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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.

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Part 2: Aging and DisabilitiesInternational audienceTouchscreen technologies have become increasingly common in personal devices, so it seems necessary to improve their accessibility and usability for the older people. In the past years, a lot of studies have been conducted to improve touch interfaces, however, most them do not consider older people with very low attitude with ICTs. Moreover, the majority of studies date back 2014, so they lack to consider the most innovative technologies available today. The present study involves a sample of older people without previous experience with ICTs with the aim of analyzing how basic features of a touchscreen interface affect their performances with typical touch-gestures. A total of 22 participants have been involved. Results partially confirm the existent literature and partially reveal new interesting findings that can be useful to improve the touch screen accessibility for older people