Paper Citation Record · LEDGER

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments

As of 17 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2505.06285.

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

pith.paper-citation-record.v1
2505.06285 v3

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:40:03.688500Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

37 of 37 outbound references displayed

verified exact2
verified fuzzy17
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 83224ea1-b9f9-4672-807e-c6d0c1ead79d · outbound

This paper cites Appli- cationsofmachinelearningtomachinefaultdiagnosis:Areviewand roadmap.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Appli- cationsofmachinelearningtomachinefaultdiagnosis:Areviewand roadmap

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:04.848672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1eb40727-ff54-4ee8-90b0-8b7876a45bdf · outbound

This paper cites Consistency-regularized-label-aware contrastive learning with uncertainty-aware periodic pseudo-labeling for machinery fault diagnosis under limited labeled data.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Consistency-regularized-label-aware contrastive learning with uncertainty-aware periodic pseudo-labeling for machinery fault diagnosis under limited labeled data

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:04.837863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 546c0591-069b-4ac8-af70-dcda81cc6d77 · outbound

This paper cites Physics modeling-driven interpretable data augmentation method for bear- ing fault diagnosis under imbalanced data.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Physics modeling-driven interpretable data augmentation method for bear- ing fault diagnosis under imbalanced data

Reference 3

Resolution: metadata mismatch; raw_fallback, observed 2026-08-15T23:40:04.651785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c7830a29-8829-496c-b44e-3d8662b9595e · outbound

This paper cites A time- frequencyspectralamplitudemodulationmethodanditsapplications in rolling bearing fault diagnosis.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments A time- frequencyspectralamplitudemodulationmethodanditsapplications in rolling bearing fault diagnosis

Reference 4

Resolution: metadata mismatch; raw_fallback, observed 2026-08-15T23:40:04.580737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dee0f680-6186-49e3-bee4-c46b56233f41 · outbound

This paper cites Weak fault detection of rolling bearing using a ds-based adaptive spectrum reconstruction method.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Weak fault detection of rolling bearing using a ds-based adaptive spectrum reconstruction method

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:04.827122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6415f6d8-7c09-41b0-985d-e308897dad0f · outbound

This paper cites Feature extractionbasedonhierarchicalimprovedenvelopespectrumentropy for rolling bearing fault diagnosis.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Feature extractionbasedonhierarchicalimprovedenvelopespectrumentropy for rolling bearing fault diagnosis

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T23:40:03.577358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 543e04bc-7341-46b2-a04f-158dc3ae0d40 · outbound

This paper cites Application of ICEEMDAN energy entropy and AFSA-SVM for fault diagnosis of hoist sheave bearing.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Application of ICEEMDAN energy entropy and AFSA-SVM for fault diagnosis of hoist sheave bearing

Reference 7

Resolution: verified exact; doi, observed 2026-08-15T23:40:03.764568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ef75b742-0167-4869-9622-b7c3e265082a · outbound

This paper cites A novel rolling bearing fault diagnosis method based on continuous hierarchical fractional range entropy.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments A novel rolling bearing fault diagnosis method based on continuous hierarchical fractional range entropy

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T23:40:03.584887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4ee1501-d72c-471c-b0e0-c90e888cbb14 · outbound

This paper cites Meta-learning with distributional similarity preference for few-shot fault diagnosis under varying working conditions.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Meta-learning with distributional similarity preference for few-shot fault diagnosis under varying working conditions

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:04.815891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3941eb6c-011e-4061-bc67-369874b81565 · outbound

This paper cites A meta-learning method for electric machine bearing fault diagnosis undervaryingworkingconditionswithlimiteddata.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments A meta-learning method for electric machine bearing fault diagnosis undervaryingworkingconditionswithlimiteddata

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T23:40:03.595131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1eacccef-8be5-4737-ba55-0ec6c19b7045 · outbound

This paper cites Semi- supervised fault diagnosis of gearbox based on feature pre-extraction mechanismandimprovedgenerativeadversarialnetworksunderlim- ited labeled samples and noise environment.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Semi- supervised fault diagnosis of gearbox based on feature pre-extraction mechanismandimprovedgenerativeadversarialnetworksunderlim- ited labeled samples and noise environment

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T23:40:03.598729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a5e9cf7-ec81-420c-8e3d-b6fa1364854f · outbound

This paper cites Ahybridcross-domainfew- shot bearing fault diagnosis method combining multi-scale feature association and physical information.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Ahybridcross-domainfew- shot bearing fault diagnosis method combining multi-scale feature association and physical information

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:04.804647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3d1aa435-3f27-4515-b4f3-b40710117b58 · outbound

This paper cites Multiscale deep attention q network: A new deep reinforcement learning method for imbalancedfaultdiagnosisingearboxes.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Multiscale deep attention q network: A new deep reinforcement learning method for imbalancedfaultdiagnosisingearboxes

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T23:40:03.605724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c4a042a-080e-4acd-8156-bce4208da39c · outbound

This paper cites an unresolved cited work.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Unresolved cited work

Reference 14

Resolution: metadata mismatch; raw_fallback, observed 2026-08-15T23:40:04.117971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9b674ce5-4b4f-46bc-a7b9-5464beeb9356 · outbound

This paper cites Multiscale residual attention convolutional neural network for bearing fault diagnosis.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Multiscale residual attention convolutional neural network for bearing fault diagnosis

Reference 15

Resolution: metadata mismatch; raw_fallback, observed 2026-08-15T23:40:04.042892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c51acc26-5c97-4b25-a440-e9d94a3bc400 · outbound

This paper cites Remain- ing useful life prediction for the harmonic reducer of industrial robots via in-situ current signal and lightweight multiscale atten- tion deep networks.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Remain- ing useful life prediction for the harmonic reducer of industrial robots via in-situ current signal and lightweight multiscale atten- tion deep networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:04.793918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e16573c5-8782-459b-a951-535a64ab3dd0 · outbound

This paper cites Variational Attention-Based Interpretable Transformer Network for Rotary Ma- chine Fault Diagnosis.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Variational Attention-Based Interpretable Transformer Network for Rotary Ma- chine Fault Diagnosis

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:04.781946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 307be121-935b-4008-8fc8-619916cc97da · outbound

This paper cites A novel time–frequency Transformer based on self–attention mechanism and its application infaultdiagnosisofrollingbearings.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments A novel time–frequency Transformer based on self–attention mechanism and its application infaultdiagnosisofrollingbearings

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:04.771032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bb7a80de-7969-4835-87f5-447795eefaca · outbound

This paper cites Wd-kantf: An interpretable intelligent fault diagnosis framework for rotating machinery under noise environments and small sample conditions.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Wd-kantf: An interpretable intelligent fault diagnosis framework for rotating machinery under noise environments and small sample conditions

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:04.760095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7d8ef23f-a759-47f2-87f2-3b96613e9a0d · outbound

This paper cites Convformer- NSE: A novel end-to-end gearbox fault diagnosis framework under heavy noise using joint global and local information.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Convformer- NSE: A novel end-to-end gearbox fault diagnosis framework under heavy noise using joint global and local information

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T23:40:03.633984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6a1a248-bc35-438e-9c1f-896f9dbba042 · outbound

This paper cites YOLOv11: An Overview of the Key Architectural Enhancements.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments YOLOv11: An Overview of the Key Architectural Enhancements

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T23:40:03.637996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc29d885-8b34-4084-a765-d62c5d5de8d9 · outbound

This paper cites Inception-v4, inception-resnet and the impact of residual connections on learning.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Inception-v4, inception-resnet and the impact of residual connections on learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:04.749084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fc1c0809-f1f0-47aa-a05e-43abc009ca34 · outbound

This paper cites DRSwin- ST: An intelligent fault diagnosis framework based on dynamic threshold noise reduction and sparse transformer with shifted win- dows 250, 110327.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments DRSwin- ST: An intelligent fault diagnosis framework based on dynamic threshold noise reduction and sparse transformer with shifted win- dows 250, 110327

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:04.738777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7ee503e5-9fb5-4f47-94a8-637b8ec20769 · outbound

This paper cites Longformer: The long- document transformer.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Longformer: The long- document transformer

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:04.728178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c7133979-d48a-4763-bbda-8f593ca826b9 · outbound

This paper cites Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T23:40:03.652510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5a96805-76b9-4987-ac06-3ac95e8c1953 · outbound

This paper cites One wide feedforward is all you need, in: Proceedings of the Eighth Conference on Machine Translation, Association for Computational Linguistics.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments One wide feedforward is all you need, in: Proceedings of the Eighth Conference on Machine Translation, Association for Computational Linguistics

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:04.717045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 579568cc-9a33-4685-9421-5bbe4c7b5b1e · outbound

This paper cites FNet: Mixing Tokens with Fourier Transforms.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments FNet: Mixing Tokens with Fourier Transforms

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T23:40:03.659808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 108e4cd2-c1e1-49ef-9173-b994c2594147 · outbound

This paper cites An Attention Free Transformer.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments An Attention Free Transformer

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T23:40:03.663756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2df1e844-d58c-4af6-9db0-ba8e84733df6 · outbound

This paper cites an unresolved cited work.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:40:04.706604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0c812ec7-68e6-4ddc-b8db-d67f17a0cff8 · outbound

This paper cites Bearing fault diagnosis base on multi-scale cnn and lstm model.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Bearing fault diagnosis base on multi-scale cnn and lstm model

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:04.695365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dcfe0afc-87af-432e-8145-1b5abce728e0 · outbound

This paper cites Understanding and learning discriminant features based on multiattention 1dcnn for wheelset bearing fault diagnosis.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Understanding and learning discriminant features based on multiattention 1dcnn for wheelset bearing fault diagnosis

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:04.684583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d71bcae5-5723-4870-926c-0b0fb50c1e7a · outbound

This paper cites Deeplearningalgorithmsforrotatingmachineryintelligentdiagnosis: An open source benchmark study.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Deeplearningalgorithmsforrotatingmachineryintelligentdiagnosis: An open source benchmark study

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:04.674003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b3cff2a6-bb0b-4c54-ad6b-3b6277b8dbca · outbound

This paper cites LiConvFormer: A lightweight fault diagnosis framework using separable multiscale convolutionandbroadcastself-attention.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments LiConvFormer: A lightweight fault diagnosis framework using separable multiscale convolutionandbroadcastself-attention

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T23:40:03.684597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3661b6b3-ae98-466a-bda1-b6591b17b36a · outbound

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

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Highly accurate machine fault diagnosis using deep transfer learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:40:04.662735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 34292558-e4a1-41cc-b02e-06ded20c81f7 · outbound

This paper cites an unresolved cited work.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Unresolved cited work

Reference 336

Resolution: verified exact; doi, observed 2026-08-15T23:40:03.751763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cd51f7fd-4cfc-4d7b-b301-ddbb2be7cbcb · outbound

This paper cites an unresolved cited work.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Unresolved cited work

Reference 2756

Resolution: unresolved; no resolver link, observed 2026-08-15T23:40:03.591912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab4f50cf-f82f-49f6-bda9-9b049072a6d4 · outbound

This paper cites an unresolved cited work.

FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments Unresolved cited work

Reference 5745

Resolution: unresolved; no resolver link, observed 2026-08-15T23:40:03.677755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.