Deep Health Indicator Extraction: A Method based on Auto-encoders and Extreme Learning Machines



Yang Hu Thomas Palmé Olga Fink


In this paper, we propose a novel deep learning method for feature extraction in prognostics and health management applications. The proposed method is based on Extreme Learning Machines (ELM) and Auto-Encoders (AE), which have demonstrated very good performance and very short training time compared to other deep learning methods on several applications, including image recognition problems. The proposed approach is applied to vibration condition monitoring data to extract features from normal operation (i.e. fault free conditions) without any additional expert knowledge or prior information on the type of signals and the information content in the datasets. The approach demonstrates a better performance in terms of trendability and monotonicity compared to commonly applied feature extraction methods.

How to Cite

Hu, Y., Palmé, T., & Fink, O. (2016). Deep Health Indicator Extraction: A Method based on Auto-encoders and Extreme Learning Machines. Annual Conference of the PHM Society, 8(1).
Abstract 229 | PDF Downloads 217



Bearing, deep learning, feature learning, Extreme Learning Machines, Stacked Auto-Encoders

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