Paper
12 December 2024 Health monitoring of converter tilting reducer equipment based on stress wave technology
Hong Kang
Author Affiliations +
Proceedings Volume 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024); 1343913 (2024) https://doi.org/10.1117/12.3055339
Event: Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 2024, Xiamen, China
Abstract
With the development of Industrial Internet, the idea of Industrial Internet has penetrated into all walks of life, actively promoting the transformation and upgrading of China's manufacturing industry. In the metallurgical industry, for some special and typical low-speed and heavy-duty equipment, they play an extremely crucial role in the production process. However, currently, the operation and maintenance of such equipment are mainly manual, and usually only when the equipment has already malfunctioned or the malfunction is relatively serious, human workers will discover it and take post maintenance measures. In view of this issue, this article introduces a unique technology - stress wave technology, which can accurately locate and analyze faults in the early stages of faults. It has been well applied in the metallurgical industry, especially in typical low-speed and heavy-duty equipment. The online monitoring and diagnosis of the converter tilting primary and secondary reducers in a steelmaking plant of a certain steel enterprise was completed using this technology. Finally, the monitoring data showed that this monitoring technology can comprehensively reflect the operating status of the converter tilting reducers and effectively monitor the health status of the equipment.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Hong Kang "Health monitoring of converter tilting reducer equipment based on stress wave technology", Proc. SPIE 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 1343913 (12 December 2024); https://doi.org/10.1117/12.3055339
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KEYWORDS
Diagnostics

Vibration

Histograms

Analytical research

Ear

Sensors

Statistical analysis

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