K-Means algorithm to detect solenoid valve function status.

  • Diego Yánez Vinnytsia National Technical University, Ukraine
  • Iván Molina Escuela Politécnica Nacional, Ecuador
  • Andrés Gallardo Politecnico di Milano, Italy
DOI: https://doi.org/10.46480/esj.5.3.157
Keywords: Automation, Artificial Intelligence, Electronic Technology

Abstract

Now days, the applications of artificial intelligence within engineering processes in general are extremely broad, given the characteristics of both artificial intelligence and engineering in general. In this investigative work we expose the use of artificial intelligence, specifically clustering to be able to detect when a valve is faulty or not, in this way to be able to know, precisely, the operating state of the solenoid valve by Artificial Intelligence from collected data, providing with the analysis carried out in MatLab as a Data-Set for training process. The item of study for this article is a spring compensated three position solenoid valve (DIVW style C). The valve is used in the present experiment in two different operating regions: at 5 MPa and 20 MPa. In addition, a scenario in which the valve presents damage was added to the analysis, which consists of a groove 0.1 [mm] deep.

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Published
2021-11-30
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How to Cite
Yánez, D., Molina, I., & Gallardo, A. (2021). K-Means algorithm to detect solenoid valve function status. Ecuadorian Science Journal, 5(3), 220-238. https://doi.org/10.46480/esj.5.3.157
Issue
Vol 5 No 3 (2021): RITAM 2021
Section
Research Paper

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