Use of collaborative concept mapping in team diagnosis

Pan, Dan; Wang, Ting‐Yi; Zhang, Xuegang; Li, Zhizhong

doi:10.1002/hfm.20894

Cited by 1 publication

(1 citation statement)

References 51 publications

(70 reference statements)

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“…Following a similar logic, a knowledge graph was introduced for all stakeholders to exchange their diagnosis experience, which established semantic associations in diagnosis knowledge [26]. In addition, an assist visualisation tool is developed to facilitate communication and reduce ambiguity among teammates [27].…”

Section: Collaborative Fault Diagnosismentioning

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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Section: Collaborative Fault Diagnosismentioning

confidence: 99%