“…Advanced Control of Turbofan Engines describes the operational performance requirements of turbofan (commercial)engines from a controls systems perspective, covering industry-standard methods and research-edge advances. …”
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“…In turbofan engine datasets, to address problems, such as noise interference, diverse data types, large data volumes, complex feature extraction, inability to effectively describe degradation trends, and poor remaining useful life (RUL) prognosis effects, a remaining useful life prognosis model combining an improved stack sparse autoencoder (imSSAE) and an improved echo state network (imESN) is proposed in this paper. …”
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“…The prognosis of the remaining useful life (RUL) of turbofan engine provides an important basis for predictive maintenance and remanufacturing, and plays a major role in reducing failure rate and maintenance costs. …”
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“…As a kind of gas turbine engines, turbofan engines have powered a number of aero-vehicles in aviation sector. …”
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“…This study prognoses the remaining useful life of a turbofan engine using a deep learning model, which is essential for the health management of an engine. …”
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“…The time-series data generated by turbofan engines has a great degree of complexity and dynamics. …”
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“…The experimental results show that the method is effective and reasonable in dealing with risk analysis in the air system of an aero turbofan engine.…”
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“…The sprayDyMFoam solver is used to simulate a typical double-rotor turbofan engine: the calculation capacity and efficiency meet the use requirements, and the obtained compressor performance can form a good match with the test. …”
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“…Airbreathing Propulsion covers the physics of combustion, fluid and thermo-dynamics, and structural mechanics of airbreathing engines, including piston, turboprop, turbojet, turbofan, and ramjet engines. End-of-chapter exercises allow the reader to practice the fundamental concepts behind airbreathing propulsion, and the included PAGIC computer code will help the reader to examine the relationships between the performance parameters of different engines. …”
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“…Topics include: car and aircraft engines,  including Otto, Diesel and Atkinson cycles, by-pass turbofan engines, ramjet and scramjet;  steam and gas power plants, including advanced regenerative systems, solar tower, and compressed air energy storage; mixing and separation, including reverse osmosis, osmotic powerplants, and carbon sequestration; phase equilibrium and chemical equilibrium, distillation, chemical reactors, combustion processes, and fuel cells; the microscopic definition of entropy.    …”
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“…Fundamentals of Aircraft and Rocket Propulsion provides information about and analyses of: thermodynamic cycles of shaft engines (piston, turboprop, turboshaft and propfan); jet engines (pulsejet, pulse detonation engine, ramjet, scramjet, turbojet and turbofan); chemical and non-chemical rocket engines; conceptual design of modular rocket engines (combustor, nozzle and turbopumps); and conceptual design of different modules of aero-engines in their design and off-design state. …”
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“…To evaluate the proposed approach, two public multi-sensor data sets are used for the remaining useful life prediction applications: (1) CMAPSS turbofan engine dataset, and (2) FEMTO Pronostia rolling element bearing data set. …”
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“…As an additional benchmark we use a simulated turbofan engine data set provided by NASA. We also use explainability methods in order to understand the model’s predictions. …”
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“…To improve the intershaft seal performance of the dual-rotor turbofan engine and extend the life of the intershaft seal, a compliant cylindrical aerodynamic intershaft seal structure is proposed, which avoids the problem of leakage increase after tooth wear of intershaft labyrinth seal. …”
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“…This paper proposed an integrated deep learning approach for RUL prediction of a turbofan engine by integrating an autoencoder (AE) with a deep convolutional generative adversarial network (DCGAN). …”
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“…Based on a subset of the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset as case studies, the Cox Proportional Hazards Model (Cox PHM) with time-varying covariates is utilised to generate the reliability indices of individual turbofan units. Afterwards, a Vector Autoregressive model with Exogenous variables (VARX) combined with pairwise Conditional Granger Causality (CGC) tests for sensor selections is defined to model the time-varying influence of sensor signals on the reliability indices of different units that have been previously generated by the Cox PHM with time-varying covariates. …”
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“…The proposed method is demonstrated on a real-world dataset from a typical type of commercial turbofan engine and the result shows that the F1 score reaches a maximum of 0.99 with a threshold of 0.45. …”
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“…In this data article, a reconstructed database, which provides information from PHM08 challenge data set, is presented. The original turbofan engine data were from the Prognostic Center of Excellence (PCoE) of NASA Ames Research Center (Saxena and Goebel, 2008), and were simulated by the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) (Saxena et al., 2008). …”
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