Smart and Intelligent Sensors

John C. Stennis Space Center (SSC) provides rocket engine propulsion testing for NASA's space programs. Since the development of the Space Shuttle, every Space Shuttle Main Engine (SSME) has undergone acceptance testing at SSC before going to Kennedy Space Center (KSC) for integration into the Space Shuttle. The SSME is a large cryogenic rocket engine that uses Liquid Hydrogen (LH2) as the fuel. As NASA moves to the new ARES V launch system, the main engines on the new vehicle, as well as the upper stage engine, are currently base lined to be cryogenic rocket engines that will also use LH2. The main rocket engines for the ARES V will be larger than the SSME, while the upper stage engine will be approximately half that size. As a result, significant quantities of hydrogen will be required during the development, testing, and operation of these rocket engines.Better approaches are needed to simplify sensor integration and help reduce life-cycle costs. 1.Smarter sensors. Sensor integration should be a matter of "plug-and-play" making sensors easier to add to a system. Sensors that implement new standards can help address this problem; for example, IEEE STD 1451.4 defines transducer electronic data sheet (TEDS) templates for commonly used sensors such as bridge elements and thermocouples. When a 1451.4 compliant smart sensor is connected to a system that can read the TEDS memory, all information needed to configure the data acquisition system can be uploaded. This reduces the amount of labor required and helps minimize configuration errors. 2.Intelligent sensors. Data received from a sensor be scaled, linearized; and converted to engineering units. Methods to reduce sensor processing overhead at the application node are needed. Smart sensors using low-cost microprocessors with integral data acquisition and communication support offer the means to add these capabilities. Once a processor is embedded, other features can be added; for example, intelligent sensors can make a health assessment to inform the data acquisition client when sensor performance is suspect. 3.Distributed sample synchronization. Networks of sensors require new ways for synchronizing samples. Standards that address the distributed timing problem (for example, IEEE STD 1588) provide the means to aggregate samples from many distributed smart sensors with sub-microsecond accuracy. 4. Reduction in interconnect. Alternative means are needed to reduce the frequent problems associated with cabling and connectors. Wireless technologies offer the promise of reducing interconnects and simultaneously making it easy to quickly add a sensor to a system

A state-of-the-art assessment of active structures

A state-of-the-art assessment of active structures with emphasis towards the applications in aeronautics and space is presented. It is felt that since this technology area is growing at such a rapid pace in many different disciplines, it is not feasible to cover all of the current research but only the relevant work as relates to aeronautics and space. Research in smart actuation materials, smart sensors, and control of smart/intelligent structures is covered. In smart actuation materials, piezoelectric, magnetostrictive, shape memory, electrorheological, and electrostrictive materials are covered. For sensory materials, fiber optics, dielectric loss, and piezoelectric sensors are examined. Applications of embedded sensors and smart sensors are discussed

Intelligent Sensors for Integrated Systems Health Management (ISHM)

IEEE 1451 Smart Sensors contribute to a number of ISHM goals including cost reduction achieved through: a) Improved configuration management (TEDS); and b) Plug-and-play re-configuration. Intelligent Sensors are adaptation of Smart Sensors to include ISHM algorithms; this offers further benefits: a) Sensor validation. b) Confidence assessment of measurement, and c) Distributed ISHM processing. Space-qualified intelligent sensors are possible a) Size, mass, power constraints. b) Bus structure/protocol

Integrated System Health Management: Foundational Concepts, Approach, and Implementation

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

Managing Challenges of Non Communicable Diseases during Pregnancy: An Innovative Approach

Le sfide lanciate dalle malattie non trasmissibili sono accolte da tecnologie sempre più all'avanguardia. Nonostante questo, ancora oggi gestire e monitorare gravidanze a rischio rimane un problema. La simulazione di condizioni come quella data dal diabete gestazionale, può aiutare a capire quali sono i principali fattori che influenzano l'andamento della malattia in modo da poterne evitare l'insorgenza e, in questo modo, migliorare la salute di madri e generazioni future. Questa tesi ha come obietto lo studio e il miglioramento di un sistema Agent-Based pensato per il trattamento del diabete di tipo 1 e la modellazione di una sua estensione per il diabete gestazionale. Al termine della tesi è stato migliorato il sistema rendendolo più fedele ai cambiamenti fisiologici che avvengono durante il metabolismo del glucosio e la modellazione della placenta e relativamente delle modifiche che apporta all'intero sistema getta le basi per nuovi sviluppi legati al trattamento di malattie durante il periodo di gestazione

PhysioAR: smart sensing and augmented reality for physical rehabilitation

The continuous evolution of technology allows for a better analysis of the human being. In certain medical areas such as physiotherapy is required a correct analysis of the patient's evolution. The development of Information and Communication Technologies and recent innovations in the Internet of Things opens new possibilities in the medical field as systems of remote monitoring of patients with new sensors that allow the correct analysis of the health data of patients. In physiotherapy one of the most common problems in the application of treatments is the patient demotivation, something that today can be reduced with the introduction of Augmented Reality that provides a new experience to the patient. For this purpose, a system was developed that combines intelligent sensors with Augmented Reality application that will help monitor patient performance. This system is capable of monitoring lower limb movements acceleration, knee joint angle, patient equilibrium, muscular activity and cardiac activity using electromyography and electrocardiography with a wearable set of tools for easy utilization.A evolução continua da tecnologia permite cada vez mais uma melhor análise do ser humano. Em certas áreas médicas, como a fisioterapia, é necessária uma correta análise da evolução do paciente. O desenvolvimento das Tecnologias de Informação e Comunicação, e as inovações no domínio de Internet das Coisas novas possibilidades no ramo da medicina, como sistemas de monitorização remota de pacientes com novos sensores que permitem a correta análise dos dados de saúde dos pacientes. Na fisioterapia um dos problemas mais comuns na aplicação dos tratamentos é a desmotivação do paciente, algo que hoje pode ser reduzido com introdução da aplicação da Realidade Aumentada que proporciona uma nova experiência ao paciente. Para isso nesta dissertação foi desenvolvido um sistema que combina sensores inteligentes com Realidade Aumentada que vai ajudar o paciente monitorizando o seu desempenho. Este sistema é capaz de monitorizar o ângulo do joelho, captar acelaração de movimentos dos membros inferiores, equilíbrio do paciente, atividade muscular e atividade cárdica usando electromiografia e electrocardiografia num conjunto wearable de fácil utilização

Internet of Things and Intelligent Technologies for Efficient Energy Management in a Smart Building Environment

Internet of Things (IoT) is attempting to transform modern buildings into energy efficient, smart, and connected buildings, by imparting capabilities such as real-time monitoring, situational awareness and intelligence, and intelligent control. Digitizing the modern day building environment using IoT improves asset visibility and generates energy savings. This dissertation provides a survey of the role, impact, and challenges and recommended solutions of IoT for smart buildings. It also presents an IoT-based solution to overcome the challenge of inefficient energy management in a smart building environment. The proposed solution consists of developing an Intelligent Computational Engine (ICE), composed of various IoT devices and technologies for efficient energy management in an IoT driven building environment. ICE’s capabilities viz. energy consumption prediction and optimized control of electric loads have been developed, deployed, and dispatched in the Real-Time Power and Intelligent Systems (RTPIS) laboratory, which serves as the IoT-driven building case study environment. Two energy consumption prediction models viz. exponential model and Elman recurrent neural network (RNN) model were developed and compared to determine the most accurate model for use in the development of ICE’s energy consumption prediction capability. ICE’s prediction model was developed in MATLAB using cellular computational network (CCN) technique, whereas the optimized control model was developed jointly in MATLAB and Metasys Building Automation System (BAS) using particle swarm optimization (PSO) algorithm and logic connector tool (LCT), respectively. It was demonstrated that the developed CCN-based energy consumption prediction model was highly accurate with low error % by comparing the predicted and the measured energy consumption data over a period of one week. The predicted energy consumption values generated from the CCN model served as a reference for the PSO algorithm to generate control parameters for the optimized control of the electric loads. The LCT model used these control parameters to regulate the electric loads to save energy (increase energy efficiency) without violating any operational constraints. Having ICE’s energy consumption prediction and optimized control of electric loads capabilities is extremely useful for efficient energy management as they ensure that sufficient energy is generated to meet the demands of the electric loads optimally at any time thereby reducing wasted energy due to excess generation. This, in turn, reduces carbon emissions and generates energy and cost savings. While the ICE was tested in a small case-study environment, it could be scaled to any smart building environment