system-level-RUL uncertainty
时间: 2023-10-19 08:08:35 浏览: 39
System-level RUL (Remaining Useful Life) uncertainty refers to the uncertainty surrounding the estimate of the remaining useful life of a system. This uncertainty arises due to various factors such as incomplete knowledge about the system's degradation mechanisms, variability in operating conditions, measurement errors, and model inaccuracies. System-level RUL uncertainty can be quantified using probabilistic methods such as Bayesian inference and Monte Carlo simulations. Accurate estimation of system-level RUL uncertainty is crucial for effective maintenance planning and decision-making to ensure the safe and reliable operation of systems.
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system-level-RUL
System-level RUL (Remaining Useful Life) refers to the estimated time that a system or component will continue to function properly before it fails completely. This estimation is based on the analysis of various factors such as historical data, environmental conditions, usage patterns, and maintenance activities. The purpose of calculating RUL is to predict when maintenance or replacement of the system or component will be necessary, in order to avoid unexpected failures and downtime. System-level RUL is often used in industries such as aviation, manufacturing, and transportation, where reliability and uptime are critical.
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.