Home ›
Entities
› academia
› Deep learning framework for gas turbine performance digital twin and degradation prognostics from airline operator perspective
Deep learning framework for gas turbine performance digital twin and degradation prognostics from airline operator perspective
Research article (Reliability Engineering & System Safety, 2023) · cited 59× · AI/ML
Deep learning framework for gas turbine performance digital twin and degradation prognostics from airline operator perspective
Summary
Deep learning framework for gas turbine performance digital twin and degradation prognostics from airline operator perspective is a scholarly article[1].
Key Facts
Deep learning framework for gas turbine performance digital twin and degradation prognostics from airline operator perspective's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Deep learning framework for gas turbine performance digital twin and degradation prognostics from airline operator perspective. Retrieved May 24, 2026, from https://4ort.xyz/entity/deep-learning-framework-for-gas-turbine-performance-digital-twin-and-degradation-prognostics-from-airline-operator-persp
MLA“Deep learning framework for gas turbine performance digital twin and degradation prognostics from airline operator perspective.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/deep-learning-framework-for-gas-turbine-performance-digital-twin-and-degradation-prognostics-from-airline-operator-persp.
BibTeX@misc{4ortxyz_deep-learning-framework-for-gas-turbine-performance-digital-twin-and-degradation-prognostics-from-airline-operator-persp_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Deep learning framework for gas turbine performance digital twin and degradation prognostics from airline operator perspective}}, year = {2026}, url = {https://4ort.xyz/entity/deep-learning-framework-for-gas-turbine-performance-digital-twin-and-degradation-prognostics-from-airline-operator-persp}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Deep learning framework for gas turbine performance digital twin and degradation prognostics from airline operator perspective — https://4ort.xyz/entity/deep-learning-framework-for-gas-turbine-performance-digital-twin-and-degradation-prognostics-from-airline-operator-persp (retrieved 2026-05-24)