首页|New Data from Aerospace Corporation Illuminate Findings in Machine Learning (Mac hine-learning and Physics-based Tool for Anomaly Identification In Propulsion Sy stems)
New Data from Aerospace Corporation Illuminate Findings in Machine Learning (Mac hine-learning and Physics-based Tool for Anomaly Identification In Propulsion Sy stems)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting from El Segundo, California, by NewsRx jo urnalists, research stated, "Launch anomalies occur frequently during the early phase of a program, with many of the anomalies attributed to propulsion systems. Approaches for identifying and mitigating potential propulsion failures can aid development programs and accelerate the resolution of root cause investigations ." Financial support for this research came from Internal Research and Development. The news correspondents obtained a quote from the research from Aerospace Corpor ation, "In reusable systems, anomaly detection methods can be employed to detect latent system health issues that could become problematic as the system ages. M odern launch support relies on human judgement for redline limit generation and visual family data comparison for many operational aspects, which makes it chall enging to identify failure modes and to diagnose an anomaly. Additionally, famil y data comparison is unavailable for the first few launches of a new vehicle. Au tomated tools to quickly identify system failures of new and reusable systems ca n bridge these gaps. Physics-based modeling and machine learning (PBMML) offers methods that can improve the reliability of new or reusable launch vehicles by i dentifying propulsion anomalies or issues before they jeopardize future space mi ssions. PBMML can then be used to inform corrective actions."
El SegundoCaliforniaUnited StatesN orth and Central AmericaBusinessCyborgsEmerging TechnologiesMachine Lear ningAerospace Corporation
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