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A self-adaptive deep belief network with Nesterov momentum for the fault diagnosis of rolling element bearings

Published: 02 June 2017 Publication History

Abstract

Effective fault diagnosis of rotating machinery helps prevent unexpected machine breakdowns resulting from the failure of essential components. Traditional artificial intelligence methods, such as artificial neural networks and support vector machine, have been proved to be effective in fault identification. However, extracting features manually requires a high degree of expertise in signal processing. Deep belief network (DBN) has gained popularity as a new method for machine learning because of its potential merits such as its capability to extract effective features automatically in fault diagnosis. Therefore, a novel adaptive learning rate DBN with Nesterov momentum is proposed in this study for the fault diagnosis of rolling element bearings. An experiment is conducted using a dataset of bearing health states obtained from a test rig to substantiate the utility of the proposed DBN architecture. Results show that the proposed method demonstrates impressive performance in fault pattern recognition. Comparison analyses are further conducted to demonstrate that the advanced method performs better than current methods.

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Cited By

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  • (2021)Novel Three-Stage Feature Fusion Method of Multimodal Data for Bearing Fault DiagnosisIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2021.307123270(1-10)Online publication date: 2021
  • (2019)Improved Hierarchical Adaptive Deep Belief Network for Bearing Fault DiagnosisApplied Sciences10.3390/app91633749:16(3374)Online publication date: 16-Aug-2019

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cover image ACM Other conferences
ICDLT '17: Proceedings of the 2017 International Conference on Deep Learning Technologies
June 2017
108 pages
ISBN:9781450352321
DOI:10.1145/3094243
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Southwest Jiaotong University

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New York, NY, United States

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Published: 02 June 2017

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Author Tags

  1. adaptive learning rate
  2. deep belief network
  3. fault diagnosis
  4. feature extraction

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Cited By

View all
  • (2021)Novel Three-Stage Feature Fusion Method of Multimodal Data for Bearing Fault DiagnosisIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2021.307123270(1-10)Online publication date: 2021
  • (2019)Improved Hierarchical Adaptive Deep Belief Network for Bearing Fault DiagnosisApplied Sciences10.3390/app91633749:16(3374)Online publication date: 16-Aug-2019

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