A comparison of envelope and statistical analyses for bearing diagnosis in hot steel rolling mill lines

Abstract

Steel-working industries are characterized by high temperatures and pressures, elevated production speeds, and intense throughput, so that their sudden interruption leads to great money losses. Undoubtedly, they would extremely benefit from Industry 4.0 advancements in predicting anomalies and breakdowns. However, in these industries, the adoption of predictive maintenance methodologies based on the analysis of historical data is a challenging task. Indeed, to avoid costly and dangerous breakdowns, plant managers prefer to apply an early substitution of machine components long before the end of their useful life, making data on fault events, as well as trends on parts degradation, rarely available. This paper reports the outcome of an industrial research project on data-driven fault diagnosis in a steel making production process. The study aims to identify early stage degradations in rotating machines components in hot rolling mill lines. We compare two methodologies: a well-known frequency-domain analysis of vibrations data is correlated with an ad-hoc designed statistical analysis. The comparison has been conducted on experimental data collected in a steel making plant placed in the South of Italy.