Study on the machine-learning based system for detecting abnormal pressure drops in hydraulic press machines

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  • Published: 17 January 2024
  • Volume 130 , pages 5045–5054, ( 2024 )

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  • Naoyuki Takeda 1 ,
  • Koki Shige 1 &
  • Osamu Terashima   ORCID: orcid.org/0000-0002-8515-5487 1  

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Due to declining working-age populations in some countries, manufacturing and production sites are increasingly leveraging digital technologies to boost efficiency and labor productivity. In response to this trend, we have developed a system that swiftly assesses the operational status of machinery to optimize production efficiency within manufacturing companies, thus shortening the time needed for machine inspections and repairs. Our system, a machine learning-based approach to failure detection, specifically targets pressure drops in hydraulic press machines. We installed vibrational acceleration sensors on the cylinders—the press machine’s primary components—and collected continuous signal data. By modeling normal operations using standard deviation, crest factor, and maximum signal values, we can detect deviations and temporal changes in the data that indicate failures and anomalies. This allows for the proactive prediction and monitoring of potential failures.

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Acknowledgements

The authors sincerely thank Mr. Keiji Kawahara, Mr. Kiyoaki Inaba, and Mr. Yoshihisa Mashita from Hokuriku Kenko Co. Ltd. for their valuable advice and fruitful discussions on the hydraulic pressing machine.

We would like to express our gratitude for the financial support received from Toyama Prefecture and Fluid Power Technology Promotion Foundation in conducting this research.

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Toyama Prefectural University, 5180, Kurokawa, Imizu, Toyama, 9390398, Japan

Naoyuki Takeda, Zhe Li, Koki Shige & Osamu Terashima

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Contributions

Osamu Terashima conceived the idea of the study. Zhe Li, Naoyuki Takeda and Koki Shige developed the statistical analysis plan and conducted statistical analyses. Osamu Terashima contributed to the interpretation of the results. Zhe Li drafted the original manuscript. Naoyuki Takeda performed experiments. All authors reviewed the manuscript draft and revised it critically on intellectual content. All authors approved the final version of the manuscript to be published.

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Correspondence to Osamu Terashima .

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Takeda, N., Li, Z., Shige, K. et al. Study on the machine-learning based system for detecting abnormal pressure drops in hydraulic press machines. Int J Adv Manuf Technol 130 , 5045–5054 (2024). https://doi.org/10.1007/s00170-024-13001-3

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Received : 19 September 2023

Accepted : 07 January 2024

Published : 17 January 2024

Issue Date : February 2024

DOI : https://doi.org/10.1007/s00170-024-13001-3

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