C-MAPSS dataset的Scoring function 函数
时间: 2024-05-30 08:15:52 浏览: 19
C-MAPSS数据集中的Scoring function函数是用来计算发动机寿命的。该函数使用以下公式:
RUL = TTF - TT
其中,RUL代表剩余使用寿命,TTF代表故障时间,TT代表历史使用时间。该函数通过比较预测的剩余寿命和实际寿命来评估模型的性能。具体而言,该函数计算预测剩余寿命和实际寿命之间的均方根误差(RMSE)和平均绝对误差(MAE)。较低的RMSE和MAE值表示模型预测的剩余寿命与实际寿命更接近,因此表明该模型具有更好的性能。
相关问题
C-MAPSS dataset
The C-MAPSS dataset (Commercial Modular Aero-Propulsion System Simulation) is a widely used benchmark dataset for prognostics and health management (PHM) research in the field of aviation. It was originally developed by NASA to support research in the areas of fault diagnosis, prognostics, and remaining useful life (RUL) estimation for aircraft engines.
The dataset contains sensor measurements and operational data from a fleet of commercial turbofan engines, which were operated under different conditions and subjected to various types of faults and failures. The dataset includes four different sub-datasets, each representing a different engine model and covering different levels of fault severity.
The C-MAPSS dataset has been widely used by researchers and practitioners to develop and evaluate various PHM techniques, including data-driven and model-based approaches for fault detection, diagnosis, and RUL estimation. It has also been used to compare the performance of different PHM methods and to benchmark different algorithms and techniques in the field.
Overall, the C-MAPSS dataset has played a critical role in advancing the state of the art in PHM research for aircraft engines and has contributed to improving the safety, reliability, and efficiency of commercial aviation.
furg-fire-dataset
furg-fire-dataset是一个用于火灾研究的数据集,包含了大量关于火灾的信息和数据。该数据集收集了各种类型的火灾事件的数据,包括火灾的发生地点、起火原因、持续时间、影响范围等信息。这些数据有助于研究人员深入了解火灾的特点、规律和影响,从而促进火灾预防和应对措施的制定和完善。
furg-fire-dataset的数据来源于多个渠道,包括消防部门的报告、媒体报道以及专业研究机构的调查。这样多方面的数据来源保证了数据的全面性和准确性,为火灾研究提供了可靠的依据。
研究人员可以利用furg-fire-dataset来分析火灾的发生规律,探讨火灾的防范措施,提出改进建议,甚至开展火灾预测模型的研究。通过对这些数据的深入研究,我们可以更好地了解火灾的危害程度,提高火灾的应对能力,降低火灾对人们生命和财产的危害。
总的来说,furg-fire-dataset为火灾研究提供了重要的数据支持,有助于推动火灾防控工作的发展,提高社会对火灾的认识和预防意识。希望更多的研究人员能够利用这个数据集,共同致力于火灾防范工作的推进。
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