Publication: Automated fault detection using deep belief networks for the quality inspection of electromotors
Automated fault detection using deep belief networks for the quality inspection of electromotors
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Sun, J., Wyss, R., Steinecker, A., & Glocker, P. (2014). Automated fault detection using deep belief networks for the quality inspection of electromotors. Tm - Technisches Messen, 81(5), 255–263. https://doi.org/10.1515/teme-2014-1006
Abstract
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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:
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- Sun, Jianwen
- Wyss, Reto
- Steinecker, Alexander
- Glocker, Philipp
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Citations
Sun, J., Wyss, R., Steinecker, A., & Glocker, P. (2014). Automated fault detection using deep belief networks for the quality inspection of electromotors. Tm - Technisches Messen, 81(5), 255–263. https://doi.org/10.1515/teme-2014-1006
