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Fault diagnosis of train traction motor bearing based on improved deep residual network

Research article (Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science, 2023) · cited 18× · AI/ML
academia openalex:W4386546830
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Fault diagnosis of train traction motor bearing based on improved deep residual network

Summary

Fault diagnosis of train traction motor bearing based on improved deep residual network is a scholarly article[1].

Key Facts

  • Fault diagnosis of train traction motor bearing based on improved deep residual network's instance of is recorded as scholarly article[2].

References

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Fault diagnosis of train traction motor bearing based on improved deep residual network. Retrieved May 24, 2026, from https://4ort.xyz/entity/fault-diagnosis-of-train-traction-motor-bearing-based-on-improved-deep-residual-network
MLA “Fault diagnosis of train traction motor bearing based on improved deep residual network.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/fault-diagnosis-of-train-traction-motor-bearing-based-on-improved-deep-residual-network.
BibTeX @misc{4ortxyz_fault-diagnosis-of-train-traction-motor-bearing-based-on-improved-deep-residual-network_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Fault diagnosis of train traction motor bearing based on improved deep residual network}}, year = {2026}, url = {https://4ort.xyz/entity/fault-diagnosis-of-train-traction-motor-bearing-based-on-improved-deep-residual-network}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Fault diagnosis of train traction motor bearing based on improved deep residual network — https://4ort.xyz/entity/fault-diagnosis-of-train-traction-motor-bearing-based-on-improved-deep-residual-network (retrieved 2026-05-24)

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