Unsupervised Clustering at the Service of Automatic Anomaly Detection in Industry 4.0

Industrial processes are among the most complex systems, for they are dynamic, nonlinear and comprise many interdependent parts. In the scope of the contemporaneous fourth industrial revolution, the Industry 4.0, the trend is to integrate Artificial Intelligence and hyperconnectivity to intelligently exploit any available resources of a system. A key issue for system management is the control of anomalies, which may cause severe failures if not corrected rapidly, or affect product quality; defining and knowing how to handle them is thus of major importance. This paper proposes to apply Machine Learning-based unsupervised clustering to industrial data to automatically identify the anomalies historically encountered; this is expected to help understand the system and define a framework for diagnosis of failures. Results show that unsupervised clustering is able to detect salient groups of data, which can therefore be classified as anomalies by comparing them to the regular system's behaviors, obtained using another round of unsupervised clustering.