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

Yuan, Nanqi; Yang, Wenli; Kang, Byeong Ho; Xu, Shuxiang; Wang, Xiaolin

doi:10.3390/app8122611

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…In this paper, a linear degradation model based on an inverse Kalman filter to imitate the stochastic deterioration process was proposed. In addition, it referred to others about state monitoring methods, such as the extended Kalman filter (EKF) applied in the estimation of the position of the intake valve of the engine, and a theoretical basis, which was built up for the algorithm proposed in this paper [18,19]. Although Wang et al [17] benefits were well proved, the algorithm was not sensitive to the initial real-time monitoring degradation data in solving the RUL estimation.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Deterioration Process Based Approach for Micro Switches Remaining Useful Life Estimation

Zhang

Shao

Chang³

et al. 2019

Applied Sciences

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Real-time prediction of remaining useful life (RUL) is one of the most essential works inprognostics and health management (PHM) of the micro-switches. In this paper, a lineardegradation model based on an inverse Kalman filter to imitate the stochastic deterioration processis proposed. First, Bayesian posterior estimation and expectation maximization (EM) algorithm areused to estimate the stochastic parameters. Second, an inverse Kalman filter is delivered to solvethe errors in the initial parameters. In order to improve the accuracy of estimating nonlinear data,the strong tracking filtering (STF) method is used on the basis of Bayesian updating Third, theeffectiveness of the proposed approach is validated on an experimental data relating tomicro-switches for the rail vehicle. Additionally, it proposes another two methods for comparisonto illustrate the effectiveness of the method with an inverse Kalman filter in this paper. Inconclusion, a linear degradation model based on an inverse Kalman filter shall deal with errors inRUL estimation of the micro-switches excellently.

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Section: Introductionmentioning

confidence: 99%

A Stochastic Deterioration Process Based Approach for Micro Switches Remaining Useful Life Estimation

Zhang

Shao

Chang³

et al. 2019

Applied Sciences

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“…Gear and bearing are very important parts in mechanical systems, because their working conditions can directly affect safety and stable operations. Therefore, it is of great significance to detect and diagnose their operating states [1][2][3][4]. The main denoising algorithms aim at one dimensional time domain vibration signals in the field of current mechanical signal noise reduction methods [5].…”

Section: Introductionmentioning

confidence: 99%

Fault Classification of Rotary Machinery Based on Smooth Local Subspace Projection Method and Permutation Entropy

Xiao

2019

Applied Sciences

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Collected mechanical signals usually contain a number of noises, resulting in erroneous judgments of mechanical condition diagnosis. The mechanical signals, which are nonlinear or chaotic time series, have a high computational complexity and intrinsic broadband characteristic. This paper proposes a method of gear and bearing fault classification, based on the local subspace projection noise reduction and PE. A novel nonlinear projection noise reduction method, smooth orthogonal decomposition (SOD), is proposed to denoise the vibration signals of various operation conditions. SOD can decompose the reconstructed multiple strands to identify smooth local subspace. In the process of projection from a high dimension to a low dimension, a new weight matrix is put forward to achieve a better denoising effect. Afterwards, permutation entropy (PE) is applied in the detection of time sequence randomness and dynamic mutation behavior, which can effectively detect and amplify the variation of vibration signals. Hence PE can characterize the working conditions of gear and bearing under different conditions. The experimental results illustrate the effectiveness and superiority of the proposed approach. The theoretical derivations, numerical simulations and experimental studies, all confirm that the proposed approach based on the smooth local subspace projection method and PE, is promising in the field of the fault classification of rotary machinery.

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“…The published article [1] has been retracted at the request of the authors. The co-authors were not aware that the article had been submitted for publication and do not agree to the publication of the paper in its current form.…”

mentioning

confidence: 99%

“…Applied Sciences is a member of the Committee on Publication Ethics (COPE) and strives to uphold the highest ethical standards. The published article [1] is retracted and shall be marked accordingly.…”

mentioning

confidence: 99%

RETRACTED: Yuan et al. Laplacian Eigenmaps Feature Conversion and Particle Swarm Optimization-Based Deep Neural Network for Machine Condition Monitoring. Appl. Sci. 2018, 8, 2611

et al. 2020

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

Cited by 6 publications

References 26 publications

A Stochastic Deterioration Process Based Approach for Micro Switches Remaining Useful Life Estimation

A Stochastic Deterioration Process Based Approach for Micro Switches Remaining Useful Life Estimation

Fault Classification of Rotary Machinery Based on Smooth Local Subspace Projection Method and Permutation Entropy

RETRACTED: Yuan et al. 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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