Scalable Distributed Sensor Fault Diagnosis for Smart Buildings

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.      

Data-Driven Modeling, Fault Diagnosis and Optimal Sensor Selection for HVAC Chillers

Chillers constitute a significant portion of energy consumption equipment in heating, ventilating and air-conditioning (HVAC) systems. The growing complexity of building systems has become a major challenge for field technicians to troubleshoot the problems manually; this calls for automated ldquosmart-service systemsrdquo for performing fault detection and diagnosis (FDD). The focus of this paper is to develop a generic FDD scheme for centrifugal chillers and also to develop a nominal data-driven (ldquoblack-boxrdquo) model of the chiller that can predict the system response under new loading conditions. In this vein, support vector machines, principal component analysis, and partial least squares are the candidate fault classification techniques in our approach. We present a genetic algorithm-based approach to select a sensor suite for maximum diagnosabilty and also evaluated the performance of selected classification procedures with the optimized sensor suite. The responses of these selected sensors are predicted under new loading conditions using the nominal model developed via the black-box modeling approach. We used the benchmark data on a 90-t real centrifugal chiller test equipment, provided by the American Society of Heating, Refrigerating and Air-Conditioning Engineers, to demonstrate and validate our proposed diagnostic procedure. The database consists of data from sixty four monitored variables of the chiller under 27 different modes of operation during nominal and eight faulty conditions with different severities.     

Integrated operation planning: Intelligent system approach for HVAC processes

This paper describes the construction of an Intelligent System for Operation Planning (ISOP) in heating, ventilating, and air conditioning (HVAC) processes. The system contains important expertise, qualitative reasoning, and quantitative computation. It is used to assist or train operators to achieve better operation in HVAC systems. Expertise about operation planning is expressed as air enthalpy, and moisture conditions and air supply are considered as dynamic parameters. Therefore, it provides a real-time integrated operation planning method in HVAC processes. It offers better energy conservation, comfort and indoor air quality than other methods being currently used. ISOP consists of two levels of frames. The first level classifies HVAC systems by qualitatively reasoning the system structure information, and activates the subframe. In the second level, 16 frames that correspond to the HVAC system structure, accomplish indoor comfort setting, supply air parameter estimations, air enthalpy, and misture evaluation, and then recommend optimal operation conditions. An integrated distributed intelligent system framework is introduced to integrate qualitative reasoning and quantitative computation.     

Recursive Approximation of Complex Behaviours With IoT-Data Imperfections

This paper presents an approach to recursively estimate the simplest linear model that approximates the time-varying local behaviors from imperfect(noisy and incomplete) measurements in the internet of things(IoT) based distributed decision-making problems. We first show that the problem of finding the lowest order model for a multi-input single-output system is a cardinality(l0) optimization problem, known to be NP-hard.To solve the problem a simpler approach is proposed which uses the recently developed atomic norm concept and the modified Frank-Wolfe(mFW) algorithm is introduced. Further, the paper computes the minimum data-rate required for computing the models with imperfect measurements. The proposed approach is illustrated on a building heating, ventilation, and air-conditioning(HVAC) control system that aims at optimizing energy consumption in commercial buildings using IoT devices in a distributed manner. The HVAC control application requires recursive thermal dynamical model updates due to frequently changing conditions and non-linear dynamics. We show that the method proposed in this paper can approximate such complex dynamics on single-board computers interfaced to sensors using unreliable communication channels. Real-time experiments on HVAC systems and simulation studies are used to illustrate the proposed method.     

基于人体热舒适性指标PMV的暖通空调控制器

分析了人类对暖通空调系统的要求,介绍了暖通空调控制的现状,提出了一种新的基于人体热舒适性指标PMV的暖通空调控制器,该控制器能满足人类对暖通空调系统健康、舒适和节能的要求,是一种理想的暖通空调控制器。     

Extending the information delivery manual approach to identify information requirements for performance analysis of HVAC systems

Heating, Ventilation and Air-Conditioning (HVAC) systems account for more than 15% of the total energy consumption in the US. In order to improve the energy efficiency of HVAC systems, researchers have developed hundreds of algorithms to automatically analyze their performance. However, the complex information, such as configurations of HVAC systems, layouts and materials of building elements and dynamic data from the control systems, required by these algorithms inhibits the process of deploying them in real-world facilities. To address this challenge, we envision a framework that automatically integrates the required information items and provides them to the performance analysis algorithms for HVAC systems. This paper presents an approach to identify and document the information requirements from the publications that describe these algorithms. We extend the Information Delivery Manual (IDM) approach so that the identified information requirements can be mapped to multiple information sources that use various formats and schemas. This paper presents the extensions to the IDM approach and the results of using it to identify information requirements for performance analysis algorithms of HVAC systems.     

A statistically based fault detection and diagnosis approach for non-residential building water distribution systems

Large non-residential buildings can contain complex and often inefficient water distribution systems. As requirements for water increase due to water scarcity and industrialization, it has become increasingly important to effectively detect and diagnose faults in water distribution systems in large buildings. In many cases, if water supply is not impacted, faults in water distribution systems can go unnoticed. This can lead to unnecessary increases in water usage and associated energy due to pumping, treating, and heating water. The majority of fault detection and diagnosis studies in the water sector are limited to municipal water supply and leakage detection. The application of detection and diagnosis for faults in building water networks remains largely unexplored and the ability to identify and distinguish between routine and non-routine water usage at this scale remains a challenge. This study using case-study data, presents the application of principal component analysis and a multi-class support vector machine to detect and classify faults for non-residential building water networks. In the absence of a process model (which is typical for such water distribution systems), principal component analysis is proposed as a data-driven fault detection technique for building water distribution systems for the first time herein. Hotelling T²-statistics and Q-statistics were employed to detect abnormality within incoming data, and a multi-class support vector machine was trained for fault classification. Despite the relatively limited training data available from the case-study (which would reflect the situation in many buildings), meaningful faults were detected, and the technique proved successful in discriminating between various types of faults in the water distribution system. The effectiveness of the proposed approach is compared to a univariate threshold technique by comparison of their respective performance in the detection of faults that occurred in the case-study site. The results demonstrate the promising capabilities of the proposed fault detection and diagnosis approach. Such a strategy could provide a robust methodology that can be applied to buildings to reduce inefficient water use, reducing their life-cycle carbon footprint.     

Unsupervised feature selection using swarm intelligence and consensus clustering for automatic fault detection and diagnosis in Heating Ventilation and Air Conditioning systems

Various sensory and control signals in a Heating Ventilation and Air Conditioning (HVAC) system are closely interrelated which give rise to severe redundancies between original signals. These redundancies may cripple the generalization capability of an automatic fault detection and diagnosis (AFDD) algorithm. This paper proposes an unsupervised feature selection approach and its application to AFDD in a HVAC system. Using Ensemble Rapid Centroid Estimation (ERCE), the important features are automatically selected from original measurements based on the relative entropy between the low- and high-frequency features. The materials used is the experimental HVAC fault data from the ASHRAE-1312-RP datasets containing a total of 49 days of various types of faults and corresponding severity. The features selected using ERCE (Median normalized mutual information (NMI) = 0.019) achieved the least redundancies compared to those selected using manual selection (Median NMI = 0.0199) Complete Linkage (Median NMI = 0.1305), Evidence Accumulation K-means (Median NMI = 0.04) and Weighted Evidence Accumulation K-means (Median NMI = 0.048). The effectiveness of the feature selection method is further investigated using two well-established time-sequence classification algorithms: (a) Nonlinear Auto-Regressive Neural Network with eXogenous inputs and distributed time delays (NARX-TDNN); and (b) Hidden Markov Models (HMM); where weighted average sensitivity and specificity of: (a) higher than 99% and 96% for NARX-TDNN; and (b) higher than 98% and 86% for HMM is observed. The proposed feature selection algorithm could potentially be applied to other model-based systems to improve the fault detection performance.     

基于KPCA的HVAC系统传感器故障诊断

传感器状态的好坏很大程度上影响暖通空调(HVAC)系统的运行,对其展开故障诊断十分必要。核主成分分析(KPCA)方法通过集成算子与非线性核函数计算高维特性空间的主元成分,有效捕捉过程变量中的非线性关系,将其用于传感器常见4种故障的诊断,先用Q统计量进行故障监测,再用T2贡献量百分比变化来识别故障。实验结果表明:KPCA方法具有很好的故障监测与诊断能力。     

Low computational cost technique for predictive management of thermal comfort in non-residential buildings

In France, non-residential buildings account for a significant part of energy consumption. A large part of this consumption is due to HVAC (Heating, Ventilation and Air-Conditioning) systems, which are in most cases poorly handled. The present work deals with an efficient approach allowing energy consumption to be minimized while still ensuring thermal comfort. We propose a predictive control strategy for existing zoned HVAC systems and consider the PMV (Predicted Mean Vote) index as a thermal comfort indicator. In order to test this strategy, we modelled a non-residential building located in Perpignan (south of France) using the EnergyPlus software. The twofold aim is to limit the times during which the HVAC sub-systems are turned on and to ensure a satisfactory thermal comfort when people are working in the considered building. This predictive approach, computationally tractable, allows thermal comfort requirements to be met without wasting energy.     

Energy efficient control of HVAC systems with ice cold thermal energy storage

In heating, ventilation and air conditioning (HVAC) systems of medium/high cooling capacity, energy demands can be matched with the help of thermal energy storage (TES) systems. If properly designed, TES systems can reduce energy costs and consumption, equipment size and pollutant emissions. In order to design efficient control strategies for TES systems, we present a model-based approach with the aim of increasing the performance of HVAC systems with ice cold thermal energy storage (CTES). A simulation environment based on Matlab/Simulink^® is developed, where thermal behaviour of the plant is analysed by a lumped formulation of the conservation equations. In particular, the ice CTES is modelled as a hybrid system, where the water phase transitions (solid–melting–liquid and liquid–freezing–solid) are described by combining continuous and discrete dynamics, thus considering both latent and sensible heat. Standard control strategies are compared with a non-linear model predictive control (NLMPC) approach. In the simulation examples model predictive control proves to be the best control solution for the efficient management of ice CTES systems.     

空调系统的能耗诊断与故障自动检测研究

对空调系统的能耗诊断及故障诊断,本着科学、合理、实用的原则,提出了一种用于指导空调系统能耗诊断与故障诊断的方法体系。此方法体系的建立可以定性、定量地评价空调系统的实际运行特性,从而提高系统的综合特性。     

General modeling for model-based FDD on building HVAC system

Fault detection and diagnosis (FDD) can be realized with models. It can be used to find the cause of the degradation on energy efficiency and indoor climate quality in building heating, ventilation and air-conditioning (HVAC) systems. Real buildings are diverse. It requires a general modeling method. General modeling concept comprises three steps: hierarchical modeling procedure, parameterization and tuning procedure. In the procedure of hierarchical modeling, the process is split into levels ranging from global to micro level. The objective is to detect faults on the various levels. Consequently, different models for each level should be built. It is important to increase the accuracy of the models and let the models applicable for FDD on building HVAC systems. Parameterization and tuning procedure are necessary. An example model for a real air-conditioned room shows the results of general models and the results after tuning.     

Fuzzy-genetic algorithm for automatic fault detection in HVAC systems

Detecting fault before it deteriorates the system performance is crucial for the reliability and safety of many engineering systems. This paper develops an intelligent technique based on fuzzy-genetic algorithm (FGA) for automatically detecting faults on HVAC system. Many researchers have proposed only using fuzzy systems to effect fault detection and diagnosis. Other applications of the FGA are mainly focused on the synthesis of fuzzy control rules. The proposed automatic fault detection system (AFD) monitors the HVAC system states continuously by fuzzy system. The optimization capability of genetic algorithms allows the generation of optimal fuzzy rules. Faults are represented as different fault levels in the AFD system and are distinguished by fuzzy system after tuning its rule table. Simulation studies are conducted to verify the proposed AFD system for the single zone air handler system.     

Learning-Based Switched Reliable Control of Cyber-Physical Systems With Intermittent Communication Faults

This study deals with reliable control problems in data-driven cyber-physical systems(CPSs) with intermittent communication faults, where the faults may be caused by bad or broken communication devices and/or cyber attackers. To solve them, a watermark-based anomaly detector is proposed, where the faults are divided to be either detectable or undetectable.Secondly, the fault's intermittent characteristic is described by the average dwell-time(ADT)-like concept, and then the reliable control issues, under the undetectable faults to the detector, are converted into stabilization issues of switched systems. Furthermore,based on the identifier-critic-structure learning algorithm, a datadriven switched controller with a prescribed-performance-based switching law is proposed, and by the ADT approach, a tolerated fault set is given. Additionally, it is shown that the presented switching laws can improve the system performance degradation in asynchronous intervals, where the degradation is caused by the fault-maker-triggered switching rule, which is unknown for CPS operators. Finally, an illustrative example validates the proposed method.     

UIO design for singular delayed LPV systems with application to actuator fault detection and isolation

In this paper, the unknown input observer (UIO) design for singular delayed linear parameter varying (LPV) systems is considered regarding its application to actuator fault detection and isolation. The design procedure assumes that the LPV system is represented in the polytopic framework. Existence and convergence conditions for the UIO are established. The design procedure is formulated by means of linear matrix inequalities (LMIs). Actuator fault detection and isolation is based on using the UIO approach for designing a residual generator that is completely decoupled from unknown inputs and exclusively sensitive to faults. Fault isolation is addressed considering two different strategies: dedicated and generalised bank of observers’ schemes. The applicability of these two schemes for the fault isolation is discussed. An open flow canal system is considered as a case study to illustrate the performance and usefulness of the proposed fault detection and isolation method in different fault scenarios.     

A novel control solution for improved trajectory tracking and LVRT performance in DFIG-based wind turbines

This paper presents a new control strategy for the rotor side converter of Doubly-Fed Induction Generator based Wind Turbine systems, under severe voltage dips. The main goal is to fulfill the Low Voltage Ride Through performance, required by modern grid codes. In this respect, the key point is to limit oscillations (particularly on rotor currents) triggered by line faults, so that the system keeps operating with graceful behavior. To this aim, a suitable feedforward-feedback control solution is proposed for the DFIG rotor side. The feedforward part exploits oscillation-free reference trajectories, analytically derived for the system internal dynamics. State feedback, designed accounting for control voltage limits, endows the system with robustness and further tame oscillations during faults. Moreover, improved torque and stator reactive power tracking during faults is achieved, proposing an exact mapping between such quantities and rotor-side currents, which are conventionally used as controlled outputs. Numerical simulations are provided to validate the capability of the proposed approach to effectively cope with harsh faults.     

基于事件驱动深度强化学习的建筑热舒适控制

住宅暖通空调系统通常耗用大量能源,同时也极大地影响居住者的热舒适性。目前,强化学习广泛应用于优化暖通空调系统,然而这一方法需要投入大量时间和数据资源。为了解决该问题,提出了一个新的基于事件驱动的马尔可夫决策过程（event-driven Markov decision process,ED-MDP）框架,并在此基础上,提出了基于事件驱动的深度确定性策略梯度（event-driven deep deterministic policy gradient,ED-DDPG）方法,通过事件触发优化控制,结合强化学习算法求解最优控制策略。实验结果显示,与基准方法相比,ED-DDPG在提升学习速度和减少决策频率方面表现出色,并在节能和维持热舒适方面取得了显著成果。经过实验验证,该方法在优化住宅暖通空调控制方面展现出强大的鲁棒性和适应性。     

Deep learning forecasting for electric demand applications of cooling systems in buildings

This paper presents the application of a deep learning based model for the short-term forecasting of the electric demand in a heating, ventilation, and air conditioning system (HVAC) for the demand response programs of utility companies. The deep learning model is applied through two different approaches comparing their merits. The approaches consist of: (i) a monolithic approach that applies a single large model to forecast the target variables, and (ii) a sequential approach that consists of multiple deep learning models coupled together each targeting a specific energy load within the HVAC system. The model accuracy of both approaches is explored over two case studies applied to the same institutional building; however, the case studies differ in their data source. The first case study uses synthetic data obtained from an eQuest simulation, while the second case study uses measurement data obtained from the building automation system. Results show that the difference in forecasting error of these approaches is negligible; however, the monolithic approach required the least amount of calibration time. Next, this paper explores the application of off-site weather data applied to a building model calibrated with on-site data. The experiments demonstrated that the off-site weather data can be applied with a slight reduction in forecasting performance.     

False alarm moderation for performance monitoring in industrial water distribution systems

While considerable attention has been given to data driven methods that analyse and control energy systems in buildings, the same cannot be said for building water systems. As a result, approaches which support enhanced efficiency in building water consumption are somewhat underdeveloped, particularly in industrial settings. Water consumption in industrial systems features non-stationarity (i.e., variations in statistical properties over time), making it challenging to distinguish between routine and non-routine water uses. In such scenarios, fault detection and diagnosis methods that leverage multivariate statistical process control with, for example, principal component analysis and detection indices (Hotelling T²-statistics and Q-statistics), can be successfully used to identify system alarms. However, even with these approaches there can be a high prevalence of false alarms leading to low industry uptake of fault detection and diagnosis systems, or where in place, alarms can be ignored. To efficiently detect and diagnose water distribution system faults, false alarms should be controlled through false alarm moderation approaches so that building managers/operators only need to focus on critical system alarms or system alarms with high risk levels. This paper utilises two statistical non-parametric false alarm moderation approaches (window-based, and trial-based) that generate a second control limit for T²-statistics and Q-statistics. The implementation of these false alarm moderation approaches was combined with principal component analysis to detect faults with real water time series data from two case-study sites. Using both approaches false alarms were reduced, and the overall performance and reliability of the fault detection and diagnosis approach was improved. The principal component analysis model with the window-based approach was shown to be particularly effective.