Abstract
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.
Highlights
CISE—Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, Institute of Systems and Robotics, University of Coimbra, 3004-531 Coimbra, Portugal
Experiments were performed with the aim of validating the model that has the best performance in predicting data from the industrial press
Experiments were performed with an LSTM model
Summary
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. The Internet of Things (IoT) is a recent concept, which provides many benefits to different areas, such as maintenance and production management, because it facilitates the automation of tasks such as monitoring and maintenance This results in the popularization of intelligent systems, which are highly dependent on Big Data [1] and are an important area of study, since they offer the tools and methods to acquire and process large volumes of data such as historical production processes, including many production and operating parameters. Modern time-series and other data analysis techniques have been used with success for different tasks, such as freeway traffic analysis [2] and additive manufacturing [3]
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