BSOM: A Two-Level Clustering Method Based on the Efficient Self-Organizing Maps

At the very beginning of the Industry 4.0 era, automated systems and automatic knowledge conceptualization are becoming more and more essential. The ever faster, ever more resource-demanding processes are raising a pressing need for highly efficient handling of the systems. While the industries produce ever more, there is less and less time for back-up and quality assessment; a piece of solution may come along a real-time and automated monitoring, based on sensors’ data so as to assess products’ validity: this is the areas of Behavior Identification and Anomaly Detection. In this paper, we propose to use Machine Learning and data-driven clustering to automatically identify the real behaviors of a system, and therefore its possible anomalies. More than the methodology, we mostly propose a more stable clustering method based on the Self-Organizing Maps to achieve that purpose. We apply both methodology and that improved clustering method to real industrial data, and we show that it is more efficient, more stable and more relevant to dynamic systems.