Prognostics and Health Management of machine devices and parts is a hot topic in the Industry 4.0 era. In this fashion, automated procedures to evaluate machinery working conditions are essential to minimize downtime and maintenance costs. In this work, we study how to monitor the decrease in performance of a paper sheet feeder for the packaging industry under heavy-duty cycle operations. The main measurable outcome of such degradation is the increase in backlash among the device moving components. A wide variety of methods and procedures is available to tackle this monitoring problem. In this paper, we analyze the use of a simple yet efficient diagnosis methodology that can exploit machinery controllers (i.e., Programmable Logic Controllers) edge-computing capabilities. Vibration measurements are known in the literature to retain information about the system's mechanics. Model-of Signals, a data-driven approach based on black box system identification, allows to extract that information reliably during machinery working cycle. The refinement of those data using machine learning allows the retrieval of knowledge about the health state of the machine. In this study, the feeder mechanism is run to failure with its parts backlash measured at given time intervals. Accelerometer signals are modelled as AutoRegressive processes whose coefficients are then considered as features to feed to machine learning algorithms, which are employed to perform severity evaluation of the ongoing degradation. Estimation and prediction are both implementable on-board the controller, while the learning task can be carried out remotely, in a cloud computing perspective. The exploitation of AutoRegressive modelling gives a simple and inherent methodology for feature selection, serving as a foundation of the machine learning stage. We make use of a Support Vector Machine algorithm to analyze how obtained models represent the various levels of backlash in the device and develop a suitable predictor of the degradation severity. Finally, the results of the application of the methodology to the case study are shown.
Condition Monitoring, System Identification, Machine Learning, Prognostics and Health Management, Automatic Machines, PLC, SVM, Model-of-Signals, Diagnosis, Industry 4.0
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