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Neural Processing Letters

Erschienen in:

08.08.2019

A High Generalizable Feature Extraction Method Using Ensemble Learning and Deep Auto-Encoders for Operational Reliability Assessment of Bearings

verfasst von: Xianguang Kong, Yang Fu, Qibin Wang, Hongbo Ma, Xiaodong Wu, Gang Mao

Erschienen in: Neural Processing Letters | Ausgabe 1/2020

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Abstract

Feature extraction is a major challenge in operational reliability assessment, which requires techniques and prior knowledge. Deep auto-encoder (DAE) is a popular deep learning method and is widely used in feature extraction. However, low generalization ability and structure parameters design are still the major problems of DAE for operational reliability assessment. To overcome the two problems, an ensemble DAE is proposed for operational reliability assessment. Firstly, different structure parameters are employed to design a series of DAEs for feature learning from the measured data. Secondly, a feature ensemble strategy is designed to enhance the generalization ability of the DAE model, in which the features learned by different DAEs are clustered to remove the irrelevant DAEs and select the more general feature subset. Finally, the operational reliability indicator is defined by the Euclidean distance of the selected features and the operational reliability model is developed. The proposed method is utilized to analyze the experimental bearings and the results indicate that the proposed method is effective for operational reliability assessment.

Vorheriger Artikel Global Asymptotic Stability of Periodic Solutions for Neutral-Type BAM Neural Networks with Delays

Nächster Artikel Finite Time Stability Analysis of Fractional-Order Complex-Valued Memristive Neural Networks with Proportional Delays

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Titel: A High Generalizable Feature Extraction Method Using Ensemble Learning and Deep Auto-Encoders for Operational Reliability Assessment of Bearings
verfasst von: Xianguang Kong
Yang Fu
Qibin Wang
Hongbo Ma
Xiaodong Wu
Gang Mao
Publikationsdatum: 08.08.2019
Verlag: Springer US
Erschienen in: Neural Processing Letters / Ausgabe 1/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-019-10094-w

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