Paper Citation Record · LEDGER

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods

As of 12 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2607.19455.

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2607.19455 v2

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Outbound references

Observation be143ff4-760d-4f32-9cc6-5e8c6bcac35b · outbound

This paper cites Synthesizing rolling bearing fault samples in new conditions: A framework based on a modified CGAN.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Synthesizing rolling bearing fault samples in new conditions: A framework based on a modified CGAN

Reference 1

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Observation f8ace7ed-ba73-4fd6-bf2a-4b435726b05a · outbound

This paper cites What regularized auto-encoders learn from the data-generating distribution.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods What regularized auto-encoders learn from the data-generating distribution

Reference 2

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Observation 09e42e86-ab2b-4177-862c-30a4b5582655 · outbound

This paper cites Generalized Denoising Auto-Encoders as Generative Models.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Generalized Denoising Auto-Encoders as Generative Models

Reference 3

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Observation 37b848cf-39c7-4980-a3e7-92f3008d7d2a · outbound

This paper cites Basic vibration theory.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Basic vibration theory

Reference 4

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Observation 21572bb4-1cbc-4c68-8f16-f2ea7f6c02f7 · outbound

This paper cites Bearing data center.https://engineering.case.edu/bearingdatacent er.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Bearing data center.https://engineering.case.edu/bearingdatacent er

Reference 5

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Observation dbcc6664-0d55-4ca5-9e99-9a401b9e3aef · outbound

This paper cites A new safe-level enabled borderline-smote for condition recognition of imbalanced dataset.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods A new safe-level enabled borderline-smote for condition recognition of imbalanced dataset

Reference 6

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Observation cd0c1240-c00e-4bcf-8c0b-cc8b3afb8081 · outbound

This paper cites Abearingfaultdiagnosismethodinscenariosofimbalancedsamples and insufficient labeled samples.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Abearingfaultdiagnosismethodinscenariosofimbalancedsamples and insufficient labeled samples

Reference 7

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Observation 9c8df624-1f14-451d-8446-6386fbd7ea57 · outbound

This paper cites Generative alignment of posterior probabilities for source-free domain adaptation.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Generative alignment of posterior probabilities for source-free domain adaptation

Reference 8

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This paper cites Instance-based Counterfactual Explanations for Time Series Classification.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Instance-based Counterfactual Explanations for Time Series Classification

Reference 9

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Observation d02fedf1-f53b-4ce7-aff5-f23acf2ef3a8 · outbound

This paper cites Data Sets and Download — Bearing Data Center.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Data Sets and Download — Bearing Data Center

Reference 10

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Observation 1d1be91a-c61f-4cf4-b966-6f6905f7ceb0 · outbound

This paper cites Dropoutasabayesianapproximation: Representing model uncertainty in deep learning, in: Proceedings of the 33rd International Conference on Machine Learning, pp.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Dropoutasabayesianapproximation: Representing model uncertainty in deep learning, in: Proceedings of the 33rd International Conference on Machine Learning, pp

Reference 11

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Observation 96e5e525-5029-481a-94e0-03dbf7f34454 · outbound

This paper cites Rudin–osher–fatemi total variation denoising using split bregman.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Rudin–osher–fatemi total variation denoising using split bregman

Reference 12

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Observation 83c96157-46ce-4dbd-aaa5-bc38a4055eba · outbound

This paper cites Regularisation of Neural Networks by Enforcing Lipschitz Continuity.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Regularisation of Neural Networks by Enforcing Lipschitz Continuity

Reference 13

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Observation 717a4209-3ad7-4eeb-89e2-cf7918dfed34 · outbound

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Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Unresolved cited work

Reference 14

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Observation 4aeaee7a-0434-4d45-9038-1c0818630a71 · outbound

This paper cites On calibration of modern neural networks, in: Proceedings of the 34th International Conference on Machine Learning.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods On calibration of modern neural networks, in: Proceedings of the 34th International Conference on Machine Learning

Reference 15

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This paper cites Adaptive sv-borderline smote-svmalgorithmforimbalanceddataclassification.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Adaptive sv-borderline smote-svmalgorithmforimbalanceddataclassification

Reference 16

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Observation 3e83ed71-f499-40f7-af9f-af9f6714704f · outbound

This paper cites Evaluating deep learning models for fault diagnosis of a rotating machinery with epistemic and aleatoric uncertainty.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Evaluating deep learning models for fault diagnosis of a rotating machinery with epistemic and aleatoric uncertainty

Reference 17

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Observation fd785a43-d7be-41e5-9906-e59f6e40dcbb · outbound

This paper cites Obgan:Minorityoversamplingnearborderline with generative adversarial networks.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Obgan:Minorityoversamplingnearborderline with generative adversarial networks

Reference 18

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Observation b77a8e92-b7a3-41c6-a875-26663b2842c4 · outbound

This paper cites The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification

Reference 19

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Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Unresolved cited work

Reference 20

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Observation 980c6aef-1bdc-4aa3-8d8b-28f472a87689 · outbound

This paper cites Representations aligned counterfactual domain learning for open-set fault diagnosis under speed transient conditions.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Representations aligned counterfactual domain learning for open-set fault diagnosis under speed transient conditions

Reference 21

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Observation f7e64654-a018-4eef-9e6e-ba8b61d6c4d7 · outbound

This paper cites Counterfactual-augmented few-shotcontrastivelearningformachineryintelligentfaultdiagnosis with limited samples.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Counterfactual-augmented few-shotcontrastivelearningformachineryintelligentfaultdiagnosis with limited samples

Reference 22

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Observation dd32fbf1-6a83-488b-b76d-99880341e2bc · outbound

This paper cites Fault diagnosis based on counterfactual inference for the batch fermentation process.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Fault diagnosis based on counterfactual inference for the batch fermentation process

Reference 23

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Observation 21783d57-01ca-4cff-a075-b5d95385a9fb · outbound

This paper cites A case study of conditional deep convolutional generative adversarial networks in machine fault diagnosis.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods A case study of conditional deep convolutional generative adversarial networks in machine fault diagnosis

Reference 24

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Observation d04c0932-5179-42c6-91fd-0dd5a64e6a4d · outbound

This paper cites Imbalanced fault diagnosis of rolling bearing based on generative adversarial network: A com- parative study.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Imbalanced fault diagnosis of rolling bearing based on generative adversarial network: A com- parative study

Reference 25

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Observation 113ead32-22ac-41ed-a118-c62180622a42 · outbound

This paper cites Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review

Reference 26

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This paper cites Plug & play generative networks: Conditional iterative generation of images in latent space.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Plug & play generative networks: Conditional iterative generation of images in latent space

Reference 27

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This paper cites A novel class imbalance-robust network for bearing fault diagnosis utilizing raw vibration signals.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods A novel class imbalance-robust network for bearing fault diagnosis utilizing raw vibration signals

Reference 28

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This paper cites Improvement of Generative Adversarial Network and its application in bearing fault diagnosis: A review.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Improvement of Generative Adversarial Network and its application in bearing fault diagnosis: A review

Reference 29

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Observation 5c4f1a69-0c91-41e0-8037-edda9826a7a1 · outbound

This paper cites Improved Techniques for Training GANs.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Improved Techniques for Training GANs

Reference 30

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Observation 2fffa2ce-97bb-413e-b668-7c3e9c94c62e · outbound

This paper cites A novel deep autoen- coder feature learning method for rotating machinery fault diagnosis.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods A novel deep autoen- coder feature learning method for rotating machinery fault diagnosis

Reference 31

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This paper cites A novel intelligent fault diagnosis method for rolling bearings based on wasserstein generative adversarial network and convolutional neural network under unbalanced dataset.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods A novel intelligent fault diagnosis method for rolling bearings based on wasserstein generative adversarial network and convolutional neural network under unbalanced dataset

Reference 32

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This paper cites Counterfactual expla- nations without opening the black box: Automated decisions and the gdpr.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Counterfactual expla- nations without opening the black box: Automated decisions and the gdpr

Reference 33

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This paper cites Coun- terfactual data generation method for fault diagnosis of complex electromechanical systems.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Coun- terfactual data generation method for fault diagnosis of complex electromechanical systems

Reference 34

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Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Unresolved cited work

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This paper cites Anewconvolutionalneural network-baseddata-drivenfaultdiagnosismethod.IEEETransactions on Industrial Electronics 65, 5990–5998.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Anewconvolutionalneural network-baseddata-drivenfaultdiagnosismethod.IEEETransactions on Industrial Electronics 65, 5990–5998

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This paper cites Counterfactual inference for generalized zero-shot compound-fault diagnosis.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Counterfactual inference for generalized zero-shot compound-fault diagnosis

Reference 37

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This paper cites Deep learning algorithms for bearing fault diagnostics—a comprehensive review.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Deep learning algorithms for bearing fault diagnostics—a comprehensive review

Reference 38

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This paper cites Improved Training of Wasserstein GANs.

Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Improved Training of Wasserstein GANs

Reference 2017

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Generating Bearing Vibration Signals at User-Specified Fault Probabilities Using PR-GAN and Counterfactual Methods Machine Learning

Reference 2024

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