Physical Intelligent Sensors For Integrated System Health Management

A novel Integrated System Health Management (ISHM) module is being developed which supports the development of intelligent sensors as part of an integrated systems approach, i.e. one treats the sensor as a complete system with its own sensing hardware (the traditional sensor), A/D converters, processing and storage capabilities, software drivers, self-assessment algorithms, communication protocols and evolutionary methodologies that allow it to get better with time. As a part of a project being undertaken at NASA's Stennis Space Center (SSC), an integrated framework is being developed for the intelligent monitoring of smart elements. These smart elements can be sensors, actuators or other devices. The immediate application is the monitoring of the rocket test stands at SSC, but the technology should be generally applicable to the ISHM vision. This thesis work outlines the progress made in the development of intelligent sensors by describing the work done to date on the Physical Intelligent Sensors (PIS). In this thesis work a traditional temperature sensor is converted into a Smart Networked Temperature Sensor (SNTS) for monitoring the temperature of the liquid oxygen tank on the SSC rocket test stand. (Abstract shortened by UMI.)

A Physical Intelligent Sensor (PIS) has been developed that detects the events occurring in real time data. This is a part of a project undertaken at NASA's Stennis Space Center (SSC) to build an integrated framework for the intelligent monitoring of smart elements. These smart elements can be sensors, actuators or other devices. The immediate application is the monitoring of the rocket test stands at SSC, but is also generally applicable to the Integrated System Health Management (ISHM) vision, which assists the development of intelligent sensors as part of an integrated systems approach, i.e. one treats the sensor as a complete system. This thesis work deals with the development of the Physical Intelligent Sensors (PIS) implemented with event detection routines and obtained from Glenn Research Center (GRC) and previous work called DATA-SIMLAMT (Dynamic Across Time Autonomous---Sensing, Interpretation, Model Learning and Maintenance Theory). For this work a traditional temperature sensor is converted into a Smart Networked Temperature Sensor (SNTS) for monitoring the temperature of the liquid oxygen tank on the SSC rocket test stand. A K-type thermocouple with a sigma-delta analog to digital converter (ADC) AD7794 is used to measure the temperature. This ADC is integrated with a Z-world's rabbit microprocessor "RCM3300" to make it intelligent. The temperature readings are collected in the flash memory of the microprocessor and the event detection routines are embedded in the microprocessor. The event detection routines are capable of detecting anomalies (flat, noise, spike, drift, level shift) in the acquired data, and if an anomaly is detected it is reported to the local monitoring system. Hence the output of the PIS is data coupled with a confidence factor in the reliability of the data.

ISHM is an innovative combination of technologies and methods that offers solutions to the reliability problems caused by increased complexities in design, manufacture, use conditions, and maintenance. Its key strength is in the successful integration of reliability (quantitative estimation of successful operation or failure), "diagnosibility" (ability to determine the fault source), and maintainability (how to maintain the performance of a system in operation). It draws on engineering issues such as advanced sensor monitoring, redundancy management, probabilistic reliability theory, artificial intelligence for diagnostics and prognostics, and formal validation methods, but also "quasi-technical" techniques and disciplines such as quality assurance, systems architecture and engineering, knowledge capture, information fusion, testability and maintainability, and human factors. This groundbreaking book defines and explains this new discipline, providing frameworks and methodologies for implementation and further research. Each chapter includes experiments, numerical examples, simulations and case studies. It is the ideal guide to this crucial topic for professionals or researchers in aerospace systems, systems engineering, production engineering, and reliability engineering. Solves prognostic information selection and decision-level information fusion issues Presents integrated evaluation methodologies for complex aerospace system health conditions and software system reliability assessment Proposes a framework to perform fault diagnostics with a distributed intelligent agent system and a data mining approach for multistate systems Explains prognostic methods that combine both the qualitative system running state prognostics and the quantitative remaining useful life prediction

A sound basis to guide the community in the conception and implementation of ISHM (Integrated System Health Management) capability in operational systems was provided. The concept of "ISHM Model of a System" and a related architecture defined as a unique Data, Information, and Knowledge (DIaK) architecture were described. The ISHM architecture is independent of the typical system architecture, which is based on grouping physical elements that are assembled to make up a subsystem, and subsystems combine to form systems, etc. It was emphasized that ISHM capability needs to be implemented first at a low functional capability level (FCL), or limited ability to detect anomalies, diagnose, determine consequences, etc. As algorithms and tools to augment or improve the FCL are identified, they should be incorporated into the system. This means that the architecture, DIaK management, and software, must be modular and standards-based, in order to enable systematic augmentation of FCL (no ad-hoc modifications). A set of technologies (and tools) needed to implement ISHM were described. One essential tool is a software environment to create the ISHM Model. The software environment encapsulates DIaK, and an infrastructure to focus DIaK on determining health (detect anomalies, determine causes, determine effects, and provide integrated awareness of the system to the operator). The environment includes gateways to communicate in accordance to standards, specially the IEEE 1451.1 Standard for Smart Sensors and Actuators.

Sensor Technologies: Healthcare, Wellness and Environmental Applications explores the key aspects of sensor technologies, covering wired, wireless, and discrete sensors for the specific application domains of healthcare, wellness and environmental sensing. It discusses the social, regulatory, and design considerations specific to these domains. The book provides an application-based approach using real-world examples to illustrate the application of sensor technologies in a practical and experiential manner. The book guides the reader from the formulation of the research question, through the design and validation process, to the deployment and management phase of sensor applications. The processes and examples used in the book are primarily based on research carried out by Intel or joint academic research programs. “Sensor Technologies: Healthcare, Wellness and Environmental Applications provides an extensive overview of sensing technologies and their applications in healthcare, wellness, and environmental monitoring. From sensor hardware to system applications and case studies, this book gives readers an in-depth understanding of the technologies and how they can be applied. I would highly recommend it to students or researchers who are interested in wireless sensing technologies and the associated applications.” Dr. Benny Lo Lecturer, The Hamlyn Centre, Imperial College of London “This timely addition to the literature on sensors covers the broad complexity of sensing, sensor types, and the vast range of existing and emerging applications in a very clearly written and accessible manner. It is particularly good at capturing the exciting possibilities that will occur as sensor networks merge with cloud-based ‘big data’ analytics to provide a host of new applications that will impact directly on the individual in ways we cannot fully predict at present. It really brings this home through the use of carefully chosen case studies that bring the overwhelming concept of 'big data' down to the personal level of individual life and health.” Dermot Diamond Director, National Centre for Sensor Research, Principal Investigator, CLARITY Centre for Sensor Web Technologies, Dublin City University "Sensor Technologies: Healthcare, Wellness and Environmental Applications takes the reader on an end-to-end journey of sensor technologies, covering the fundamentals from an engineering perspective, introducing how the data gleaned can be both processed and visualized, in addition to offering exemplar case studies in a number of application domains. It is a must-read for those studying any undergraduate course that involves sensor technologies. It also provides a thorough foundation for those involved in the research and development of applied sensor systems. I highly recommend it to any engineer who wishes to broaden their knowledge in this area!" Chris Nugent Professor of Biomedical Engineering, University of Ulster

Condition modelling and control is a technique used to enable decision-making in manufacturing processes of interest to researchers and practising engineering. Condition Monitoring and Control for Intelligent Manufacturing will be bought by researchers and graduate students in manufacturing and control and engineering, as well as practising engineers in industries such as automotive and packaging manufacturing.

Intelligent sensors are investigated as part of an integrated systems approach. This approach has resulted in the development of two types of sensors: Physical Intelligent Sensors (PIS) and Virtual Intelligent Sensors (VIS). The integrated systems approach, as implemented in the PIS, treats the sensor as a complete system with its own sensing hardware (the traditional sensor), A/D converter, processing and storage capabilities, software drivers, self-assessment algorithms and communication protocols. The VIS is the virtual counterpart of the PIS where the algorithms reside on the main computer and is the main focus of this thesis. The VIS is used where it is impossible to place a PIS as well as a test platform for algorithms before downloading them to the PIS. The immediate application is the monitoring of rocket test stands at NASA's Stennis Space Center, but the technology should be generally applicable to the Integrated Systems Health Monitoring (ISHM) vision. This thesis outlines progress made in the development of intelligent sensors by describing the work done to date on Virtual Intelligent Sensors (VIS) with implementation in the LabVIEW environments. The VIS has been designed to mirror the operations of the PIS, and uses existing anomaly detection routines developed at NASA. This thesis also focuses on smart sensors, and shows the working of an enhanced version of a previously developed intelligent sensor algorithm, DATA-SIMLAMT, called Enhanced DATA-SIMLAMT or EDATA-SIMLAMT. This new version contains additional properties and states for improved data interpretation. The additional properties are based on a signal processing tool known as wavelet analysis. The major advantage provided by adding wavelet analysis is the ability to detect sudden transitions as well as get a handle on the frequency content using a much smaller data set then that required by the traditional Fourier transform method.

This book describes the technology used for effective sensing of our physical world and intelligent processing techniques for sensed information, which are essential to the success of Internet of Things (IoT). The authors provide a multidisciplinary view of sensor technology from materials, process, circuits, and big data domains and showcase smart sensor systems in real applications including smart home, transportation, medical, environmental, agricultural, etc. Unlike earlier books on sensors, this book provides a “global” view on smart sensors covering abstraction levels from device, circuit, systems, and algorithms.

This book reports on the theoretical foundations, fundamental applications and latest advances in various aspects of connected services for health information systems. The twelve chapters highlight state-of-the-art approaches, methodologies and systems for the design, development, deployment and innovative use of multisensory systems and tools for health management in smart city ecosystems. They exploit technologies like deep learning, artificial intelligence, augmented and virtual reality, cyber physical systems and sensor networks. Presenting the latest developments, identifying remaining challenges, and outlining future research directions for sensing, computing, communications and security aspects of connected health systems, the book will mainly appeal to academic and industrial researchers in the areas of health information systems, smart cities, and augmented reality.

Intelligent Data Sensing and Processing for Health and Well-being Applications uniquely combines full exploration of the latest technologies for sensor-collected intelligence with detailed coverage of real-case applications for healthcare and well-being at home and in the workplace. Forward-thinking in its approach, the book presents concepts and technologies needed for the implementation of today's mobile, pervasive and ubiquitous systems, and for tomorrow’s IoT and cyber-physical systems. Users will find a detailed overview of the fundamental concepts of gathering, processing and analyzing data from devices disseminated in the environment, as well as the latest proposals for collecting, processing and abstraction of data-sets. In addition, the book addresses algorithms, methods and technologies for diagnosis and informed decision-making for healthcare and well-being. Topics include emotional interface with ambient intelligence and emerging applications in detection and diagnosis of neurological diseases. Finally, the book explores the trends and challenges in an array of areas, such as applications for intelligent monitoring in the workplace for well-being, acquiring data traffic in cities to improve the assistance of first aiders, and applications for supporting the elderly at home. Examines the latest applications and future directions for mobile data sensing in an array of health and well-being scenarios Combines leading computing paradigms and technologies, development applications, empirical studies, and future trends in the multidisciplinary field of smart sensors, smart sensor networks, data analysis and machine intelligence methods Features an analysis of security, privacy and ethical issues in smart sensor health and well-being applications Equips readers interested in interdisciplinary projects in ubiquitous computing or pervasive computing and ambient intelligence with the latest trends and developments