The "DOLPHINS" Project: A Low-Cost Real-Time Multivariate Process Control From Large Sensor Arrays Providing Sparse Binary Data

Abstract

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. The project's main aim was to establish a predictive maintenance method in real-time at a pilot plant (CNHi Iveco, Brescia, Italy). This project currently allows maintenance technicians to intervene on machinery preventively, avoiding breakdowns or stops in the production process. 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. The instrumentation and PLCs related to the truck sides' paneling phase were considered at the beginning of the project. 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. 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 optimal approach currently used involves combining PCA and SIMCA models, whose performances are continuously monitored, and the various models are updated and tested weekly. 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. Furthermore, the information on where to carry out intervention is provided to the maintenance technicians between 30 min and 3 h before the breakdown.

Keywords: machine learning; multivariate data analysis; predictive maintenance; principal component analysis; soft independent modeling by class analogy; sparse binary data.