Paper Citation Record · LEDGER

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation

As of 14 August 2026, this Paper Citation Record lists 76 of 76 outbound references and 2 inbound Pith citation observations for arXiv:2507.16696.

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

pith.paper-citation-record.v1
2507.16696 v3

Coverage vector

measured 76 of 76 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:15:09.617246Z

measured 78 of 78 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T21:56:42.758960Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

76 of 76 outbound references displayed

verified exact3
verified fuzzy56
unresolved17
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 91691348-2c47-4f42-96d9-e3580e133101 · outbound

This paper cites Qwen2.5-Omni Technical Report.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Qwen2.5-Omni Technical Report

Reference 1

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Source-reported events for the cited work

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Observation d4418689-ab8b-46ba-9bd9-ad33caa320da · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation An image is worth 16x16 words: Trans- formers for image recognition at scale,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T15:15:09.396309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 140dcc88-d828-4125-818e-05d2c96cbe47 · outbound

This paper cites Masked au- toencoders are scalable vision learners,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Masked au- toencoders are scalable vision learners,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T15:15:09.399268Z

Source-reported events for the cited work

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Observation 6993ff52-89bb-4fb0-9335-19a99dcf861f · outbound

This paper cites DINOv3.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation DINOv3

Reference 4

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Source-reported events for the cited work

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Observation 25617046-bd7e-473d-b29a-b86d3aecbb9e · outbound

This paper cites wav2vec 2.0: A framework for self-supervised learning of speech representations,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation wav2vec 2.0: A framework for self-supervised learning of speech representations,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.479462Z

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Observation 40f00180-aead-48fd-854c-ccc09340c07c · outbound

This paper cites Robust speech recognition via large-scale weak supervi- sion,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Robust speech recognition via large-scale weak supervi- sion,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T15:15:09.409615Z

Source-reported events for the cited work

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Observation 907834f9-0086-4aac-b2e9-5129425b3de1 · outbound

This paper cites Masked autoencoders that listen,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Masked autoencoders that listen,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T15:15:09.413301Z

Source-reported events for the cited work

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Observation e54ff84f-8c34-4094-b9b4-b2329cfff44b · outbound

This paper cites BEATs: Audio pre-training with acoustic tokenizers,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation BEATs: Audio pre-training with acoustic tokenizers,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.461537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1d0231fb-0387-4ef4-86c8-7eb1d9eeec8f · outbound

This paper cites Eat: self-supervised pre-training with efficient audio transformer,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Eat: self-supervised pre-training with efficient audio transformer,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T15:15:09.419210Z

Source-reported events for the cited work

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Observation 4083fc14-e743-4293-82f0-aa0c9c5eefeb · outbound

This paper cites Unsupervised anomaly detection and localization of machine audio: A gan-based approach,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Unsupervised anomaly detection and localization of machine audio: A gan-based approach,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.452322Z

Source-reported events for the cited work

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Observation ea20b91a-795a-4939-a5c7-8e65f57ced50 · outbound

This paper cites Anopatch: Towards better consistency in machine anomalous sound detection,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Anopatch: Towards better consistency in machine anomalous sound detection,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.443299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7e99b951-cea6-417b-8f85-c13f6c5ca453 · outbound

This paper cites Self-supervised learning for anomalous sound detec- tion,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Self-supervised learning for anomalous sound detec- tion,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.433961Z

Source-reported events for the cited work

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Observation a951cdfe-ed00-4dc7-b5ae-58883cfc05e4 · outbound

This paper cites Exploring self-supervised audio models for generalized anomalous sound detection,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Exploring self-supervised audio models for generalized anomalous sound detection,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T15:15:09.430410Z

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Observation b386007d-e16c-4db5-8496-81f015901979 · outbound

This paper cites Auto-embedding transformer for inter- pretable few-shot fault diagnosis of rolling bearings,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Auto-embedding transformer for inter- pretable few-shot fault diagnosis of rolling bearings,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.419161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 69ea7681-5eb1-456b-a8d1-8ca5135a6dbb · outbound

This paper cites A rolling bearing fault diagnosis method based on multimodal knowledge graph,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation A rolling bearing fault diagnosis method based on multimodal knowledge graph,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.409714Z

Source-reported events for the cited work

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Observation 2b435808-72ca-4c7e-9aa0-46b63db880c6 · outbound

This paper cites Bearllm: A prior knowledge-enhanced bearing health management framework with uni- fied vibration signal representation,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Bearllm: A prior knowledge-enhanced bearing health management framework with uni- fied vibration signal representation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.400967Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 671fbaa0-7591-49a0-bf70-6cd8836e1943 · outbound

This paper cites Cows: Self-supervised representation pre-training for cross-machine fault diagnosis,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Cows: Self-supervised representation pre-training for cross-machine fault diagnosis,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.392806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 131b5719-bed2-4ec7-b4fd-057c1e429147 · outbound

This paper cites Gearbox fault diagnosis using a deep learning model with limited data sample,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Gearbox fault diagnosis using a deep learning model with limited data sample,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.383699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4559777a-0bf6-42f6-99dc-95038bfb371b · outbound

This paper cites A review on deep learning in planetary gearbox health state recognition: methods, applications, and dataset publication,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation A review on deep learning in planetary gearbox health state recognition: methods, applications, and dataset publication,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.375018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f3a9d7cd-a5c1-4725-a972-2f2c165f5578 · outbound

This paper cites A comprehensive gear eccentricity dataset with multiple fault severity levels: Description, characteristics analysis, and fault diagnosis applications,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation A comprehensive gear eccentricity dataset with multiple fault severity levels: Description, characteristics analysis, and fault diagnosis applications,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.366608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation be3da8f8-671b-44ae-9638-4f901b3bbb9d · outbound

This paper cites Bearingfm: Towards a foundation model for bearing fault diagnosis by domain knowledge and contrastive learning,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Bearingfm: Towards a foundation model for bearing fault diagnosis by domain knowledge and contrastive learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.356614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b0566493-46f6-4ed3-bfa3-c74bde59250b · outbound

This paper cites Rmgpt: A foundation model with generative pre-trained transformer for fault diagnosis and prognosis in rotating machinery,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Rmgpt: A foundation model with generative pre-trained transformer for fault diagnosis and prognosis in rotating machinery,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.348375Z

Source-reported events for the cited work

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Observation 1cd09e22-e77d-4ccf-a07d-26cd99df9514 · outbound

This paper cites Hse: A plug-and- play module for unified fault diagnosis foundation models,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Hse: A plug-and- play module for unified fault diagnosis foundation models,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.339779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b541f2e6-1374-4f42-91d8-2773264cc762 · outbound

This paper cites Scaling up masked audio encoder learning for general audio classification,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Scaling up masked audio encoder learning for general audio classification,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.331078Z

Source-reported events for the cited work

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Observation 6738df00-1239-43d6-bb90-48b609a1bcb4 · outbound

This paper cites Ced: Consistent ensemble distillation for audio tagging,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Ced: Consistent ensemble distillation for audio tagging,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.322538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f9449dcd-5f61-4ff6-8b8a-a181359e2dfb · outbound

This paper cites Openbeats: A fully open-source general- purpose audio encoder,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Openbeats: A fully open-source general- purpose audio encoder,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.313491Z

Source-reported events for the cited work

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Observation dfdd5fa9-242f-4042-a0be-ade6705cfc4a · outbound

This paper cites Qwen2-Audio Technical Report.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Qwen2-Audio Technical Report

Reference 27

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Observation d80cd17a-6f6c-4af5-83a0-a462c3edb589 · outbound

This paper cites Audio flamingo 3: Advancing audio intelligence with fully open large audio language models,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Audio flamingo 3: Advancing audio intelligence with fully open large audio language models,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.305072Z

Source-reported events for the cited work

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Observation 8ee8850a-4f4c-4df2-a642-f1078f922c29 · outbound

This paper cites Midashenglm: Efficient audio understanding with general audio captions,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Midashenglm: Efficient audio understanding with general audio captions,

Reference 29

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Observation 4109c05f-6e83-4cbf-a09c-69e89642dc3c · outbound

This paper cites Time-moe: Billion-scale time series foundation models with mixture of experts,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Time-moe: Billion-scale time series foundation models with mixture of experts,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.297134Z

Source-reported events for the cited work

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Observation ae4008f3-a8ca-40e2-befb-9fff0dbabe0c · outbound

This paper cites Sundial: A family of highly capable time series foundation models,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Sundial: A family of highly capable time series foundation models,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.288656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8634e06e-a8ad-4f9e-af25-f64618f37cdb · outbound

This paper cites Bearing fault diagnosis based on an enhanced image representation method of vibration signal and conditional super token transformer,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Bearing fault diagnosis based on an enhanced image representation method of vibration signal and conditional super token transformer,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.279691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3eb7eab9-99ac-4327-b07a-24e9f9a2e80f · outbound

This paper cites Parinfogpt: An llm-based two- stage framework for reliability assessment of rotating machine under partial information,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Parinfogpt: An llm-based two- stage framework for reliability assessment of rotating machine under partial information,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.272027Z

Source-reported events for the cited work

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Observation 1e6b54e8-e675-4f0d-9fab-c59d7d6bf15a · outbound

This paper cites Data-efficient motor condition monitoring with time series foundation models,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Data-efficient motor condition monitoring with time series foundation models,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.262704Z

Source-reported events for the cited work

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Observation e72ac779-e2a5-466e-af3f-0f8c8fbfeb1a · outbound

This paper cites Adaptive prototype learning for anomalous sound detection with partially known attributes,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Adaptive prototype learning for anomalous sound detection with partially known attributes,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.254377Z

Source-reported events for the cited work

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Observation c28fd7f5-51f9-442e-bc5a-9449810f1210 · outbound

This paper cites ToyAD- MOS: A dataset of miniature-machine operating sounds for anomalous sound detection,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation ToyAD- MOS: A dataset of miniature-machine operating sounds for anomalous sound detection,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.246280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f4c5f718-5720-45d9-8156-884e84bd6d6f · outbound

This paper cites MIMII Dataset: Sound dataset for malfunctioning industrial machine investigation and inspection,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation MIMII Dataset: Sound dataset for malfunctioning industrial machine investigation and inspection,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.238047Z

Source-reported events for the cited work

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Observation df208435-52ed-43d5-872a-c8962efbcbfa · outbound

This paper cites Description and discussion on DCASE2020 challenge task2: Unsuper- vised anomalous sound detection for machine condition monitoring,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Description and discussion on DCASE2020 challenge task2: Unsuper- vised anomalous sound detection for machine condition monitoring,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.230250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 10854ce5-bd6b-416a-a308-a11e63194c73 · outbound

This paper cites MIMII DUE: Sound dataset for malfunctioning industrial machine investigation and inspection with domain shifts due to changes in operational and environmental conditions,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation MIMII DUE: Sound dataset for malfunctioning industrial machine investigation and inspection with domain shifts due to changes in operational and environmental conditions,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.221229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6eb31f2c-375a-4308-bcac-05602dcda7ba · outbound

This paper cites Description and discussion on dcase 2021 challenge task 2: Unsupervised anomalous detection for machine condition monitoring under domain shifted conditions,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Description and discussion on dcase 2021 challenge task 2: Unsupervised anomalous detection for machine condition monitoring under domain shifted conditions,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.213412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation af10399e-a3c2-419e-bc79-641b576519a4 · outbound

This paper cites ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.205344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dce5a06f-f4ca-40b3-8b08-968f8396fa69 · outbound

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FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Unresolved cited work

Reference 42

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c977fdeb-ddf7-4512-bac0-521fe041e583 · outbound

This paper cites Description and discussion on DCASE 2022 challenge task 2: Unsu- pervised anomalous sound detection for machine condition monitoring applying domain generalization techniques,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Description and discussion on DCASE 2022 challenge task 2: Unsu- pervised anomalous sound detection for machine condition monitoring applying domain generalization techniques,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.188900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8275bd2f-fe00-4084-b3b8-1ae97140b178 · outbound

This paper cites Description and Discussion on DCASE 2023 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Description and Discussion on DCASE 2023 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T15:15:09.522908Z

Source-reported events for the cited work

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Observation 0774a6dd-788b-4757-bcff-ed9513f2d5e8 · outbound

This paper cites First-shot anomaly sound detection for machine condition monitoring: A domain generalization baseline.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation First-shot anomaly sound detection for machine condition monitoring: A domain generalization baseline

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:15:09.858862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 989939a8-1d6e-418a-a4fa-d79d04f7799f · outbound

This paper cites Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T15:15:09.530285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3433daa8-62b4-4791-8bc9-d8ad192bae36 · outbound

This paper cites First-shot anomaly detection for machine condition monitoring: A domain generalization baseline,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation First-shot anomaly detection for machine condition monitoring: A domain generalization baseline,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.180405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 412566ac-c89b-4fed-899e-13641390782b · outbound

This paper cites Description and discussion on DCASE 2025 challenge task 2: First-shot unsupervised anomalous sound detection for machine condition monitoring,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Description and discussion on DCASE 2025 challenge task 2: First-shot unsupervised anomalous sound detection for machine condition monitoring,

Reference 48

Resolution: verified exact; raw_fallback, observed 2026-08-06T15:15:09.837196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0eefa422-5f99-4f36-b01b-18fa485e2bf0 · outbound

This paper cites Compressed air leakage detection using acoustic emissions with neural networks,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Compressed air leakage detection using acoustic emissions with neural networks,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.171654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7522c274-ec6a-434c-96e1-eda0d3e582f9 · outbound

This paper cites Sounding industry: Challenges and datasets for industrial sound analysis,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Sounding industry: Challenges and datasets for industrial sound analysis,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.163700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4baab3c7-344c-4b50-a868-bf82b22cbe94 · outbound

This paper cites Mafaulda-machinery fault database,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Mafaulda-machinery fault database,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.154949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c0745e52-7261-408d-b17d-c8810761763b · outbound

This paper cites Attention guided multi-wavelet adversarial network for cross domain fault diagnosis,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Attention guided multi-wavelet adversarial network for cross domain fault diagnosis,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.145151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e5ecda3a-fd87-46f4-b426-203bbbf835e4 · outbound

This paper cites Integrated decision-making with adaptive feature weighting adversarial network for multi-target domain compound fault diagnosis of machinery,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Integrated decision-making with adaptive feature weighting adversarial network for multi-target domain compound fault diagnosis of machinery,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.135955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fb7f3881-4027-4cca-86d7-3a5ede1cfb29 · outbound

This paper cites A novel domain adaptive fault diagnosis method for bearings based on unbalance data generation,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation A novel domain adaptive fault diagnosis method for bearings based on unbalance data generation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.126817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4d904834-cbc2-43a0-9c3d-408579f02990 · outbound

This paper cites A novel rolling bearing fault diagnosis method based on generalized nonlinear spectral sparsity,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation A novel rolling bearing fault diagnosis method based on generalized nonlinear spectral sparsity,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.118715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 459daf0b-c076-4bbd-a3b0-9191beba0ea5 · outbound

This paper cites Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.109411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5b1d5f1c-c215-4990-a551-ab0e4a4b43a6 · outbound

This paper cites Intelligent Fault Diagnosis of Type and Severity in Low-Frequency, Low Bit-Depth Signals.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Intelligent Fault Diagnosis of Type and Severity in Low-Frequency, Low Bit-Depth Signals

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:15:09.656975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 08cf650f-8f47-46fd-a3fb-b67724b8d71b · outbound

This paper cites Audio set: An ontology and human- labeled dataset for audio events,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Audio set: An ontology and human- labeled dataset for audio events,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T15:15:09.566705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82910759-8c3d-4307-9a9d-c9d2d712a3c7 · outbound

This paper cites The mtg-jamendo dataset for automatic music tagging,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation The mtg-jamendo dataset for automatic music tagging,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.094773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c69ed168-2c82-44f9-be4e-7edaffe1e1dd · outbound

This paper cites Music4all: A new music database and its applications,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Music4all: A new music database and its applications,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.085137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 834881fb-3a81-45fe-98fa-00f0f38a6dd5 · outbound

This paper cites Efficient training of audio transformers with patchout,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Efficient training of audio transformers with patchout,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.076784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dcc374bd-9e27-4b0f-983e-09079667aeee · outbound

This paper cites Mert: Acoustic music understanding model with large-scale self-supervised training,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Mert: Acoustic music understanding model with large-scale self-supervised training,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.068142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cf18d254-fe31-47ac-82ad-d3b0af6f6743 · outbound

This paper cites Muq: Self-supervised music representation learning with mel residual vector quantization,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Muq: Self-supervised music representation learning with mel residual vector quantization,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T15:15:09.579835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7f927c6-7542-4bf5-b951-0f81fc9ba9aa · outbound

This paper cites Ensemble empirical mode decomposition: a noise-assisted data analysis method,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Ensemble empirical mode decomposition: a noise-assisted data analysis method,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.054871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dd2a5c72-2ef4-4205-95b9-9e85532e319f · outbound

This paper cites Permutation entropy: a natural complexity measure for time series,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Permutation entropy: a natural complexity measure for time series,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.046736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 506fbf7b-16e7-46ad-9fd7-012a36b02f19 · outbound

This paper cites Tfpred: Learning discriminative representations from unlabeled data for few-label rotating machinery fault diagnosis,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Tfpred: Learning discriminative representations from unlabeled data for few-label rotating machinery fault diagnosis,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.037234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9a5b8b59-f56a-4300-9a65-eb2099a76d5e · outbound

This paper cites Liconvformer: A lightweight fault diagnosis framework using separable multiscale con- volution and broadcast self-attention,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Liconvformer: A lightweight fault diagnosis framework using separable multiscale con- volution and broadcast self-attention,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.028926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a6e28d8c-a3b2-42af-9836-b1d7b7cccbd0 · outbound

This paper cites A unified rotating machinery health management framework leveraging large language models for diverse components, conditions, and tasks,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation A unified rotating machinery health management framework leveraging large language models for diverse components, conditions, and tasks,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.020714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c6d15fc0-2695-4db3-b405-17c198f85db9 · outbound

This paper cites Echo: Frequency-aware hierarchical encoding for variable-length signals,.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Echo: Frequency-aware hierarchical encoding for variable-length signals,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.011510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f9a6de2b-a662-4eb8-8b9f-a3605f70d7f7 · outbound

This paper cites SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-06T15:15:09.599545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcd29ec2-e1a8-4c90-a82f-6a49686d7af8 · outbound

This paper cites For Whisper, we evaluate the encoders of five official pre-trained checkpoints.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation For Whisper, we evaluate the encoders of five official pre-trained checkpoints

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:10.002793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 11acab8f-be58-4935-9aae-36348a9049f2 · outbound

This paper cites For Open- BEATs, we employ the base and large checkpoints of iter3.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation For Open- BEATs, we employ the base and large checkpoints of iter3

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:09.992917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 44e51d18-a80a-4e3c-b301-79fc46dd7c8b · outbound

This paper cites an unresolved cited work.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Unresolved cited work

Reference 73

Resolution: unresolved; raw_fallback, observed 2026-08-06T15:15:09.984297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a1c47d8b-5bfb-43af-bc12-ad29049e2870 · outbound

This paper cites Thus, only two models are compared.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation Thus, only two models are compared

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:09.975327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0053abee-d9f0-4248-9b38-a4bf17b31dec · outbound

This paper cites For LiConvFormer, we utilize the weights trained on CWRU.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation For LiConvFormer, we utilize the weights trained on CWRU

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:09.965758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 24c0f89c-f5cf-42b4-9b4d-9add1cfff428 · outbound

This paper cites degree with the Department of Electronic Engi- neering, Tsinghua University, Beijing, China.

FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation degree with the Department of Electronic Engi- neering, Tsinghua University, Beijing, China

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:15:09.957549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation 0856c63e-18f2-4a24-82a4-d1d8ebbec88e · inbound

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection cites this paper.

Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-02T21:56:42.758960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49065796-d797-43be-a5fd-368bff46985d · inbound

ECHOv2: Two-Level Band-Splitting Representation Learning for Anomalous Sound Detection cites this paper.

ECHOv2: Two-Level Band-Splitting Representation Learning for Anomalous Sound Detection FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation

Reference 44

Resolution: unresolved; no resolver link, observed 2026-07-14T10:35:50.643862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.