Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics | Paper Citation Record

Observation 39008e72-f0ad-4db8-af1e-a3537a467b70 · outbound

This paper cites An lstm network for highway trajectory prediction.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics An lstm network for highway trajectory prediction

Reference 1

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This paper cites A prognostics approach to nuclear component degradation modeling based on gaussian process regression.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics A prognostics approach to nuclear component degradation modeling based on gaussian process regression

Reference 2

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Observation 2b7aaf6a-2737-4816-8b1a-5796440cdd44 · outbound

This paper cites On the importance of temporal information for remaining useful life prediction of rolling bearings using a random forest regressor.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics On the importance of temporal information for remaining useful life prediction of rolling bearings using a random forest regressor

Reference 3

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This paper cites Random forests.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Random forests

Reference 4

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This paper cites Classification and regression trees.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Classification and regression trees

Reference 5

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This paper cites Life prediction for turbo- propulsion systems under dwell fatigue conditions.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Life prediction for turbo- propulsion systems under dwell fatigue conditions

Reference 6

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Observation fb9e7ad5-9e2b-4cb3-ba63-9533c0de83c6 · outbound

This paper cites Prediction interval estimation of aeroengine remaining useful life based on bidirectional long short-term memory network.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Prediction interval estimation of aeroengine remaining useful life based on bidirectional long short-term memory network

Reference 7

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Observation d3448509-7bdf-4b9d-afd3-3b140e62637f · outbound

This paper cites Direct remaining useful life estimation based on random forest regression.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Direct remaining useful life estimation based on random forest regression

Reference 8

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Observation 9a59e2bc-0b5f-466d-b73d-b98b16467e66 · outbound

This paper cites Machine remaining useful life prediction via an attention-based deep learning approach.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Machine remaining useful life prediction via an attention-based deep learning approach

Reference 9

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Observation 6ef13093-82f1-4ec6-a040-e59369ba3a52 · outbound

This paper cites A dual-stage attention-based bi-lstm network for multivariate time series prediction.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics A dual-stage attention-based bi-lstm network for multivariate time series prediction

Reference 10

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Observation 7a861679-dcb3-4c8a-a4ec-7b7d7822436a · outbound

This paper cites Essential steps in prognostic health management.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Essential steps in prognostic health management

Reference 11

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Observation 3ecb2e97-c726-4d10-a635-0f7d0bb1af70 · outbound

This paper cites Remaining useful life prediction and challenges: A literature review on the use of machine learning methods.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Remaining useful life prediction and challenges: A literature review on the use of machine learning methods

Reference 12

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Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Unresolved cited work

Reference 13

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This paper cites User’s guide for the commercial modular aero-propulsion system simulation (c-mapss).

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics User’s guide for the commercial modular aero-propulsion system simulation (c-mapss)

Reference 14

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Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Unresolved cited work

Reference 15

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Observation 5557b72e-e1d7-457f-8c7e-1b3d0e5bd5fd · outbound

This paper cites Long short-term memory.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Long short-term memory

Reference 16

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This paper cites Long short-term memory.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Long short-term memory

Reference 17

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Observation c86de479-18fe-4347-a7de-e873f11932ed · outbound

This paper cites Remaining useful life prognosis of bearing based on gauss process regression.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Remaining useful life prognosis of bearing based on gauss process regression

Reference 18

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Observation c48c1924-cc90-49ab-9c3d-ff6f58f18f76 · outbound

This paper cites Time series data prediction using sliding window based rbf neural network.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Time series data prediction using sliding window based rbf neural network

Reference 19

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Observation 9b7711fe-e06b-4a98-a81e-585ed8242139 · outbound

This paper cites Soh and rul prediction of lithium-ion batteries based on gaussian process regression with indirect health indicators.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Soh and rul prediction of lithium-ion batteries based on gaussian process regression with indirect health indicators

Reference 20

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Observation 6d89cb60-9ec0-4d65-985f-80be6806cbd5 · outbound

This paper cites Machinery health prognostics: A systematic review from data acquisition to rul prediction.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Machinery health prognostics: A systematic review from data acquisition to rul prediction

Reference 21

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Observation 09aa9c87-b506-473d-b88a-c645071cdf6e · outbound

This paper cites A wiener-process-model-based method for remaining useful life prediction considering unit-to-unit variability.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics A wiener-process-model-based method for remaining useful life prediction considering unit-to-unit variability

Reference 22

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Observation 141c10d4-a6c4-43b2-be24-c7e56ded3885 · outbound

This paper cites Random forest regression for online capacity estimation of lithium-ion batteries.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Random forest regression for online capacity estimation of lithium-ion batteries

Reference 23

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Observation 5f41e490-6a08-46e8-aeb0-e7f7630b8214 · outbound

This paper cites Uncertainty prediction of remaining useful life using long short-term memory network based on bootstrap method.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Uncertainty prediction of remaining useful life using long short-term memory network based on bootstrap method

Reference 24

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Observation 00bd1d7f-ddd5-4ff1-9a1c-9a0a835998d5 · outbound

This paper cites Multiple sensors based prognostics with prediction interval optimization via echo state gaussian process.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Multiple sensors based prognostics with prediction interval optimization via echo state gaussian process

Reference 25

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Observation 790cca66-7600-4ea4-b5f7-61e1f9c995af · outbound

This paper cites A novel deep learning-based encoder-decoder model for remaining useful life prediction.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics A novel deep learning-based encoder-decoder model for remaining useful life prediction

Reference 26

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Observation c737ca2d-98bf-4135-bb25-874e128c9fee · outbound

This paper cites Uncertainty quantification and interval prediction of equipment remaining useful life based on semi-supervised learning.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Uncertainty quantification and interval prediction of equipment remaining useful life based on semi-supervised learning

Reference 27

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Observation 22b07e46-cb20-49be-9ac0-321ac631e69f · outbound

This paper cites Remaining useful life prediction of lithium-ion batteries based on health indicator and gaussian process regression model.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Remaining useful life prediction of lithium-ion batteries based on health indicator and gaussian process regression model

Reference 28

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Observation ebbd4a7e-0b57-4a2a-a7cb-4938fa3ec5cd · outbound

This paper cites Prediction of remaining useful life of multi-stage aero-engine based on clustering and lstm fusion.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Prediction of remaining useful life of multi-stage aero-engine based on clustering and lstm fusion

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:26.172866Z

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Observation 33152061-b638-43dd-835a-cddc557ec954 · outbound

This paper cites Gaussian process regression with automatic relevance determination kernel for calendar aging prediction of lithium-ion batteries.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Gaussian process regression with automatic relevance determination kernel for calendar aging prediction of lithium-ion batteries

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:26.152210Z

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Observation 86051642-6be3-4500-8450-b1f090e80c91 · outbound

This paper cites Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture

Reference 31

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Observation a72895c7-0e45-4aa0-aa35-ac110445b418 · outbound

This paper cites Deep-convolution-based lstm network for remaining useful life prediction.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Deep-convolution-based lstm network for remaining useful life prediction

Reference 32

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Observation 5fd1e0d2-869f-49c4-8e08-c6f199a1b168 · outbound

This paper cites Evolutionary neural architecture search for remaining useful life prediction.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Evolutionary neural architecture search for remaining useful life prediction

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:26.059247Z

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Observation eff5e394-5ee5-4100-b184-2c3570fa0637 · outbound

This paper cites Stock market’s price movement prediction with lstm neural networks.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Stock market’s price movement prediction with lstm neural networks

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:26.037726Z

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Observation f3d7a99d-78ab-4115-b999-4bfe6199f512 · outbound

This paper cites A lithium-ion battery remaining useful life prediction method based on the incremental capacity analysis and gaussian process regression.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics A lithium-ion battery remaining useful life prediction method based on the incremental capacity analysis and gaussian process regression

Reference 35

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Observation dc750921-6632-497c-9a6d-fe46fb3ea982 · outbound

This paper cites An interval prediction approach based on fuzzy information granulation and linguistic description for remaining useful life of lithium- ion batteries.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics An interval prediction approach based on fuzzy information granulation and linguistic description for remaining useful life of lithium- ion batteries

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:25.976937Z

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Observation 567bb83d-e87e-49d2-b21a-5aa0b8cfc6a3 · outbound

This paper cites Remaining useful life (rul) prediction of rolling element bearing using random forest and gradient boosting technique.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Remaining useful life (rul) prediction of rolling element bearing using random forest and gradient boosting technique

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:25.954932Z

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Observation 0e8cfba1-5f94-48ee-815d-6e7caa3b3cb2 · outbound

This paper cites Gaussian process regression for forecasting battery state of health.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Gaussian process regression for forecasting battery state of health

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:25.931387Z

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Observation 1e90d9bb-3a16-4493-b12d-13f66f4f62cd · outbound

This paper cites Damage propagation modeling for aircraft engine run-to-failure simulation.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Damage propagation modeling for aircraft engine run-to-failure simulation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:25.897573Z

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Observation f6de3ac5-5078-40b9-bd69-ee3b80a3890e · outbound

This paper cites A tutorial on gaussian process regression: Modelling, exploring, and exploiting functions.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics A tutorial on gaussian process regression: Modelling, exploring, and exploiting functions

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:25.862086Z

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Observation 2987b0e1-bb5b-4bdd-8cc5-3f012b5886f0 · outbound

This paper cites A dual attention lstm lightweight model based on exponential smoothing for remaining useful life prediction.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics A dual attention lstm lightweight model based on exponential smoothing for remaining useful life prediction

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:25.835231Z

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Observation eafef1bb-3eb5-4c06-b26d-38a40c792025 · outbound

This paper cites A dual-lstm framework combining change point detection and remaining useful life prediction.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics A dual-lstm framework combining change point detection and remaining useful life prediction

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:25.811077Z

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Observation ca5ad3ed-c5a2-4633-87a0-35514a3db989 · outbound

This paper cites The improvement of remaining useful life prediction for aero-engines by classification and deep learning.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics The improvement of remaining useful life prediction for aero-engines by classification and deep learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:25.792216Z

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Observation 76bfe75e-065d-40d7-855d-c855e0421898 · outbound

This paper cites Remaining useful life (rul) prediction of bearing by using regression model and principal component analysis (pca) technique.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Remaining useful life (rul) prediction of bearing by using regression model and principal component analysis (pca) technique

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:25.769923Z

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Observation 2c91df77-c089-43aa-a8aa-92a7c466246a · outbound

This paper cites Attention is all you need.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Attention is all you need

Reference 45

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Observation bf5bb064-35ce-4bd6-9d03-dac25f89123d · outbound

This paper cites Adaptive sliding window lstm nn based rul prediction for lithium-ion batteries integrating ltsa feature reconstruction.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Adaptive sliding window lstm nn based rul prediction for lithium-ion batteries integrating ltsa feature reconstruction

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:25.732486Z

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Observation f324704f-03cd-49d6-a185-5f37276f9fb8 · outbound

This paper cites Multicellular lstm-based deep learning model for aero-engine remaining useful life prediction.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Multicellular lstm-based deep learning model for aero-engine remaining useful life prediction

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:25.710130Z

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Observation ba93db18-3035-4f68-a815-a4c17a66354e · outbound

This paper cites Multiple sensor data fusion for degradation modeling and prognostics under multiple operational conditions.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Multiple sensor data fusion for degradation modeling and prognostics under multiple operational conditions

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:25.685732Z

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Observation fbc6033c-f177-4500-97b8-a676e7361a98 · outbound

This paper cites A machine-learning prediction method of lithium-ion battery life based on charge process for different applications.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics A machine-learning prediction method of lithium-ion battery life based on charge process for different applications

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:25.659627Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2b5e6491-a923-499d-9b8e-1e8d6374f738 · outbound

This paper cites Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:25.637394Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 72115a81-8bbf-4c7f-9806-10595ca70d88 · outbound

This paper cites An integrated multi-head dual sparse self-attention network for remaining useful life prediction.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics An integrated multi-head dual sparse self-attention network for remaining useful life prediction

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:25.616521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4ee06687-01e0-4330-9e1e-46caa9e45c76 · outbound

This paper cites Remaining useful life prediction for lithium-ion batteries based on exponential model and particle filter.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Remaining useful life prediction for lithium-ion batteries based on exponential model and particle filter

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:25.591996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 05b895d5-9ce7-4505-99b5-12520674d886 · outbound

This paper cites Long short-term memory network for remaining useful life estimation.

Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics Long short-term memory network for remaining useful life estimation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T17:54:25.564906Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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