Paper Citation Record · LEDGER

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis

As of 20 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2509.09251.

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

pith.paper-citation-record.v1
2509.09251 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:27:53.131940Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

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verified fuzzy0
unresolved35
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malformed identifier0
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External citation measurements

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Outbound references

Observation f3570eab-a432-4416-bd7c-2052ed46ae2a · outbound

This paper cites an unresolved cited work.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:50.780680Z

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Unavailable: canonical work link unavailable.

Observation cb9a6f55-f661-43c1-afc3-fc3feaba26ee · outbound

This paper cites Isermann.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Isermann

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:50.910910Z

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Observation 59f8551a-1881-444c-a859-1b904cf52a83 · outbound

This paper cites SSD-Faster Net: A Hybrid Network for Industrial Defect Inspection.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis SSD-Faster Net: A Hybrid Network for Industrial Defect Inspection

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:51.010383Z

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Observation 00bf2243-b174-4205-b0e4-f6c9c95c9fe3 · outbound

This paper cites Moraru, M.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Moraru, M

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:51.151345Z

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Observation 3b019675-a2e2-45da-8720-ec09b5edf5bf · outbound

This paper cites an unresolved cited work.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Unresolved cited work

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:51.268202Z

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Observation c17de84e-8089-4451-b566-1bc4fa75bd94 · outbound

This paper cites Blasch, T.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Blasch, T

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:51.335793Z

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Observation b62e2415-9a34-4969-a7dd-a218ca9521f2 · outbound

This paper cites an unresolved cited work.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:51.394947Z

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Observation b531dc57-bd15-49b1-aec2-d7dafaf25fb8 · outbound

This paper cites Image-based Freeform Handwriting Authentication with Energy-oriented Self-Supervised Learning.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Image-based Freeform Handwriting Authentication with Energy-oriented Self-Supervised Learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:51.443781Z

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Observation 824a90f7-0601-4048-abb5-ca34b09e892d · outbound

This paper cites an unresolved cited work.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:51.489130Z

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Observation e1696e5e-1e6d-4640-ba3e-bccb76989d3b · outbound

This paper cites Camps-Valls.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Camps-Valls

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:51.546493Z

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Observation 9c76484c-ee4f-43b8-8ea0-97231860c81e · outbound

This paper cites Gupta, M.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Gupta, M

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:51.592529Z

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Observation 2ede0c72-caa2-4495-af88-8efe5d227f69 · outbound

This paper cites an unresolved cited work.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:51.667250Z

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Observation 517d5a22-4cea-4d20-9c8b-520015b07aaf · outbound

This paper cites Advanc- ing complex wide-area scene understanding with hierarchical coresets selection.arXiv preprint arXiv:2507.13061, 2025.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Advanc- ing complex wide-area scene understanding with hierarchical coresets selection.arXiv preprint arXiv:2507.13061, 2025

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:51.710571Z

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Observation f68b55eb-5794-4bb2-8ebf-77a868c8a9fd · outbound

This paper cites Model-agnostic meta- learning for fast adaptation of deep networks.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Model-agnostic meta- learning for fast adaptation of deep networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:51.780320Z

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Observation 718a71f3-72d5-46ac-898a-3c2061f6c667 · outbound

This paper cites On the Universality of Self-Supervised Learning.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis On the Universality of Self-Supervised Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:51.863790Z

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Observation 0a3007e7-38d7-4814-8120-ea1c2fa698ef · outbound

This paper cites Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:51.925502Z

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Observation 74587ee0-6391-49d5-96df-24963d97c453 · outbound

This paper cites Meta-Auxiliary Learning for Micro-Expression Recognition.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Meta-Auxiliary Learning for Micro-Expression Recognition

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:52.000401Z

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Unavailable: canonical work link unavailable.

Observation 2a1b3568-d3cf-4040-b4d2-fb218c5a5a8d · outbound

This paper cites Towards task sampler learning for meta-learning.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Towards task sampler learning for meta-learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:52.071456Z

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Observation 8e921c97-a634-4d50-ab05-d67fea154a8a · outbound

This paper cites Prototypicalnetworksfor few-shot learning.Advances in neural information processing systems, 30, 2017.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Prototypicalnetworksfor few-shot learning.Advances in neural information processing systems, 30, 2017

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:52.144902Z

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Observation 36fbbe15-9d7b-492b-bd1b-0e674ab24d2d · outbound

This paper cites On First-Order Meta-Learning Algorithms.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis On First-Order Meta-Learning Algorithms

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:52.207540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b63c472-d246-452e-922a-1c9a6dd3ab78 · outbound

This paper cites Vibration-based condition monitoring: Industrial, aerospace and automotive applications.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Vibration-based condition monitoring: Industrial, aerospace and automotive applications

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:52.261160Z

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Observation 435d98b9-cdeb-48ae-b1da-6c2a4e9299d9 · outbound

This paper cites Academic press, 2009.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Academic press, 2009

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:52.343326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4139bc85-462f-4f64-a2a2-e3578f0eaa24 · outbound

This paper cites Bearing fault diagnosis based on multi-scale cnn and lstm network.Measurement, 103:5–12, 2017.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Bearing fault diagnosis based on multi-scale cnn and lstm network.Measurement, 103:5–12, 2017

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:52.401875Z

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Observation bae74de7-3372-4f95-8903-2d635ae3c59f · outbound

This paper cites Noise-robust fault diagnosis method for rotating machin- ery based on time–frequency analysis and convolutional neural networks.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Noise-robust fault diagnosis method for rotating machin- ery based on time–frequency analysis and convolutional neural networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:52.459800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6f86c43-0442-46d0-80f0-1e49c63fe224 · outbound

This paper cites Time-frequency analysis in mechanical fault diagnosis–a review with applications.Mechanical Systems and Signal Processing, 121:209–237, 2018.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Time-frequency analysis in mechanical fault diagnosis–a review with applications.Mechanical Systems and Signal Processing, 121:209–237, 2018

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:52.518779Z

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Observation 6ef370c9-4452-41e8-b754-8196a44b67f3 · outbound

This paper cites Springer, 2006.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Springer, 2006

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:52.570964Z

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Unavailable: canonical work link unavailable.

Observation 60795c95-89fd-43a8-84cf-6b69238b0e24 · outbound

This paper cites AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design Anywhere.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design Anywhere

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:52.631815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7317e77c-a686-482e-a3d4-ad21b91602c2 · outbound

This paper cites Tfpred: Learning discriminative representations from unlabeled data for few-label rotating machinery fault diagnosis.Control Engineering Practice, 146:105900, 2024.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Tfpred: Learning discriminative representations from unlabeled data for few-label rotating machinery fault diagnosis.Control Engineering Practice, 146:105900, 2024

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:52.689887Z

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Observation 5477daac-db2f-49ee-81f8-d084e6e90d77 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis A simple framework for contrastive learning of visual representations

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:52.773088Z

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Observation 08953202-cf62-4c9b-bbc2-dab11f053b12 · outbound

This paper cites Boot- strap your own latent-a new approach to self-supervised learning.Ad- vances in neural information processing systems, 33:21271–21284, 2020.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Boot- strap your own latent-a new approach to self-supervised learning.Ad- vances in neural information processing systems, 33:21271–21284, 2020

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:52.832909Z

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Observation 00279a97-2356-4709-9cfc-8adbb53c7e8f · outbound

This paper cites Barlow twins: Self-supervised learning via redundancy reduction.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Barlow twins: Self-supervised learning via redundancy reduction

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:52.918081Z

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Unavailable: canonical work link unavailable.

Observation 4b9d34ef-f0de-42ca-b2c2-c2ea36f958e9 · outbound

This paper cites Learning to compare: Relation network for few-shot learning.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Learning to compare: Relation network for few-shot learning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:52.978111Z

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Unavailable: canonical work link unavailable.

Observation bf9daec6-89ad-48d2-80a0-e520785aa1fd · outbound

This paper cites FEDformer: Frequency enhanced decomposed transformer for long- term series forecasting.ICML, 2022.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis FEDformer: Frequency enhanced decomposed transformer for long- term series forecasting.ICML, 2022

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:53.022841Z

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Observation 2411a6fa-0e3c-4285-a394-a2f8ca99a791 · outbound

This paper cites Timesnet: Temporal 2d-variation modeling for general time series analysis.ICLR, 2023.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Timesnet: Temporal 2d-variation modeling for general time series analysis.ICLR, 2023

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:53.080838Z

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Observation 8cbb8160-37c6-4437-8616-606d7d89e4b0 · outbound

This paper cites Bearingfaultdiagnosisbase onmulti-scalecnnandlstmmodel.Journal of Intelligent Manufacturing, 32(4):971–987, 2021.

Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis Bearingfaultdiagnosisbase onmulti-scalecnnandlstmmodel.Journal of Intelligent Manufacturing, 32(4):971–987, 2021

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T19:27:53.131940Z

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Pith citing papers

No inbound Pith citation observations are available.