characteristics are adopted to detect 9 of the 10 faults. Two pattern recognition indexes are introduced for fault isolation to overcome the difficulty in differentiating damper sticking and hysteresis from improper controller tuning. A principal component analysis (PCA)-based method is developed to detect VAV terminal flow sensor biases and to reconstruct the faulty sensors. The FDD strategy is tested and validated on typical VAV air-conditioning systems involving multiple faults both in simulation and in situ tests.
In this paper, a model based fault detection and isolation (FDI) scheme with online fault learning capabilities is proposed for HVAC systems. An observer comprising of an online approximator in discrete-time (OLAD) and a robust term is used for detection. A fault is detected if the generated detection residual, which is defined as the error between the observer outputs and HVAC system states, exceeds an apriori chosen threshold. The OLAD term in the FD observer learns the fault dynamics online while the robust term guarantees asymptotic estimation of the system states. Subsequent to detection, a fault isolation observer, which comprises of the model of fault functions and another robust term, is initiated to identify the root cause. A fault is identified if the isolation residual converges to zero, where the residual is obtained by comparing outputs of the isolation observer and the system. Additionally, we consider different fault scenarios in the system such as single or simultaneous multiple faults. Analytical results for the FDI scheme guarantee the robustness and stability of the proposed scheme. Finally, a simulation example is used to demonstrate the proposed FDI scheme.
Distributed diagnosis of actuator and sensor faults in hvac systems
- P M Papadopoulos
- V Reppa
- M M Polycarpou
- C G Panayiotou
Distributed diagnosis of actuator and sensor faults in hvac systems
- papadopoulos