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Retraction published on 20 February 2020, see Appl. Sci. 2020, 10(4), 1413.
Open AccessArticle

Laplacian Eigenmaps Feature Conversion and Particle Swarm Optimization-Based Deep Neural Network for Machine Condition Monitoring

1
Discipline of ICT, School of Technology, Environments and Design, University of Tasmania, Hobart TAS7005, Australia
2
School of Engineering, Australian Maritime College, University of Tasmania, Hobart TAS7005, Australia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(12), 2611; https://doi.org/10.3390/app8122611
Received: 11 November 2018 / Revised: 6 December 2018 / Accepted: 11 December 2018 / Published: 13 December 2018
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
This work reports a novel method by fusing Laplacian Eigenmaps feature conversion and deep neural network (DNN) for machine condition assessment. Laplacian Eigenmaps is adopted to transform data features from original high dimension space to projected lower dimensional space, the DNN is optimized by the particle swarm optimization algorithm, and the machine run-to-failure experiment were investigated for validation studies. Through a series of comparative experiments with the original features, two other effective space transformation techniques, Principal Component Analysis (PCA) and Isometric map (Isomap), and two other artificial intelligence methods, hidden Markov model (HMM) as well as back-propagation neural network (BPNN), the present method in this paper proved to be more effective for machine operation condition assessment. View Full-Text
Keywords: Laplacian Eigenmaps; feature conversion; deep neural network; particle swarm optimization; condition assessment Laplacian Eigenmaps; feature conversion; deep neural network; particle swarm optimization; condition assessment
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Yuan, N.; Yang, W.; Kang, B.; Xu, S.; Wang, X. Laplacian Eigenmaps Feature Conversion and Particle Swarm Optimization-Based Deep Neural Network for Machine Condition Monitoring. Appl. Sci. 2018, 8, 2611.

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