Short and long forecast to implement predictive maintenance in a pulp industry

• Autorzy: Rodrigues João Antunes , Farinha José Torres , Mendes Mateus , Mateus Ricardo , Cardoso António Marques

Identyfikatory

DOI

10.17531/ein.2022.1.5

• Języki publikacji: EN

Abstrakty

EN

Predictive maintenance is very important for effective prevention of failures in an industry. The present paper describes a case study where a wood chip pump system was analyzed, and a predictive model was proposed. An Ishikawa diagram and FMECA are used to identify possible causes for system failure. The Chip Wood has several sensors installed to monitor the working conditions and system state. The authors propose a variation of exponential smoothing technique for short time forecasting and an artificial neural network for long time forecasting. The algorithms were integrated into a dashboard for online condition monitoring, where the users are alerted when a variable is determined or predicted to get out of the expected range. Experimental results show prediction errors in general less than 10 %. The proposed technique may be of help in monitoring and maintenance of the asset, aiming at greater availability.

Słowa kluczowe

EN

predictive maintenance; condition based maintenance; time series; artificial neural networks; forecasting

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2022
• Tom: Vol. 24, no. 1
• Strony: 33--41
• Opis fizyczny: Bibliogr. 38 poz., rys., tab.

Twórcy

autor

Rodrigues João Antunes

• CISE - Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro,62001-001 Covilhã, Portugal
• EIGeS - Research Centre in Industrial Engineering, Management and Sustainability, Lusófona University, Campo Grande, 376, 1749-024 Lisboa, Portugal

• https://orcid.org/0000-0002-8210-5468

autor

Farinha José Torres

• Polytechnic of Coimbra – ISEC, Quinta da Nora, 3030-199 Coimbra, Portugal
• University of Coimbra, CEMMPRE - Centre for Mechanical Engineering, Materials and Processes, 3030-788 Coimbra, Portugal

• https://orcid.org/0000-0002-9694-8079

autor

Mendes Mateus

• Polytechnic of Coimbra – ISEC, Quinta da Nora, 3030-199 Coimbra, Portugal

• https://orcid.org/0000-0003-4313-7966

autor

Mateus Ricardo

• EIGeS - Research Centre in Industrial Engineering, Management and Sustainability, Lusófona University, Campo Grande, 376, 1749-024 Lisboa, Portugal

• https://orcid.org/0000-0003-3630-6426

autor

Cardoso António Marques

• CISE - Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro,62001-001 Covilhã, Portugal

• https://orcid.org/0000-0001-8737-6999

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).