Predictive Maintenance (PdM) is a prominent strategy comprising all the operational techniques and actions required to ensure machine availability and to prevent a machine-down failure. One of the main challenges of PdM is to design and develop an embedded smart system to monitor and predict the health status of the machine. In this work, we use a data-driven approach based on machine learning applied to woodworking industrial machines for a major woodworking Italian corporation. Predicted failures probabilities are calculated through tree-based classification models (Gradient Boosting, Random Forest and Extreme Gradient Boosting) and calculated as the temporal evolution of event data. This is achieved by applying temporal feature engineering techniques and training an ensemble of classification algorithms to predict Remaining Useful Lifetime (RUL) of woodworking machines. The effectiveness of the proposed method is showed by testing an independent sample of additional woodworking machines without presenting machine down. The Gradient Boosting model achieved accuracy, recall, and precision of 98.9%, 99.6%, and 99.1%. Our predictive maintenance approach deployed on a Big Data framework allows screening simultaneously multiple connected machines by learning from terabytes of log data. The target prediction provides salient information which can be adopted within the maintenance management practice.
ABSTRACT:Digital documentation and high-quality 3D representation are always more requested in many disciplines and areas due to the large amount of technologies and data available for fast, detailed and quick documentation. This work aims to investigate the area of medium and small sized artefacts and presents a fast and low cost acquisition system that guarantees the creation of 3D models with an high level of detail, making the digitalization of cultural heritage a simply and fast procedure. The 3D models of the artefacts are created with the photogrammetric technique Structure From Motion that makes it possible to obtain, in addition to three-dimensional models, high-definition images for a deepened study and understanding of the artefacts. For the survey of small objects (only few centimetres) it is used a macro lens and the focus stacking, a photographic technique that consists in capturing a stack of images at different focus planes for each camera pose so that is possible to obtain a final image with a higher depth of field. The acquisition with focus stacking technique has been finally validated with an acquisition with laser triangulation scanner Minolta that demonstrates the validity compatible with the allowable error in relation to the expected precision.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.