A generic fault prognostics algorithm for manufacturing industries using unsupervised machine learning classifiers

This paper presents a generic methodology for fault forecasting or prognosis in industrial equipment. Particularly, this technique regards training some unsupervised machine learning model using high amount of historical process data from such equipment as input and stop data as reference. The goal is to correlate as strongly as possible the anomalies found by the model in the process data with upcoming faults, according to a forecasting...