Paper Citation Record · LEDGER

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation

As of 16 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 2 inbound Pith citation observations for arXiv:2412.15998.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.15998 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T10:57:21.416884Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:35:35.146580Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T08:19:44.620254Z

Reference resolution

42 of 42 outbound references displayed

verified exact1
verified fuzzy39
unresolved2
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 63949598-c255-407a-bea8-3e0c8b911df1 · outbound

This paper cites Accurate RUL estimation plays a crucial role in Predictive Maintenance applications.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Accurate RUL estimation plays a crucial role in Predictive Maintenance applications

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.916969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2da5b642-cfbe-4d18-a5f3-a40bf94a070e · outbound

This paper cites an unresolved cited work.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-11T10:57:22.895267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f1e5528d-297b-4d0c-a78e-7391844add2a · outbound

This paper cites CNN have great potential to identify the various salient patterns of sensor signals.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation CNN have great potential to identify the various salient patterns of sensor signals

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.841045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e0dea3c2-80aa-4c3a-9914-986563c9bfd0 · outbound

This paper cites Remaining Cycles,.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Remaining Cycles,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.810420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e91690f3-54ba-45e3-a095-cc8642a0e53c · outbound

This paper cites an unresolved cited work.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-11T10:57:22.782526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a8d32c65-0cb1-4a53-9c08-d95460d3358e · outbound

This paper cites Our experiments on C-MAPSS dataset showed that our proposed model outperforms other approaches and gives the best performance in RUL estimation.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Our experiments on C-MAPSS dataset showed that our proposed model outperforms other approaches and gives the best performance in RUL estimation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.749853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cc2b2d4d-efd5-4aa4-83f9-7eb8f90b899f · outbound

This paper cites REFERENCES :.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation REFERENCES :

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.719693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 877e3795-46bd-4ea4-9d4e-5836e6bb16c8 · outbound

This paper cites XGBoost Documentation.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation XGBoost Documentation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.449025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ad1846f9-bf81-45a3-9f28-6bf94e2ac973 · outbound

This paper cites A generic conceptual simulation model for maintenance systems,.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation A generic conceptual simulation model for maintenance systems,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.689083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 75c0c312-fa9f-4252-95d5-4ef9443c9dcd · outbound

This paper cites Remaining useful life estimation–a review on the statistical data driven approaches,.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Remaining useful life estimation–a review on the statistical data driven approaches,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.642080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 50f69fba-2384-4882-98f5-ef1a007b735e · outbound

This paper cites Recurrent neural networks for remaining useful life estimation.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Recurrent neural networks for remaining useful life estimation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.618264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 92cd1f17-da70-4cde-ac6f-3c04b6c1c91d · outbound

This paper cites Deep convolutional neural network based regression approach for estimation of remaining useful life,.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Deep convolutional neural network based regression approach for estimation of remaining useful life,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.586079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3db90f60-edbe-41e9-aa35-0e3ef6988491 · outbound

This paper cites Long Short -Term Memory Network for Remaining Useful Life Estimation,.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Long Short -Term Memory Network for Remaining Useful Life Estimation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.535109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8ba6c275-226d-40b3-9fc4-66234f977de1 · outbound

This paper cites Random Forests.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Random Forests

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.501150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f36a8525-6db2-48ee-82be-691bd1b3c327 · outbound

This paper cites XGBoost: A Scalable Tree Boosting System.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation XGBoost: A Scalable Tree Boosting System

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.473222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9b0f54e5-017e-4c48-974c-a6253842db79 · outbound

This paper cites Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.172546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 38f4dc9a-66c1-4944-9994-1d6506873be5 · outbound

This paper cites Scikit-learn Documentation: MLPRegressor.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Scikit-learn Documentation: MLPRegressor

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.429580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f3032f45-c5a1-4c6e-9987-fc9a4d115141 · outbound

This paper cites The Digital Twin Paradigm for Smarter Systems and Environments: The Industry Use Cases,.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation The Digital Twin Paradigm for Smarter Systems and Environments: The Industry Use Cases,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.390461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 34c7edc3-7b54-4614-b12f-8f6493afcdda · outbound

This paper cites Hyperdimensional Data Analysis Using Parallel Coordinates,.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Hyperdimensional Data Analysis Using Parallel Coordinates,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.351532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 86de695b-89d1-44fb-bd4f-c83ccdd35ed9 · outbound

This paper cites Log based predictive maintenance.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Log based predictive maintenance

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.297174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 24f802dd-953f-48a4-821c-9965090cbbae · outbound

This paper cites Predictive maintenance on event logs: Application on an ATM fleet.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Predictive maintenance on event logs: Application on an ATM fleet

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-11T10:57:21.494360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e50c350b-7501-4bad-b989-15c14c05d650 · outbound

This paper cites Vibration analysis for IOT enabled predictive maintenance.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Vibration analysis for IOT enabled predictive maintenance

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.269346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f95e7192-632c-469c-a92e-f3a8d9fc97b9 · outbound

This paper cites Predicting Bearings Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Predicting Bearings Degradation Stages for Predictive Maintenance in the Pharmaceutical Industry

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.212204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6bf95a00-4325-4593-852c-f013f7d05bf6 · outbound

This paper cites Performance benchmarking and analysis of prognostic methods for cmapss datasets.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Performance benchmarking and analysis of prognostic methods for cmapss datasets

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:21.897999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7f2c1cf6-a0b7-4862-9723-290f8e46ac49 · outbound

This paper cites Support-vector networks.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Support-vector networks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.133683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c43a2d0d-3d1e-47ec-a711-06d772d007e3 · outbound

This paper cites A similarity-based prognostics approach for remaining useful life estimation of engineered systems.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation A similarity-based prognostics approach for remaining useful life estimation of engineered systems

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.102227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 99217593-7a42-4598-b4e0-3da96b17d8c2 · outbound

This paper cites Estimation of remaining useful life based on switching kalman filter neural network ensemble.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Estimation of remaining useful life based on switching kalman filter neural network ensemble

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.071615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9590a0fd-97b3-4c99-a147-5ccee5a2ae8a · outbound

This paper cites The existing algorithms in the literature for RUL estimation are either based on multivariate time series analysis or damage progression analysis [3, 18, 19, 20, 26].

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation The existing algorithms in the literature for RUL estimation are either based on multivariate time series analysis or damage progression analysis [3, 18, 19, 20, 26]

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.864359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 006b4f62-46ae-486f-96d2-6ce6addb3736 · outbound

This paper cites Review and analysis of algorithmic approaches developed for prognostics on CMAPSS dataset.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Review and analysis of algorithmic approaches developed for prognostics on CMAPSS dataset

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:22.037394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c27c0e3e-0309-4e77-ad00-bb22fde9c250 · outbound

This paper cites Applying LSTM to time series predictable through time-window approaches,.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Applying LSTM to time series predictable through time-window approaches,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:21.992922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a2b4dd44-e56b-48e8-9c7a-1ef0e152b0d5 · outbound

This paper cites An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring,.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:21.955800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7beb19a7-b28c-4d22-9dfd-7b8da7aeebba · outbound

This paper cites Long short-term memory,.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Long short-term memory,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:21.919913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9a132814-dfc1-4a89-ab29-988fd1e18600 · outbound

This paper cites Recurrent neural networks and robust time series prediction.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Recurrent neural networks and robust time series prediction

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:21.850876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d866294f-adb1-420d-b9a9-dbc15bbd2dc3 · outbound

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

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Damage propagation modeling for aircraft engine run-to-failure simulation

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:21.825274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bd1b2bb8-d90b-4b12-bcbd-f7601b42afb1 · outbound

This paper cites Deep convolutional neural networks on multichannel time series for human activity recognition.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Deep convolutional neural networks on multichannel time series for human activity recognition

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:21.800461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2b71728e-f0d8-476d-9cf4-dc7f60bc3ddf · outbound

This paper cites Towards Sequential Multivariate Fault Prediction for Vehicular Predictive Maintenance.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Towards Sequential Multivariate Fault Prediction for Vehicular Predictive Maintenance

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:21.770912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ed9842f5-1823-48ba-8c0e-74f09dd8b71e · outbound

This paper cites Fault Detection and Predictive Maintenance of Electrical Machines.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Fault Detection and Predictive Maintenance of Electrical Machines

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:21.736846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 443622a8-31ef-42c6-93b1-174f6286d3aa · outbound

This paper cites Machine learning for predictive maintenance: A multiple classifier approach.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Machine learning for predictive maintenance: A multiple classifier approach

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:21.690572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bdac918b-1964-44a4-a9a8-72a29a52dc1f · outbound

This paper cites Turbofan Engine Degradation Simulation Data Set.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Turbofan Engine Degradation Simulation Data Set

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:21.659531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 95ae44d5-8966-40b5-9b26-9f79e122d95b · outbound

This paper cites Remaining useful life prediction using multi-scale deep convolutional neural network.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Remaining useful life prediction using multi-scale deep convolutional neural network

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:21.623272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 91b10bdf-9249-471f-99c2-1325f19665d6 · outbound

This paper cites Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:21.568716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e977e685-abad-4919-9928-7c12fc30773d · outbound

This paper cites Evaluation of neural networks in the subject of prognostics as compared to linear regression model.

CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation Evaluation of neural networks in the subject of prognostics as compared to linear regression model

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T10:57:21.545081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation 3ccaf0eb-b0c7-45e5-84e3-7fd4c47deda8 · inbound

From Handcrafted Features to Functional Edge Learning: Evolution of EEG Seizure Detection Frameworks cites this paper.

From Handcrafted Features to Functional Edge Learning: Evolution of EEG Seizure Detection Frameworks CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:19:44.621721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0977f2c4-a195-4b17-96de-13773a1f2ea4 · inbound

Predictive Failure Detection in Network Hardware Using Thermal Imaging and Deep Learning with Sensor Fusion cites this paper.

Predictive Failure Detection in Network Hardware Using Thermal Imaging and Deep Learning with Sensor Fusion CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T00:35:35.146580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.