Intelligent technologies for real-time monitoring and decision support systems

MPhilAutomation of data processing and control of operations involving intelligent technologies that is considered the next generation technology requires error-free data capture systems in both clinical research and healthcare. The presented research constitutes a step in the development of intelligent technologies in healthcare. The proposed improvement is by automation that includes the elements of intelligence and prediction. In particular automatic data acquisition systems for several devices are developed including pervasive computing technologies for mobility. The key feature of the system is the minimisation/near eradication of erroneous data input along with a number of other security measures ensuring completeness, accuracy and reliability of the patients‟ data. The development is based on utilising existing devices to keep the cost of Data Acquisition Systems down. However, with existing technology and devices one can be limited to features required to perform more refined analysis. Research of existing and development of a new device for assessment of neurological diseases, such as MS (Multiple Sclerosis) using Stroop test is performed. The software can also be customized for use in other diseases affecting Central Nervous System such as Parkinson‟s disease. The introduction of intelligent functions into the majority of operations enables quality checks and provides on-line user assistance. It could become a key tool in the first step of patient diagnosis before referring to more advanced tests for further investigation. Although the software cannot fully ensure the diagnosis of MS or PD but can make significant contribution in the process of diagnosis and monitorin

FAULT DETECTION AND PREDICTION IN ELECTROMECHANICAL SYSTEMS VIA THE DISCRETIZED STATE VECTOR-BASED PATTERN ANALYSIS OF MULTI-SENSOR SIGNALS

Department of System Design and Control EngineeringIn recent decades, operation and maintenance strategies for industrial applications have evolved from corrective maintenance and preventive maintenance, to condition-based monitoring and eventually predictive maintenance. High performance sensors and data logging technologies have enabled us to monitor the operational states of systems and predict fault occurrences. Several time series analysis methods have been proposed in the literature to classify system states via multi-sensor signals. Since the time series of sensor signals is often characterized as very-short, intermittent, transient, highly nonlinear, and non-stationary random signals, they make time series analyses more complex. Therefore, time series discretization has been popularly applied to extract meaningful features from original complex signals. There are several important issues to be addressed in discretization for fault detection and prediction: (i) What is the fault pattern that represents a system???s faulty states, (ii) How can we effectively search for fault patterns, (iii) What is a symptom pattern to predict fault occurrences, and (iv) What is a systematic procedure for online fault detection and prediction. In this regard, this study proposes a fault detection and prediction framework that consists of (i) definition of system???s operational states, (ii) definitions of fault and symptom patterns, (iii) multivariate discretization, (iv) severity and criticality analyses, and (v) online detection and prediction procedures. Given the time markers of fault occurrences, we can divide a system???s operational states into fault and no-fault states. We postulate that a symptom state precedes the occurrence of a fault within a certain time period and hence a no-fault state consists of normal and symptom states. Fault patterns are therefore found only in fault states, whereas symptom patterns are either only found in the system???s symptom states (being absent in the normal states) or not found in the given time series, but similar to fault patterns. To determine the length of a symptom state, we present a symptom pattern-based iterative search method. In order to identify the distinctive behaviors of multi-sensor signals, we propose a multivariate discretization approach that consists mainly of label definition, label specification, and event codification. Discretization parameters are delicately controlled by considering the key characteristics of multi-sensor signals. We discuss how to measure the severity degrees of fault and symptom patterns, and how to assess the criticalities of fault states. We apply the fault and symptom pattern extraction and severity assessment methods to online fault detection and prediction. Finally, we demonstrate the performance of the proposed framework through the following six case studies: abnormal cylinder temperature in a marine diesel engine, automotive gasoline engine knockings, laser weld defects, buzz, squeak, and rattle (BSR) noises from a car door trim (using a typical acoustic sensor array and using acoustic emission sensors respectively), and visual stimuli cognition tests by the P300 experiment.ope

Trends extraction and analysis for complex system monitoring and decision support

International audienceThis paper presents an effective trend extraction procedure, based on a simple, yet powerful, representation. Its usefulness for complex system monitoring and decision support is illustrated by three examples. The method extracts semi-qualitative temporal episodes on-line, from any univariate time series,. Three primitives are used to describe the episodes: {Increasing, Decreasing, Steady}. The method uses a segmentation algorithm, a classification of the segments into seven temporal shapes and a temporal aggregation of episodes. It acts on noisy data, without pre-filtering. The first illustration is devoted to decision support in intensive care units. The signals contain information and noise at very different frequencies, and smoothing must not mask some interesting high frequency data features. The second illustration is dedicated to a food industry process. On-line trends of key variables represent a very useful monitoring tool to control the end product quality despite high variations of raw materials at the input and a long delay,. The last example concerns operator support and predictive maintenance. The results issued from a diagnostic module are complemented by the extrapolation of the key variable trends , which gives an idea of the time left to repair or reconfigure the process

Proceedings of the 6th International Conference EEDAL'11 Energy Efficiency in Domestic Appliances and Lighting

This book contains the papers presented at the sixth international conference on Energy Efficiency in Domestic Appliances and Lighting. EEDAL'11 was organised in Copenhagen, Denmark in May 2011. This major international conference, which was previously been staged in Florence 1997, Naples 2000, Turin 2003, London 2006, Berlin 200h9a s been very successful in attracting an international community of stakeholders dealing with residential appliances, equipment, metering liagnhdti ng (including manufacturers, retailers, consumers, governments, international organisations aangde ncies, academia and experts) to discuss the progress achieved in technologies, behavioural aspects and poliacineds , the strategies that need to be implemented to further progress this important work. Potential readers who may benefit from this book include researchers, engineers, policymakers, and all those who can influence the design, selection, application, and operation of electrical appliances and lighting.JRC.F.7-Renewable Energ