Prediction maintenance based on vibration analysis and deep learning — A case study of a drying press supported on a Hidden Markov Model

作者：Alexandre Martins, Inácio Fonseca, José Torres Farinha, Jo�ão Reis, António J. Marques Cardoso · 发表于：Applied Soft Computing · 年份：2024 · DOI：10.1016/j.asoc.2024.111885 · 被引用次数：30 · 研究领域：Industrial Vision Systems and Defect Detection、Fault Detection and Control Systems、Manufacturing Process and Optimization

The main objective of this paper is to describe a methodology that was developed to support maintenance decision-making methods based on equipment condition. Condition-Based Maintenance allows to increase equipment availability and maximize investments. This is mainly due to the prevention of unexpected equipment downtime. By avoiding turning on/off industrial equipment, production flows are more efficient, allowing manufacturers to improve the quality of the end-product. The industry aims more and more to correspond satisfying customer expectations. We argue that the methodology developed in this paper adds value to the existing literature, namely because the fact that it is possible to anticipate the state of an equipment without a large amount of support data. In other words, although one could find information gaps regarding the occurrence of failures, it was possible to accurately assess the state of the equipment. This approach is robust, as it can be used in distinct equipment with different sensors, making this methodology generalizable for Condition-Based Maintenance. The paper presents the validation of the preceding through a case study on drying presses in the paper industry. To do so, three states were adopted, namely: “Proper function”; “Alert state”; and “Equipment failure”. The methodology follows a series of steps, going through the collection of values from vibration sensors, imputation of values using Deep Artificial Neural Networks through on-line sensors,...