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Short and long forecast to implement predictive maintenance in a pulp industry
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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
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
Publication date: 2022-03-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2022;24(1):33–41
  • This article presents a predictive model for a wood chip pump system.
  • The Ishikawa diagram and the FMECA analysis were used to identify possible causes of system failures.
  • Development of an algorithm for predicting the values of equipment sensors in the short and long term.
  • The prediction made through Neural Networks had a mean absolute percentage error in all variables lower than 10%.
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
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