Predicting pumping station failures using unsupervised machine learning

Machine learning is at the heart of many innovative artificial intelligence technologies. Implementation of machine learning-based programs in production allows predicting breakdowns of industrial equipment, thus preventing huge operational maintenance costs. Based on temporal data from pumping station sensors, the effectiveness of various teacherless machine learning algorithms based on the Scikit-Learn Python library is investigated.