Agnostic Learning for Packing Machine Stoppage Prediction in Smart Factories

TL;DR

This paper explores agnostic machine learning approaches for predicting packing machine stoppages in smart factories using only a single operational signal, demonstrating promising results in Industry 4.0 predictive maintenance.

Contribution

It introduces an agnostic learning methodology that relies solely on one signal for machine stoppage prediction, simplifying data requirements in industrial settings.

Findings

Achieved promising prediction performance on three use cases.

Validated effectiveness of single-signal approach in predictive maintenance.

Demonstrated applicability in real industrial environment.

Abstract

The cyber-physical convergence is opening up new business opportunities for industrial operators. The need for deep integration of the cyber and the physical worlds establishes a rich business agenda towards consolidating new system and network engineering approaches. This revolution would not be possible without the rich and heterogeneous sources of data, as well as the ability of their intelligent exploitation, mainly due to the fact that data will serve as a fundamental resource to promote Industry 4.0. One of the most fruitful research and practice areas emerging from this data-rich, cyber-physical, smart factory environment is the data-driven process monitoring field, which applies machine learning methodologies to enable predictive maintenance applications. In this paper, we examine popular time series forecasting techniques as well as supervised machine learning algorithms in the applied context of Industry 4.0, by transforming and preprocessing the historical industrial dataset of a packing machine's operational state recordings (real data coming from the production line of a manufacturing plant from the food and beverage domain). In our methodology, we use only a single signal concerning the machine's operational status to make our predictions, without considering other operational variables or fault and warning signals, hence its characterization as ``agnostic''. In this respect, the results demonstrate that the adopted methods achieve a quite promising performance on three targeted use cases.