Anticipating Future Behavior of an Industrial Press Using LSTM Networks

• Autor(a) principal: Mateus, Balduíno César
• Data de Publicação: 2021
• Outros Autores: Mendes, Mateus, Farinha, José Torres, Cardoso, António Marques
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/100622; https://doi.org/10.3390/app11136101
• topic: time series prediction; LSTM prediction; deep learning prediction; predictive maintenance

Predictive maintenance is very important in industrial plants to support decisions aiming to maximize maintenance investments and equipment’s availability. This paper presents predictive models based on long short-term memory neural networks, applied to a dataset of sensor readings. The aim is to forecast future equipment statuses based on data from an industrial paper press. The datasets contain data from a three-year period. Data are pre-processed and the neural networks are optimized to minimize prediction errors. The results show that it is possible to predict future behavior up to one month in advance with reasonable confidence. Based on these results, it is possible to anticipate and optimize maintenance decisions, as well as continue research to improve the reliability of the model.