QSCGAN: An Un-Supervised Quick Self-Attention Convolutional GAN for LRE Bearing Fault Diagnosis Under Limited Label-Lacked Data

Wan, Wenqing; He, Shuilong; Chen, Jinglong; Li, Aimin; Feng, Yong

doi:10.1109/tim.2021.3125973

Cited by 38 publications

(11 citation statements)

References 27 publications

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“…Lack of diversity in the training data, leads to homogeneous samples generated by GANs [19]. Note that generating training data in the industrial settings (welding beads [17], bearings status in rocket engine [18], tyre defects [19], milling tool wear and tear [20]), although limited in amount, is still an expensive and a tedious effort. In this work, our novel augmentation scheme transforms simulated data into diverse larger dataset, which shows good performance even on experimental signals.…”

Section: Related Workmentioning

confidence: 99%

“…Artificial neural networks (ANNs) are being increasingly used for instrumentation and measurement applications, such as localization of welding beads [17], bearing fault detection in a rocket engine [18], tyre- [19] and milling tool- [20] defect detection etc. Focusing on SMI, such ANNs have also been previously employed to process SMI signals, such as for noise removal [21], [22], fringe classification [23], modality detection [24] and displacement measurement [25].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Augmentation-Assisted Robust Fringe Detection on Unseen Experimental Signals Applied to Optical Feedback Interferometry Using a Deep Network

Khurshid

Hussain

Zabit

et al. 2023

IEEE Trans. Instrum. Meas.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Augmentation-Assisted Robust Fringe Detection on Unseen Experimental Signals Applied to Optical Feedback Interferometry Using a Deep Network

Khurshid

Hussain

Zabit

et al. 2023

IEEE Trans. Instrum. Meas.

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“…Permission from IEEE must be obtained for all other users, including reprinting republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. in rocket engine [18], tyre defects [19], milling tool wear and tear [20]), although limited in amount, is still an expensive and a tedious effort. In this work, our novel augmentation scheme transforms simulated data into diverse larger dataset, which shows good performance even on experimental signals.…”

Section: Related Workmentioning

confidence: 99%

Augmentation assisted robust fringe detection on unseen experimental signals applied to optical feedback interferometry using a deep network

Khurshid¹,

Hussain²,

Zabit³

et al. 2023

Preprint

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<p>In this work, we propose using deep neural 2D networks, which have renowned generalization performance on unseen data. It specifically explains the use of Novel Augmentation technique for SMI Fringe detection and classification. Overall, it highlights the importance of data augmentation in computer vision algorithms. The results prove that computer vision can be used to cater the problems in signal processing and photoelectronics domain.</p>

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“…Upsampling is one of the mainstream methods, which creates new samples by randomly selecting from scarce data [16]. Generative Adversarial Network is also a feasible method, which learns the characteristics of the raw data and then creates new samples by reinforcement learning [17].…”

Section: Data-driven Modelling Approaches With Limited Datamentioning

confidence: 99%

Privacy‐preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis

Xia

Zheng

et al. 2022

IET Collab Intel Manufact

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Data-driven fault diagnosis approaches have been widely adopted due to their persuasive performance. However, data are always insufficient to develop effective fault diagnosis models in real manufacturing scenarios. Despite numerous approaches that have been offered to mitigate the negative effects of insufficient data, the most challenging issue lies in how to break down the data silos to enlarge data volume while preserving data privacy. To address this issue, a vertical federated learning (FL) model, privacy-preserving boosting tree, has been developed for collaborative fault diagnosis of industrial practitioners while maintaining anonymity. Only the model information will be shared under the homomorphic encryption protocol, safeguarding data privacy while retaining high accuracy. Besides, an Autoencoder model is provided to encourage practitioners to contribute and then improve model performance. Two bearing fault case studies are conducted to demonstrate the superiority of the proposed approach by comparing it with typical scenarios. This present study's findings offer industrial practitioners insights into investigating the vertical FL in fault diagnosis.

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QSCGAN: An Un-Supervised Quick Self-Attention Convolutional GAN for LRE Bearing Fault Diagnosis Under Limited Label-Lacked Data

Cited by 38 publications

References 27 publications

Augmentation-Assisted Robust Fringe Detection on Unseen Experimental Signals Applied to Optical Feedback Interferometry Using a Deep Network

Augmentation-Assisted Robust Fringe Detection on Unseen Experimental Signals Applied to Optical Feedback Interferometry Using a Deep Network

Augmentation assisted robust fringe detection on unseen experimental signals applied to optical feedback interferometry using a deep network

Privacy‐preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis

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