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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Comparison of Different Features and Neural Networks for Predicting Industrial Paper Press Condition João Rodrigues, José Farinha, Mateus Mendes, Ricardo Mateus, António Cardoso Energies
Automatic Risk Assessment for an Industrial Asset Using Unsupervised and Supervised Learning João Rodrigues, Alexandre Martins, Mateus Mendes, José Farinha, Ricardo Mateus, Antonio Cardoso Energies
Double Layered Priority based Gray Wolf Algorithm (PrGWO-SK) for safety management in IoT network through anomaly detection Agrawal Prasad, Nanhay Singh Eksploatacja i Niezawodnosc - Maintenance and Reliability
Improved GRU prediction of paper pulp press variables using different pre-processing methods Balduíno Mateus, Mateus Mendes, Farinha Torres, Cardoso Marques, Rui Assis, Hamzeh Soltanali Production & Manufacturing Research
Online Monitoring of Sensor Calibration Status to Support Condition-Based Maintenance Alexandre Martins, Inácio Fonseca, José Farinha, João Reis, António Cardoso Sensors
Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models Alexandre Martins, Balduíno Mateus, Inácio Fonseca, José Farinha, João Rodrigues, Mateus Mendes, António Cardoso Energies
An Artificial Intelligence Approach for Improving Maintenance to Supervise Machine Failures and Support Their Repair Izabela Rojek, Małgorzata Jasiulewicz-Kaczmarek, Mariusz Piechowski, Dariusz Mikołajewski Applied Sciences
Leveraging Classical Statistical Methods for Sustainable Maintenance in Automotive Assembly Equipment Juan Bucay-Valdiviezo, Pedro Escudero-Villa, Jenny Paredes-Fierro, Manuel Ayala-Chauvin Sustainability
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