Paper Citation Record · LEDGER

A domain adaptation neural network for digital twin-supported fault diagnosis

As of 12 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2505.21046.

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

pith.paper-citation-record.v1
2505.21046 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:44:50.950882Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

30 of 30 outbound references displayed

verified exact2
verified fuzzy26
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d6c3ab63-d9ba-412b-8faf-56175c95daf7 · outbound

This paper cites Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing,.

A domain adaptation neural network for digital twin-supported fault diagnosis Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:56.793216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8e5706b6-173f-4638-a309-6d3af0866f9b · outbound

This paper cites Overview of predictive maintenance based on digital twin technology,.

A domain adaptation neural network for digital twin-supported fault diagnosis Overview of predictive maintenance based on digital twin technology,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:56.672655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 70f95f7a-acf9-4491-b94a-dbc64c66290d · outbound

This paper cites Digital twins: Review and challenges,.

A domain adaptation neural network for digital twin-supported fault diagnosis Digital twins: Review and challenges,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:56.541965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bf69f552-9b98-4aa6-956b-951a70a45ba4 · outbound

This paper cites A digital twin approach for fault diagnosis in distributed photovoltaic systems,.

A domain adaptation neural network for digital twin-supported fault diagnosis A digital twin approach for fault diagnosis in distributed photovoltaic systems,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:56.336589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 49909d15-9f06-4ce4-9f70-a0aab64a3e3f · outbound

This paper cites Digital twin for rotating machinery fault diagnosis in smart manufacturing,.

A domain adaptation neural network for digital twin-supported fault diagnosis Digital twin for rotating machinery fault diagnosis in smart manufacturing,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:56.102432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 691b9f37-c6a2-4b31-850b-a3a56bf67f8b · outbound

This paper cites Digital twin-driven fault diagnosis method for composite faults by combining virtual and real data,.

A domain adaptation neural network for digital twin-supported fault diagnosis Digital twin-driven fault diagnosis method for composite faults by combining virtual and real data,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:55.917469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 397c178e-460f-42f4-805a-a7a39b993cc9 · outbound

This paper cites A Survey of Predictive Maintenance: Systems, Purposes and Approaches.

A domain adaptation neural network for digital twin-supported fault diagnosis A Survey of Predictive Maintenance: Systems, Purposes and Approaches

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:44:51.362726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 028ab9fc-f8b8-4d6c-a08f-ba4466fbdbc6 · outbound

This paper cites Use Digital Twins to Support Fault Diagnosis From System-level Condition-monitoring Data.

A domain adaptation neural network for digital twin-supported fault diagnosis Use Digital Twins to Support Fault Diagnosis From System-level Condition-monitoring Data

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:44:51.164542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 29a8111e-d565-484c-b202-417462448474 · outbound

This paper cites Unsupervised domain adaptation by backpropagation,.

A domain adaptation neural network for digital twin-supported fault diagnosis Unsupervised domain adaptation by backpropagation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:55.716724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2aad7bed-cda1-49e1-9dfc-aaa405655580 · outbound

This paper cites Long short-term memory,.

A domain adaptation neural network for digital twin-supported fault diagnosis Long short-term memory,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:55.517158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bfe7251f-896d-497c-b837-a7daa89e4694 · outbound

This paper cites Attention is all you need,.

A domain adaptation neural network for digital twin-supported fault diagnosis Attention is all you need,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:49.271497Z

Source-reported events for the cited work

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Observation 9f10f6d8-7ad3-49b2-af58-3e7dd13eca6a · outbound

This paper cites Handwritten digit recognition with a back-propagation network,.

A domain adaptation neural network for digital twin-supported fault diagnosis Handwritten digit recognition with a back-propagation network,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:55.238600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8c45a43c-2136-459d-b266-f50be5f95ac2 · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

A domain adaptation neural network for digital twin-supported fault diagnosis An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:49.518992Z

Source-reported events for the cited work

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Observation f777c52e-ca55-45c6-a722-71d26974fd62 · outbound

This paper cites Deep learning based approach for bearing fault diagnosis,.

A domain adaptation neural network for digital twin-supported fault diagnosis Deep learning based approach for bearing fault diagnosis,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:55.055262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 976c6389-1ef2-46fa-9979-8342340678b8 · outbound

This paper cites Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks,.

A domain adaptation neural network for digital twin-supported fault diagnosis Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:54.889884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation caa7d113-9d5b-4106-baa3-0a9f77af2414 · outbound

This paper cites Planetary gearbox fault diagnosis using bidirectional-convolutional lstm networks,.

A domain adaptation neural network for digital twin-supported fault diagnosis Planetary gearbox fault diagnosis using bidirectional-convolutional lstm networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:54.693267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 42bd46cc-a701-49e9-9136-f83b20ad02a3 · outbound

This paper cites Fault detection and identification of rolling element bearings with attentive dense cnn,.

A domain adaptation neural network for digital twin-supported fault diagnosis Fault detection and identification of rolling element bearings with attentive dense cnn,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:54.472956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1df1a66a-edfe-4c56-9f43-c25244880228 · outbound

This paper cites A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges,.

A domain adaptation neural network for digital twin-supported fault diagnosis A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:54.308831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a1d4a7ec-f56c-4d20-8705-ede7116a86ea · outbound

This paper cites Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder,.

A domain adaptation neural network for digital twin-supported fault diagnosis Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:54.109952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9731c062-5f87-46eb-9d07-423bbf38d1dc · outbound

This paper cites Unsupervised domain- share cnn for machine fault transfer diagnosis from steady speeds to time-varying speeds,.

A domain adaptation neural network for digital twin-supported fault diagnosis Unsupervised domain- share cnn for machine fault transfer diagnosis from steady speeds to time-varying speeds,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:53.785906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 00ce64f3-f274-4a8f-a97b-2cf4c347bc1f · outbound

This paper cites Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data,.

A domain adaptation neural network for digital twin-supported fault diagnosis Deep convolutional transfer learning network: A new method for intelligent fault diagnosis of machines with unlabeled data,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:53.550143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 49686fe1-f154-42de-b9fb-0f670e31a4c2 · outbound

This paper cites A cross-domain stacked denoising autoencoders for rotating machinery fault diagnosis under different working conditions,.

A domain adaptation neural network for digital twin-supported fault diagnosis A cross-domain stacked denoising autoencoders for rotating machinery fault diagnosis under different working conditions,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:53.302574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a2571215-34f1-40f8-b034-934517b2ea64 · outbound

This paper cites Domain adaptive motor fault diagnosis using deep transfer learning,.

A domain adaptation neural network for digital twin-supported fault diagnosis Domain adaptive motor fault diagnosis using deep transfer learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:53.135213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7c166bdd-2557-4985-82f9-9533308bfdc6 · outbound

This paper cites An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings,.

A domain adaptation neural network for digital twin-supported fault diagnosis An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:52.855580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0125a7ac-f75a-4104-a520-867eddd3762f · outbound

This paper cites Improved deep transfer auto- encoder for fault diagnosis of gearbox under variable working conditions with small training samples,.

A domain adaptation neural network for digital twin-supported fault diagnosis Improved deep transfer auto- encoder for fault diagnosis of gearbox under variable working conditions with small training samples,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:52.572563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b50565a7-d4df-4e1e-9fec-5f7024c5ed78 · outbound

This paper cites A new parameter repurposing method for parameter transfer with small dataset and its application in fault diagnosis of rolling element bearings,.

A domain adaptation neural network for digital twin-supported fault diagnosis A new parameter repurposing method for parameter transfer with small dataset and its application in fault diagnosis of rolling element bearings,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:52.350364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ca1b3f28-e338-4c33-8cae-3fd4e9d1027f · outbound

This paper cites Highly accurate machine fault diagnosis using deep transfer learning,.

A domain adaptation neural network for digital twin-supported fault diagnosis Highly accurate machine fault diagnosis using deep transfer learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:52.094472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e35f58df-89ac-41da-adb9-3a9eb80f1ada · outbound

This paper cites Wasserstein distance based deep adversarial transfer learning for intelligent fault diagnosis with unlabeled or insufficient labeled data,.

A domain adaptation neural network for digital twin-supported fault diagnosis Wasserstein distance based deep adversarial transfer learning for intelligent fault diagnosis with unlabeled or insufficient labeled data,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:51.937878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b04dc287-cc16-416f-b56c-412fffe8823b · outbound

This paper cites Da-dcgan: An effective methodology for dc series arc fault diagnosis in photovoltaic systems,.

A domain adaptation neural network for digital twin-supported fault diagnosis Da-dcgan: An effective methodology for dc series arc fault diagnosis in photovoltaic systems,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:51.790573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d1850dbc-a117-467c-a570-d3971fb47fe8 · outbound

This paper cites Improving predictive inference under covariate shift by weighting the log-likelihood function,.

A domain adaptation neural network for digital twin-supported fault diagnosis Improving predictive inference under covariate shift by weighting the log-likelihood function,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:44:51.574756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.