Case study of system development for predictive maintenance 4.0

Authors

  • Eduardo Marcio Zaro Universidade de Caxias do Sul (UCS), Rio Grande do Sul, RS, Brasil.
  • Carine Getruldes Webber Universidade de Caxias do Sul (UCS), Rio Grande do Sul, RS, Brasil.

DOI:

https://doi.org/10.14488/1676-1901.v22i3.4557

Keywords:

Industry 4.0, Predictive Maintenance, MindSphere, Vibration, Manufacturing

Abstract

The fourth industrial revolution presents several technologies for the development of society and especially for the manufacturing sector. Inserted in this new world, the case study aims to explore concepts of predictive maintenance with analysis and failure prevention for equipment that operate with vibration. Analyzing predictive maintenance 4.0 concepts already implemented in the market and incorporating new technologies, we tend to obtain downtime results through these concepts explored in the work. By analyzing the data collected through cloud tools and IoT sensors, we were able to determine parameters and the behavior of the equipment. With this prevention of the facts, it was possible to implement real-time alerts of any factor that could become a failure, thus predicting corrective action in the equipment. Working in this way, it was possible to obtain a 24% reduction in equipment downtime, bringing gains to the company and cost reductions for the final product. Predictive maintenance, along with other technologies from industry 4.0, has great potential for studies and improvements, incorporating more and more machine learning and artificial intelligence, making more and more intelligent equipment and decision makers themselves.

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References

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Published

2023-03-21

How to Cite

Zaro, E. M., & Webber, C. G. (2023). Case study of system development for predictive maintenance 4.0. Revista Produção Online, 22(3), 3418–3340. https://doi.org/10.14488/1676-1901.v22i3.4557

Issue

Vol. 22 No. 3 (2022)

Section

Papers

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