Time-Pattern Profiling from Smart Meter Data to Detect Outliers in Energy Consumption

Smart meters have become a core part of the Internet of Things, and its sensory network is increasing globally. For example, in the UK there are over 15 million smart meters operating across homes and businesses. One of the main advantages of the smart meter installation is the link to a reduction in carbon emissions. Research shows that, when provided with accurate and real-time energy usage readings, consumers are more likely to turn off unneeded appliances and change other behavioural patterns around the home (e.g., lighting, thermostat adjustments). In addition, the smart meter rollout results in a lessening in the number of vehicle callouts for the collection of consumption readings from analogue meters and a general promotion of renewable sources of energy supply. Capturing and mining the data from this fully maintained (and highly accurate) sensing network, provides a wealth of information for utility companies and data scientists to promote applications that can further support a reduction in energy usage. This research focuses on modelling trends in domestic energy consumption using density-based classifiers. The technique estimates the volume of outliers (e.g., high periods of anomalous energy consumption) within a social class grouping. To achieve this, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points to Identify the Clustering Structure (OPTICS) and Local Outlier Factor (LOF) demonstrate the detection of unusual energy consumption within naturally occurring groups with similar characteristics. Using DBSCAN and OPTICS, 53 and 208 outliers were detected respectively; with 218 using LOF, on a dataset comprised of 1,058,534 readings from 1026 homes

Advanced Sensing, Fault Diagnostics, and Structural Health Management

Advanced sensing, fault diagnosis, and structural health management are important parts of the maintenance strategy of modern industries. With the advancement of science and technology, modern structural and mechanical systems are becoming more and more complex. Due to the continuous nature of operation and utilization, modern systems are heavily susceptible to faults. Hence, the operational reliability and safety of the systems can be greatly enhanced by using the multifaced strategy of designing novel sensing technologies and advanced intelligent algorithms and constructing modern data acquisition systems and structural health monitoring techniques. As a result, this research domain has been receiving a significant amount of attention from researchers in recent years. Furthermore, the research findings have been successfully applied in a wide range of fields such as aerospace, manufacturing, transportation and processes

Data Quality Management in Large-Scale Cyber-Physical Systems

Cyber-Physical Systems (CPSs) are cross-domain, multi-model, advance information systems that play a significant role in many large-scale infrastructure sectors of smart cities public services such as traffic control, smart transportation control, and environmental and noise monitoring systems. Such systems, typically, involve a substantial number of sensor nodes and other devices that stream and exchange data in real-time and usually are deployed in uncontrolled, broad environments. Thus, unexpected measurements may occur due to several internal and external factors, including noise, communication errors, and hardware failures, which may compromise these systems quality of data and raise serious concerns related to safety, reliability, performance, and security. In all cases, these unexpected measurements need to be carefully interpreted and managed based on domain knowledge and computational models. Therefore, in this research, data quality challenges were investigated, and a comprehensive, proof of concept, data quality management system was developed to tackle unaddressed data quality challenges in large-scale CPSs. The data quality management system was designed to address data quality challenges associated with detecting: sensor nodes measurement errors, sensor nodes hardware failures, and mismatches in sensor nodes spatial and temporal contextual attributes. Detecting sensor nodes measurement errors associated with the primary data quality dimensions of accuracy, timeliness, completeness, and consistency in large-scale CPSs were investigated using predictive and anomaly analysis models via utilising statistical and machine-learning techniques. Time-series clustering techniques were investigated as a feasible mean for detecting long-segmental outliers as an indicator of sensor nodes’ continuous halting and incipient hardware failures. Furthermore, the quality of the spatial and temporal contextual attributes of sensor nodes observations was investigated using timestamp analysis techniques. The different components of the data quality management system were tested and calibrated using benchmark time-series collected from a high-quality, temperature sensor network deployed at the University of East London. Furthermore, the effectiveness of the proposed data quality management system was evaluated using a real-world, large-scale environmental monitoring network consisting of more than 200 temperature sensor nodes distributed around London. The data quality management system achieved high accuracy detection rate using LSTM predictive analysis technique and anomaly detection associated with DBSCAN. It successfully identified timeliness and completeness errors in sensor nodes’ measurements using periodicity analysis combined with a rule engine. It achieved up to 100% accuracy in detecting potentially failed sensor nodes using the characteristic-based time-series clustering technique when applied to two days or longer time-series window. Timestamp analysis was adopted effectively for evaluating the quality of temporal and spatial contextual attributes of sensor nodes observations, but only within CPS applications in which using gateway modules is possible

Prognostic and health management of critical aircraft systems and components: an overview

This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2023Prognostic and health management (PHM) plays a vital role in ensuring the safety and reliability of aircraft systems. The process entails the proactive surveillance and evaluation of the state and functional effectiveness of crucial subsystems. The principal aim of PHM is to predict the remaining useful life (RUL) of subsystems and proactively mitigate future breakdowns in order to minimize consequences. The achievement of this objective is helped by employing predictive modeling techniques and doing real-time data analysis. The incorporation of prognostic methodologies is of utmost importance in the execution of condition-based maintenance (CBM), a strategic approach that emphasizes the prioritization of repairing components that have experienced quantifiable damage. Multiple methodologies are employed to support the advancement of prognostics for aviation systems, encompassing physics-based modeling, data-driven techniques, and hybrid prognosis. These methodologies enable the prediction and mitigation of failures by identifying relevant health indicators. Despite the promising outcomes in the aviation sector pertaining to the implementation of PHM, there exists a deficiency in the research concerning the efficient integration of hybrid PHM applications. The primary aim of this paper is to provide a thorough analysis of the current state of research advancements in prognostics for aircraft systems, with a specific focus on prominent algorithms and their practical applications and challenges. The paper concludes by providing a detailed analysis of prospective directions for future research within the field.European Union funding: 95568

Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations