Raw Level automatisiert zertifiziert US beta
Diese Daten erreichen Raw Level am on 22 October 2015 das bedeutet dies ist ein großartiger Schritt für die Grundlagen der Veröffentlichung offener Daten.
Wrong decisions during the missions can lead to an unsafe condition or immediate failure, while correct decisions can help continue the missions even from faulty conditions. In view of the lessons learned from mishaps, i.e., failed space missions, it is imminent that reliability analysis and risk assessment are kept in sync with space system design as it evolves from the concept through preliminary design, detailed design, production, and operations. From the successful proof-of-concept demonstration for the proposal solution in Phase I, Qualtech Systems, Inc. (QSI) in collaboration with Dr. John Sheppard from Montana State University (MSU) proposes to architect the solution for continuous real-time health monitoring and diagnosis, automatically generating current risk assessment for Loss of Mission, Loss of Crew, Loss of Vehicle during vehicle operations while taking into account the current health of the vehicle and operational modes and phases in Phase II. The QSI-MSU team plans to emphasize advancement in the six following areas: (a) enhancement of the existing EPS model/modeling a new target system, (b) dynamic generation of fault-tree by TEAMS-RDS®, (c) expansion of risk modeling and learning, (d) expansion of risk assessment capabilities, (e) Automatic information exchange between TEAMS-RDS® reasoner and CTBN reasoner for both design-time and run-time, and (f) enhancement and incorporation of the risk visualization tool capability into web-based TEAMS-RDS® dashboard. The solution architecture will provide the ability for the crew to assess and select the "right" mitigation option for component failures and subsequently update the health diagnosis and risk assessment given the executed mitigation plan.
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