In the field of industry, the features of Industry 4.0 are showing a growing impact on the productive processes, since the companies are financially encouraged to move towards industrial automation that integrates some new production technologies aimed at improving working conditions, creating new business models, and increasing the productivity and product quality of their plants. The current and future sustainable economic growth of companies worldwide are today, more than ever, increasingly based on the value and the information created by data. Furthermore, the information on where to carry out intervention is provided to the maintenance technicians between 30 min and 3 h before the breakdown. Tuning the time range predictions enabled the shop floor and the maintenance operators to achieve sensitivity and specificity values higher than 90%, but the performance results are constantly improved since new data are collected daily. The optimal approach currently used involves combining PCA and SIMCA models, whose performances are continuously monitored, and the various models are updated and tested weekly. Several MDA approaches were tested (e.g., PCA, PLS-DA, SVM, XGBoost, and SIMCA) and validated in the plant (in terms of repeated double cross-validation strategies). The instrumentation under evaluation was equipped with sensors already connected to PLCs (only on/off switches, i.e., neither analog sensors nor continuous measurements are available, and the data are in sparse binary format) so that the data provided by PLCs were acquired in a binary way before being processed by multivariate data analysis (MDA) models. The instrumentation and PLCs related to the truck sides’ paneling phase were considered at the beginning of the project. For this purpose, several predictive maintenance models were tested starting from databases on programmable logic controllers (PLCs) already available, thus taking advantage of Machine Learning techniques without investing additional resources in purchasing or installing new sensors. This project currently allows maintenance technicians to intervene on machinery preventively, avoiding breakdowns or stops in the production process. The project’s main aim was to establish a predictive maintenance method in real-time at a pilot plant (CNHi Iveco, Brescia, Italy). The “DOLPHINS” project started in 2018 under a collaboration between three partners: CNH Industrial Iveco (CHNi), RADA (an informatics company), and the Chemistry Department of the University of Turin.
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