A general end-to-end diagnosis framework for manufacturing systems

A general end-to-end diagnosis framework for manufacturing systems. Yuan, Y., Ma, G., Cheng, C., Zhou, B., Zhao, H., Zhang, H., & Ding, H. National Science Review, 7(2):418–429, February, 2020.

Paper doi abstract bibtex

The manufacturing sector is envisioned to be heavily influenced by artificial-intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in the manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications. Here, we propose a general data-driven, end-to-end framework for the monitoring of manufacturing systems. This framework, derived from deep-learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to automatically extract hidden degradation features from noisy, time-course data. We have experimented the proposed framework on 10 representative data sets drawn from a wide variety of manufacturing applications. Results reveal that the framework performs well in examined benchmark applications and can be applied in diverse contexts, indicating its potential use as a critical cornerstone in smart manufacturing.

@article{yuan_general_2020,
	title = {A general end-to-end diagnosis framework for manufacturing systems},
	volume = {7},
	issn = {2095-5138},
	url = {https://doi.org/10.1093/nsr/nwz190},
	doi = {10.1093/nsr/nwz190},
	abstract = {The manufacturing sector is envisioned to be heavily influenced by artificial-intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in the manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications. Here, we propose a general data-driven, end-to-end framework for the monitoring of manufacturing systems. This framework, derived from deep-learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to automatically extract hidden degradation features from noisy, time-course data. We have experimented the proposed framework on 10 representative data sets drawn from a wide variety of manufacturing applications. Results reveal that the framework performs well in examined benchmark applications and can be applied in diverse contexts, indicating its potential use as a critical cornerstone in smart manufacturing.},
	number = {2},
	urldate = {2022-05-14},
	journal = {National Science Review},
	author = {Yuan, Ye and Ma, Guijun and Cheng, Cheng and Zhou, Beitong and Zhao, Huan and Zhang, Hai-Tao and Ding, Han},
	month = feb,
	year = {2020},
	pages = {418--429},
}

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