Use of artificial intelligence techniques in characterization of vibration signals for application in agri-food engineering

Abstract Bottling machinery is a critical component in agri-food industries, where maintaining operational efficiency is key to ensuring productivity and minimizing economic losses. Early detection of faulty conditions in this equipment can significantly improve maintenance procedures and overall system performance. This research focuses on health monitoring of gripping pliers in bottling plants, a crucial task that has traditionally relied on analyzing raw vibration signals or using narrowly defined, application-specific features. However, these methods often face challenges related to limited robustness, high computational costs, and sensitivity to noise. To address these limitations, we propose a novel approach based on generic features extracted through basic signal processing techniques applied to vibration signals. These features are then classified using a random forest algorithm, enabling an effective analysis of health states. The proposed method is evaluated against traditional approaches and demonstrates clear advantages, including higher accuracy in detecting and classifying faulty conditions, greater robustness against random perturbations, and a reduced computational cost. Additionally, the method requires fewer training instances to achieve reliable performance. This study highlights the potential of artificial intelligence and signal processing techniques in predictive maintenance, offering a scalable and efficient solution for fault detection in manufacturing processes, particularly within the agri-food sector.