Fault Detection and Diagnosis Encyclopedia for Building Systems:A Systematic Review

This review aims to provide an up-to-date, comprehensive, and systematic summary of fault detection and diagnosis (FDD) in building systems. The latter was performed through a defined systematic methodology with the final selection of 221 studies. This review provides insights into four topics: (1) glossary framework of the FDD processes; (2) a classification scheme using energy system terminologies as the starting point; (3) the data, code, and performance evaluation metrics used in the reviewed literature; and (4) future research outlooks. FDD is a known and well-developed field in the aerospace, energy, and automotive sector. Nevertheless, this study found that FDD for building systems is still at an early stage worldwide. This was evident through the ongoing development of algorithms for detecting and diagnosing faults in building systems and the inconsistent use of the terminologies and definitions. In addition, there was an apparent lack of data statements in the reviewed articles, which compromised the reproducibility, and thus the practical development in this field. Furthermore, as data drove the research activity, the found dataset repositories and open code are also presented in this review. Finally, all data and documentation presented in this review are open and available in a GitHub repository

Challenges in using operational data for reliable wind turbine condition monitoring

Operational data of wind turbines recorded by the Supervisory Control And Data Acquisition (SCADA) system originally intended only for operation and performance monitoring show promise also for assessing the health of the turbines. Using these data for monitoring mechanical components, in particular the drivetrain subassembly with gearbox and bearings, has recently been investigated with multiple techniques. In this paper the advantages and drawbacks of suggested approaches as well as general challenges and limitations are discussed focusing on automated and farm-wide condition monitoring

Visualised inspection system for monitoring environmental anomalies during daily operation and maintenance

PurposeVisual inspection and human judgement form the cornerstone of daily operations and maintenance (O&M) services activities carried out by facility managers nowadays. Recent advances in technologies such as building information modelling (BIM), distributed sensor networks, augmented reality (AR) technologies and digital twins present an immense opportunity to radically improve the way daily O&M is conducted. This paper aims to describe the development of an AR-supported automated environmental anomaly detection and fault isolation method to assist facility managers in addressing problems that affect building occupants’ thermal comfort.Design/methodology/approachThe developed system focusses on the detection of environmental anomalies related to the thermal comfort of occupants within a building. The performance of three anomaly detection algorithms in terms of their ability to detect indoor temperature anomalies is compared. Based on the fault tree analysis (FTA), a decision-making tree is developed to assist facility management (FM) professionals in identifying corresponding failed assets according to the detected anomalous symptoms. The AR system facilitates easy maintenance by highlighting the failed assets hidden behind walls/ceilings on site to the maintenance personnel. The system can thus provide enhanced support to facility managers in their daily O&M activities such as inspection, recording, communication and verification.FindingsTaking the indoor temperature inspection as an example, the case study demonstrates that the O&M management process can be improved using the proposed AR-enhanced inspection system. Comparative analysis of different anomaly detection algorithms reveals that the binary segmentation-based change point detection is effective and efficient in identifying temperature anomalies. The decision-making tree supported by FTA helps formalise the linkage between temperature issues and the corresponding failed assets. Finally, the AR-based model enhanced the maintenance process by visualising and highlighting the hidden failed assets to the maintenance personnel on site.Originality/valueThe originality lies in bringing together the advances in augmented reality, digital twins and data-driven decision-making to support the daily O&M management activities. In particular, the paper presents a novel binary segmentation-based change point detection for identifying temperature anomalous symptoms, a decision-making tree for matching the symptoms to the failed assets, and an AR system for visualising those assets with related information.EPSRC, Innovate U

A Novel Damage Index for Online Monitoring of RC Slabs under Monotonic Loading by Integration of Process Controlling into Acoustic Emission Technique

This study introduces a novel structural health monitoring scheme for cementitious composite slabs with the aid of acoustic emission (AE) technique coupled with statistical process controlling (SPC) method. The adopted framework is an integrated monitoring solution that effectively relates current state (damaged) to reference state of the structure. Evaluation of the latter was made possible using autoregressive model incorporating a set of damage-sensitive feature. In order to provide a benchmark damage indicator, the collected data were processed using control chart analysis. The damage indicators for the former was similarly obtained and then compared with the benchmark to gauge the structural damage. These control charts offer a robust framework meticulously identifying inconsistency in the damage-sensitive feature imposed over the monitoring period. Linear and quadratic projections were also incorporated into SPC model to enhance identification of system transition to other damage states

Exploiting Robust Multivariate Statistics and Data Driven Techniques for Prognosis and Health Management

This thesis explores state of the art robust multivariate statistical methods and data driven techniques to holistically perform prognostics and health management (PHM). This provides a means to enable the early detection, diagnosis and prognosis of future asset failures. In this thesis, the developed PHM methodology is applied to wind turbine drive train components, specifically focussed on planetary gearbox bearings and gears. A novel methodology for the identification of relevant time-domain statistical features based upon robust statistical process control charts is presented for high frequency bearing accelerometer data. In total, 28 time-domain statistical features were evaluated for their capabilities as leading indicators of degradation. The results of this analysis describe the extensible multivariate “Moments’ model” for the encapsulation of bearing operational behaviour. This is presented, enabling the early degradation of detection, predictive diagnostics and estimation of remaining useful life (RUL). Following this, an extended physics of failure model based upon low frequency SCADA data for the quantification of wind turbine gearbox condition is described. This extends the state of the art, whilst defining robust performance charts for quantifying component condition. Normalisation against loading of the turbine and transient states based upon empirical data is performed in the bivariate domain, with extensibility into the multivariate domain if necessary. Prognosis of asset condition is found to be possible with the assistance of artificial neural networks in order to provide business intelligence to the planning and scheduling of effective maintenance actions. These multivariate condition models are explored with multivariate distance and similarity metrics for to exploit traditional data mining techniques for tacit knowledge extraction, ensemble diagnosis and prognosis. Estimation of bearing remaining useful life is found to be possible, with the derived technique correlating strongly to bearing life (r = .96

Energy Optimizations for Smart Buildings and Smart Grids

Modern buildings are heavy power consumers. For instance, of the total electricity consumed in the US, 75% is consumed in the residential and commercial buildings. This consumption is not evenly distributed over time. Typical consumption profile exhibits several peaks and troughs. The peakiness, in turn, dictates the electric grid\u27s generation, transmission and distribution costs, and also the associated carbon emissions. This thesis discusses challenges involved in achieving the sustainability goals in buildings and electric grids. It investigates building and grid energy footprint optimization techniques to achieve the following goals: 1) making buildings energy efficient, 2) cutting building\u27s electricity bills, 3) cutting utility\u27s costs in electricity generation and distribution, 4) reducing carbon footprints, and 5) making the aggregate electricity consumption profile grid-friendly. In this thesis, we first design SmartCap, a system to enable homes flatten their consumption/demand by scheduling background loads (such as A/Cs, refrigerator), without causing user discomfort and without direct user involvement. Demand flattening facilitates aggregate peak reduction, which in turn enables grids to 1) reduce carbon emissions, and 2) cut installation and operational costs. Our results demonstrate that SmartCap can decrease the average deviation from mean power by over 20% across all periods with high deviation, thereby flattening the peaky demand. Next, we present SmartCharge, an intelligent battery charging system that shifts a building\u27s electricity consumption to off-peak periods by storing low-cost energy for use during high-cost periods, without active user involvement. We show that SmartCharge can typically save 10-15% in bills and can reduce the grid-wide peak demand by up to 20%. We then extend SmartCharge to GreenCharge, which integrates on-site renewables in a building\u27s electricity consumption. Our experiments show that GreenCharge can cut user electricity bills up to 20%. After GreenCharge, we investigate the use of large-scale distributed energy storage at buildings throughout the grid to flatten grid demand, while 1) maintaining end-user incentives for storage adoption at grid-scale, and 2) ensuring grid stability. We design PeakCharge, an online peak-aware charging algorithm to optimize the use of energy storage in the presence of a peak demand surcharge. Empirical evaluations show that total storage capacity required by PeakCharge to flatten grid demand is within 18% of the capacity required by a centralized system. Finally, we examine the efficacy of employing different combinations of energy storage technologies at different levels of the grid’s distribution hierarchy to cut electric utility\u27s daily operational costs. We present an optimization framework for modeling the primary characteristics of various energy storage technologies and important tradeoffs in placing different storage technologies at different levels of the distribution hierarchy. We show that by employing hybrid storage technologies at multiple levels of the distribution hierarchy, utilities can reduce their daily operating costs due to distributing electricity by up to 12%

Fault Diagnosis Of Sensor And Actuator Faults In Multi-Zone Hvac Systems

Globally, the buildings sector accounts for 30% of the energy consumption and more than 55% of the electricity demand. Specifically, the Heating, Ventilation, and Air Conditioning (HVAC) system is the most extensively operated component and it is responsible alone for 40% of the final building energy usage. HVAC systems are used to provide healthy and comfortable indoor conditions, and their main objective is to maintain the thermal comfort of occupants with minimum energy usage. HVAC systems include a considerable number of sensors, controlled actuators, and other components. They are at risk of malfunctioning or failure resulting in reduced efficiency, potential interference with the execution of supervision schemes, and equipment deterioration. Hence, Fault Diagnosis (FD) of HVAC systems is essential to improve their reliability, efficiency, and performance, and to provide preventive maintenance. In this thesis work, two neural network-based methods are proposed for sensor and actuator faults in a 3-zone HVAC system. For sensor faults, an online semi-supervised sensor data validation and fault diagnosis method using an Auto-Associative Neural Network (AANN) is developed. The method is based on the implementation of Nonlinear Principal Component Analysis (NPCA) using a Back-Propagation Neural Network (BPNN) and it demonstrates notable capability in sensor fault and inaccuracy correction, measurement noise reduction, missing sensor data replacement, and in both single and multiple sensor faults diagnosis. In addition, a novel on-line supervised multi-model approach for actuator fault diagnosis using Convolutional Neural Networks (CNNs) is developed for single actuator faults. It is based a data transformation in which the 1-dimensional data are configured into a 2-dimensional representation without the use of advanced signal processing techniques. The CNN-based actuator fault diagnosis approach demonstrates improved performance capability compared with the commonly used Machine Learning-based algorithms (i.e., Support Vector Machine and standard Neural Networks). The presented schemes are compared with other commonly used HVAC fault diagnosis methods for benchmarking and they are proven to be superior, effective, accurate, and reliable. The proposed approaches can be applied to large-scale buildings with additional zones

Smart Composite Overwrapped Pressure Vessel - Integrated Structural Health Monitoring System to Meet Space Exploration and International Space Station Mission Assurance Needs

Currently there are no integrated NDE methods for baselining and monitoring defect levels in fleet for Composite Overwrapped Pressure Vessels (COPVs) or related fracture critical composites, or for performing life-cycle maintenance inspections either in a traditional remove-and-inspect mode or in a more modern in situ inspection structural health monitoring (SHM) mode. Implicit in SHM and autonomous inspection is the existence of quantitative accept-reject criteria. To be effective, these criteria must correlate with levels of damage known to cause composite failure. Furthermore, implicit in SHM is the existence of effective remote sensing hardware and automated techniques and algorithms for interpretation of SHM data. SHM of facture critical composite structures, especially high pressure COPVs, is critical to the success of nearly every future NASA space exploration program as well as life extension of the International Space Station. It has been clearly stated that future NASA missions may not be successful without SHM [1]. Otherwise, crews will be busy addressing subsystem health issues and not focusing on the real NASA missio

Step Length Estimation in Daily Activities using RSSI-based Techniques

Step length, an essential component in gait analysis, is becoming appealing in many aspects of our life. It can reflect physical fitness among the young and the senior, e.g., obesity, falling probability and severity. It can also help predict the life expectancy of the elderly. Moreover, the disabled or patients with impaired cognitive functions also behave differently from healthy people in terms of step length. Another application of step length estimation is that it leverages non-GPS localisation where the global positioning system (GPS) is restricted or prohibited. Accurate measurements of step length are thus important in numerous applications. Unfortunately, the existing step length measurement techniques are yet matured. Their common drawbacks could be expensive costs, specific location requirements, constraints of human activities to be measured and of the movement direction of the human under test, proneness to errors due to occlusions, modest accuracy, or a combination of these drawbacks. An accurate path loss model between two human feet is also missing. Therefore, this thesis examines step length estimation and distance measurement between human body parts in wireless body area networks (WBANs). The thesis aims to overcome several above drawbacks by proposing novel techniques to estimate the step length of pedestrians, using our developed wearable, unobtrusive hardware during ambulation or other daily activities