Refining data to predict valve failures

A model is presented for using and refining data to monitor and identify failures early on in control and on-off valves.

ABSTRACT

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A model is presented for utilizing and refining data to monitor and identify failures early on in control and on-off valves. This model can help increase valve reliability, strengthen predictive maintenance, decrease operation risk, and evolve programs adhering to industry 4.0. The maintenance strategy applied in the pulp and paper industry has a direct impact on operation and maintenance costs. In the valve department, there still exists a preventive maintenance culture based on time. Therefore, the purpose of this work is to present a model for utilizing and refining data, providing the steps to structure and develop a predictive work methodology in valves using history and performance trend data presented in valve controllers of valves in the pulp and paper industry.

The methodology is oriented to share with readers to provide fast decision-making, allowing work to be developed with a focus on data refinement, that is, to identify and understand what your valves are “saying” through history or performance trends, to boost operation safety and high valve performance. A Gartner Group study discovered that in industrial plant processes, 50% of preventive maintenance tasks were not necessary and 10% were actually harmful. As a result, the company allocated resources to the “wrong” problems, and it was also possible to identify the inclusion of problems in good valves. Using this methodology oriented at refining data and focusing on valve triage, as presented in this model, guides maintenance teams towards the “right” problems and not spending time in the field working on the “wrong” problems and, consequently, focusing tasks on the valves that are really need attention.

The number of maintenance tasks in the field can be decreased; therefore, industrial plants become a safer place by reducing maintenance teams’ exposure to risk without the real need to remove the valve and thus decrease potential failures and accidents.

Keywords: valve, predictive, performance, data, positioner

O PAPEL vol. 85, N.o 3, pp. 83 – 90 – MAR 2024

Authors:

Guilherme Golfetto1
1
Valmet Flow Control, Brazil

Corresponding author: Golfetto, G: Sorocaba/SP/Brazil – 18087-101. Mobile: +55-15-99130-1678. E-mail: [email protected]

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