Paper Citation Record · LEDGER

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models

As of 16 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:1908.01529.

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

pith.paper-citation-record.v1
1908.01529 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:16:31.772318Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

verified exact1
verified fuzzy28
unresolved6
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b51df3e6-5348-4e61-a7fe-93a9c1f60d01 · outbound

This paper cites Deep Learning and Its Applications to Machine Health Monitoring: A Survey.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Deep Learning and Its Applications to Machine Health Monitoring: A Survey

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:32.351490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4babb0dd-30ab-45f7-90e6-03baae2b24dc · outbound

This paper cites A review on the application of deep learning in system health management, jul 2018.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models A review on the application of deep learning in system health management, jul 2018

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:32.336955Z

Source-reported events for the cited work

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Observation a24d0068-640f-4f8e-ae2c-00231fc19c82 · outbound

This paper cites Online class imbalance learning and its applications in fault detection.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Online class imbalance learning and its applications in fault detection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:32.322616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 63160c90-474b-472f-b125-83082da23867 · outbound

This paper cites Power distribution fault cause identiﬁcation with imbalanced data using the data mining-based fuzzy classiﬁcatione-algorithm.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Power distribution fault cause identiﬁcation with imbalanced data using the data mining-based fuzzy classiﬁcatione-algorithm

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:32.308144Z

Source-reported events for the cited work

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Observation 6a786dec-e52e-4ef2-8d47-ec0cf193972d · outbound

This paper cites Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:32.293139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 75201f36-667b-456b-b255-44b8d89d8611 · outbound

This paper cites Robust signal reconstruction for condition monitoring of industrial components via a modiﬁed auto associative kernel regression method.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Robust signal reconstruction for condition monitoring of industrial components via a modiﬁed auto associative kernel regression method

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:32.278257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ddfd504f-e25f-42ce-a3b1-0233ad84c13f · outbound

This paper cites Fault detection based on signal reconstruction with auto-associative extreme learning machines.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Fault detection based on signal reconstruction with auto-associative extreme learning machines

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:32.262801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0eb4206a-d33a-43b1-9f53-860f1511b062 · outbound

This paper cites Deep feature learning network for fault detection and isolation.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Deep feature learning network for fault detection and isolation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:32.247257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9d20c65b-b3b3-4dbc-a274-2937afc731c9 · outbound

This paper cites Domain Adaptive Transfer Learning for Fault Diagnosis.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Domain Adaptive Transfer Learning for Fault Diagnosis

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-14T15:16:31.820224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 76c70c3d-06ef-49d9-8bab-a5b57e425b96 · outbound

This paper cites User’s Guide for the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS).

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models User’s Guide for the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:32.232303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3ca1e426-c678-42b8-8e69-47e7c85ce3f9 · outbound

This paper cites an unresolved cited work.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-14T15:16:32.217176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 15f0c7ab-f9da-4a2c-8dd9-c8176b6b3b55 · outbound

This paper cites Support vector method for novelty detection.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Support vector method for novelty detection

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:32.202157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1ba006d3-9fc0-486b-bb0e-7e3a324765ee · outbound

This paper cites Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:32.186861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 28d272e9-7b83-478e-bfca-5492f9d8ae7a · outbound

This paper cites Hybrid Physics-Based and Data-Driven Phm.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Hybrid Physics-Based and Data-Driven Phm

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:32.171249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6545d800-be9b-40d5-9175-523fbaaa60d5 · outbound

This paper cites Rausch, Kai F.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Rausch, Kai F

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:32.156445Z

Source-reported events for the cited work

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Observation cccdcc14-79de-4325-9bad-ed9690b093cd · outbound

This paper cites Variations on the Kalman Filter for Enhanced Performance Monitoring of Gas Turbine Engines.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Variations on the Kalman Filter for Enhanced Performance Monitoring of Gas Turbine Engines

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:32.140772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 08587d85-2422-48dd-a2eb-df9cfacf515d · outbound

This paper cites Physics Guided Recurrent Neural Networks For Modeling Dynamical Systems: Application to Monitoring Water Temperature And Quality In Lakes.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Physics Guided Recurrent Neural Networks For Modeling Dynamical Systems: Application to Monitoring Water Temperature And Quality In Lakes

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:32.125593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0383165f-ffbc-48a7-98f7-21a6d8c9f1ca · outbound

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Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Unresolved cited work

Reference 18

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Source-reported events for the cited work

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Observation 84d88294-2f9e-4a7c-98ab-7ffad67b9f23 · outbound

This paper cites an unresolved cited work.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-14T15:16:32.095059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 06ff97b4-ecd3-4587-92de-a406887ecbe4 · outbound

This paper cites Wind Turbine Main Bearing Fatigue Life Estimation with Physics- informed Neural Networks.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Wind Turbine Main Bearing Fatigue Life Estimation with Physics- informed Neural Networks

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:32.080664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8514ecf6-30b5-47b1-8cff-49afc5052110 · outbound

This paper cites an unresolved cited work.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-14T15:16:32.065859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ebfdf77f-943c-4472-8933-0eea0d133708 · outbound

This paper cites Lilley, Peter Mathé, and V olker Schloßhauer.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Lilley, Peter Mathé, and V olker Schloßhauer

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:32.050739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4595cd44-fb8d-411b-9f4f-6e4c4ddea7ec · outbound

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Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-14T15:16:32.034255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 51931b56-32ad-4e67-9595-b65a37f51274 · outbound

This paper cites New extension of the Kalman ﬁlter to nonlinear systems.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models New extension of the Kalman ﬁlter to nonlinear systems

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:32.015145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e50cc8d4-466f-4408-9a20-972b249e58ed · outbound

This paper cites Moya and Don R.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Moya and Don R

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:31.998019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation de8aec0b-f1a4-434f-a60f-05c64be01e5e · outbound

This paper cites Feature Learning for Fault Detection in High- Dimensional Condition-Monitoring Signals.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Feature Learning for Fault Detection in High- Dimensional Condition-Monitoring Signals

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:31.981689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 38812f16-e9a6-401b-b2cc-1db8fc8e1fa9 · outbound

This paper cites Hierarchical Extreme Learning Machine for unsupervised representation learning.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Hierarchical Extreme Learning Machine for unsupervised representation learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:31.963120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c19849ec-bd6f-42e7-8c06-3588c6efbb8e · outbound

This paper cites Auto-encoding variational bayes.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Auto-encoding variational bayes

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:31.946677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 84cf3c34-0d69-41b7-893b-6eea252228f4 · outbound

This paper cites Pedregosa, G.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Pedregosa, G

Reference 29

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Source-reported events for the cited work

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Observation b5a146cf-5a9c-40ed-89eb-fcd04e5a88de · outbound

This paper cites Adam: A method for stochastic optimization.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Adam: A method for stochastic optimization

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:31.918762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c4c1bda1-f4de-43fa-b355-20bab7bb4a7d · outbound

This paper cites Understanding the difﬁculty of training deep feedforward neural networks.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Understanding the difﬁculty of training deep feedforward neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:31.902419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d26a0c0f-4baa-4f1a-9079-6a11f7cd9713 · outbound

This paper cites LeCun, Léon Bottou, Genevieve B.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models LeCun, Léon Bottou, Genevieve B

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:31.883374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 352c2739-2f84-40b2-9ee4-4dd9d894b458 · outbound

This paper cites Hands-on transfer learning with Python : implement advanced deep learning and neural network models using TensorFlow and Keras.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Hands-on transfer learning with Python : implement advanced deep learning and neural network models using TensorFlow and Keras

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:31.867395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation af1dae20-9782-4cf9-a059-c83685578d33 · outbound

This paper cites InfoV AE: Balancing Learning and Inference in Variational Autoencoders.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models InfoV AE: Balancing Learning and Inference in Variational Autoencoders

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:31.851389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d815c375-75ae-4557-93ce-df9bba0da77f · outbound

This paper cites Tutorial on Variational Autoencoders.

Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models Tutorial on Variational Autoencoders

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:16:31.836026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.