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2019 AAAI AAAI 2019

A Machine Learning Suite for Machine Components’ Health-Monitoring

Abstract

Abstract This paper studies an intelligent technique for the healthmonitoring and prognostics of common rotary machine components, with regards to bearings in particular. During a run-to-failure experiment, rich unsupervised features from vibration sensory data are extracted by a trained sparse autoencoder. Then, the correlation of the initial samples (presumably healthy), along with the successive samples, are calculated and passed through a moving-average filter. The normalized output which is referred to as the auto-encoder correlation based (AEC) rate, determines an informative attribute of the system, depicting its health status. AEC automatically identifies the degradation starting point in the machine component. We show that AEC rate well-generalizes in several run-tofailure tests. We demonstrate the superiority of the AEC over many other state-of-the-art approaches for the health monitoring of machine bearings.

🚀 Conference Pioneer — AAAI 2019
🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning
🧭 Keyword Pioneer — predictive maintenance
🐣 Hot Topic Early Bird — sparse autoencoder
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Ramin Hasani , Guodong Wang , Radu Grosu

Topics

Machine Learning > Core Methods > Representation Learning Machine Learning > Learning Types > Unsupervised Learning Machine Learning > Application Areas > Risk Management Deep Learning > Architectures > Autoencoders Machine Learning > Core Methods > Feature Learning Machine Learning > Learning Types > Anomaly Detection

Keywords

anomaly detection fault diagnosis sparse autoencoder predictive maintenance health monitoring vibration analysis degradation detection auto-encoder correlation
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