Automated fault detection using deep belief networks for the quality inspection of electromotors

Abstract Vibration inspection of electro-mechanical components and systems is an important tool for automated reliable online as well as post-process production quality assurance. Considering that bad electromotor samples are very rare in the production line, we propose a novel automated fault detection method named “Tilear”, based on Deep Belief Networks (DBNs) training only with good electromotor samples. Tilear consctructs an auto-encoder with DBNs, aiming to reconstruct the inputs as closely as possible. Tilear is structured in two parts: training and decision-making. During training, Tilear is trained only with informative features extracted from preprocessed vibration signals of good electromotors, which enables the trained Tilear only to know how to reconstruct good electromotor vibration signal features. In the decision-making part, comparing the recorded signal from test electromotor and the Tilear reconstructed signal, allows to measure how well a recording from a test electromotor matches the Tilear model learned from good electromotors. A reliable decision can be made.