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A SiGe Remote Sensor Interface
Published in John D. Cressler, H. Alan Mantooth, Extreme Environment Electronics, 2017
In the 1990s, NASA began development of a reusable launch vehicle (RLV) called the X-33 space plane. The integrated vehicle health management (IVHM) system for the X-33 consisted of a pair of host processors and 50 RHNs distributed around the periphery of the X-33 to collect telemetry data from a variety of sensor types. The RHN, shown in Figure 70.2, was a 5 pound (11 kg) box measuring 3” × 5” × 6.75” that dissipated 17 W of power. The mixed-signal data acquisition unit was assembled from a combination of commercial integrated circuits and custom hybrids for the analog front-end arranged on three cards interconnected by ribbon cables. On one end of the box, a pair of large multi-pin connectors provided the sensor interface. On the other end, a pair of optical connections provided a redundant interface to the host computers. The RHN communicated with the hosts via a token-ring network topology, and power was provided through a +28 V bus [4].
Smart manufacturing based on Digital Twin technologies
Published in Carolina Machado, J. Paulo Davim, Industry 4.0, 2020
Shohin Aheleroff, Jan Polzer, Huiyue Huang, Zexuan Zhu, David Tomzik, Yuqian Lu, Yuan Lin, Xun Xu
DT emerged during digital transformation in the early 2000s and became popular for monitoring and predictions in manufacturing. It was proposed by National Aeronautics and Space Administration (NASA) in 2011 as “an integrated multi-physics, multi-scale, probabilistic simulation of a vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its flying twin” (Shafto et al., 2012). The original intention of DT was to mirror the status of air vehicles with high-fidelity physical models, sensor data from the vehicle's onboard integrated vehicle health management (IVHM) system, maintenance history, and all available historical/fleet data obtained using data mining and text mining.
Vehicle system dynamics in digital twin studies in rail and road domains
Published in Vehicle System Dynamics, 2023
Maksym Spiryagin, Johannes Edelmann, Florian Klinger, Colin Cole
Some evidence exists that sends us back to 2002 when the original digital twin concept was presented by Michael Grieves at the University of Michigan to industry participants to introduce the idea of a Product Lifecycle Management Center [1]. However, at that time the definition of ‘digital twin’ had not been formulated and it was merely descriptive [1]. It is commonly indicated that the first definition of the digital twin was formulated by researchers in NASA and published in [2]: A digital twin is an integrated multi-physics, multi-scale, probabilistic simulation of a vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its flying twin. The digital twin is ultra-realistic and may consider one or more important and interdependent vehicle systems, including propulsion/energy storage, avionics, life support, vehicle structure, thermal management/TPS, etc. In addition to the backbone of high-fidelity physical models, the digital twin integrates sensor data from the vehicle’s on-board integrated vehicle health management (IVHM) system, maintenance history, and all available historical/fleet data obtained using data mining and text mining. The systems on board the digital twin are also capable of mitigating damage or degradation by recommending changes in mission profile to increase both the life span and the probability of mission success.
A review of methods, techniques and tools for project planning and control
Published in International Journal of Production Research, 2018
Robert Pellerin, Nathalie Perrier
Snider et al. (2015) developed a new approach to project control based on Integrated Vehicle Health Management (IVHM), a monitoring method used for machine maintenance. The IVHM approach first captures low-level data and then uses many analysis techniques simultaneously to automatically create a high-level description of the state of the project to evaluate its performance and, if necessary, make decisions on corrective actions that need to be taken.