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User's Guide for the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS)
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This report is a Users Guide for the NASA-developed Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) software, which is a transient simulation of a large commercial turbofan engine with a realistic engine control system.Abstract:
This report is a Users Guide for the NASA-developed Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) software, which is a transient simulation of a large commercial turbofan engine (up to 90,000-lb thrust) with a realistic engine control system. The software supports easy access to health, control, and engine parameters through a graphical user interface (GUI). C-MAPSS provides the user with a graphical turbofan engine simulation environment in which advanced algorithms can be implemented and tested. C-MAPSS can run user-specified transient simulations, and it can generate state-space linear models of the nonlinear engine model at an operating point. The code has a number of GUI screens that allow point-and-click operation, and have editable fields for user-specified input. The software includes an atmospheric model which allows simulation of engine operation at altitudes from sea level to 40,000 ft, Mach numbers from 0 to 0.90, and ambient temperatures from -60 to 103 F. The package also includes a power-management system that allows the engine to be operated over a wide range of thrust levels throughout the full range of flight conditions.read more
Citations
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Journal ArticleDOI
LSTM Inefficiency in Long-Term Dependencies Regression Problems
TL;DR: In this article , an empirical analysis of LSTM networks with an emphasis on inefficiency in long-term dependencies convergence because of vanishing gradient problem is provided. But the authors focus on the case of NASA turbofan engine degradation.
Proceedings ArticleDOI
Supervised learning based approach for turbofan engines failure prognosis within the belief function framework
Rachid Hamza,Safa Ben Ayed +1 more
TL;DR: In this article , an evidential supervised learning approach built on a powerful framework called the belief function theory is applied on NASA's C-MAPSS dataset for turbofan engines failure prognosis.
Proceedings ArticleDOI
A Similarity-Based Remaining Useful Life Prediction Method for Aero Engines with Small Smples
TL;DR: In this article , a similarity-based method for predicting the remaining useful life (RUL) of small-sample aircraft engines was proposed to address the problem of insufficient engine data, where the KPCA method is used to model the engine degradation trajectory, then a simple and effective method is proposed to determine the degradation start moment of each engine, and finally the similarity between each training sample and the test sample is determined based on the trained kPCA model.
Posted Content
Uncertainty-aware Remaining Useful Life predictor
TL;DR: In this paper, Deep Gaussian Processes (DGPs) are used for estimating the remaining useful life (RUL) of an industrial asset in real-life applications.
Journal ArticleDOI
Prediction of the Remaining Useful Life of Aircraft Systems via Web Interface
TL;DR: A web-based tool is presented for the simulation of a Prognostics and Health Management system used for exploring and testing different machine learning experimental scenarios with the goal of predicting the Remaining Useful Life (RUL) of aircraft systems.
References
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Book
Multivariable Feedback Design
TL;DR: In this article, a comprehensive and unified view of modern multivariate feedback theory and design is presented, where balancing techniques with theory, the objective throughout is to enable the feedback engineer to design real systems.
Book
Multivariable Feedback Design
TL;DR: This is the first text to give a comprehensive and unified view of modern multivariable feedback theory and design the feedback engineer to design real systems.
Proceedings ArticleDOI
A Modular Aero-Propulsion System Simulation of a Large Commercial Aircraft Engine
TL;DR: In this article, a simulation of a commercial engine has been developed in a graphical environment to meet the increasing need across the controls and health management community for a common research and development platform.