Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

Cui, Hongjiang; Guan, Ying; Chen, Huayue

doi:10.1109/access.2021.3108972

Cited by 126 publications

(66 citation statements)

References 62 publications

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“…The simulation signal and actual vibration signal were selected to verify the effectiveness of the reported VMD-MCKD method. The experiment revealed that VMD-MCKD could effectively diagnose the rolling element faults with high accuracy [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Exploration of consumer preference based on deep learning neural network model in the immersive marketing environment

Zheng

Ding²

2022

PLoS ONE

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The study intends to increase the marketing quantity of various commodities and promote the comprehensive development of the market. The study first discusses the principle and current situation of the emerging Immersive Marketing. Then, it analyzes the Deep Learning (DL) Neural Network (NN) model. Finally, a Personalized Recommendation System (PRS) is designed based on the Immersive Marketing environment using the Graph Neural Network (GNN) model. The proposed PRS based on the Immersive Graph Neural Network (IGNN) model has reflected higher advantages over other recommendation systems. The experiment results suggest that Immersive Marketing can fully reflect commodities’ essential attributes and characteristics, improve users’ shopping experience, and promote sales. Meanwhile, the IGNN-based PRS reported here gives users an elevated and immersive shopping experience and entertainment process. Lastly, the model comparison finds that the proposed IGNN outperforms other models. The optimal model parameters are verified as P@20 and R@20 to gain the highest composite index values. In particular, parameter R@20 gives the model a better performance over P@20. The study provides technical references for improving the marketing process of various commodities and entertainment products and contributes to marketing technology development.

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Section: Introductionmentioning

confidence: 99%

Exploration of consumer preference based on deep learning neural network model in the immersive marketing environment

Zheng

Ding²

2022

PLoS ONE

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“… GI + CNN: The input is transformed to 2D greyscale pictures (GI), which are then fed into the MTL-CNN, which is based on the proposed CNN from [ 60 ]. VMD + MTL-CNN: To generate the multifusion input, each signal is decomposed into a sequence of intrinsic mode functions using variational mode decomposition and then channel wise joined [ 61 ]. Then, using the suggested MTL-CNN architecture, those series of intrinsic mode functions are fusioned channel wise for classification.…”

Section: Experimental Setup and Performance Analysismentioning

confidence: 99%

Bearing Fault Diagnosis Using Multidomain Fusion-Based Vibration Imaging and Multitask Learning

Hasan

Islam

2021

Sensors

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Statistical features extraction from bearing fault signals requires a substantial level of knowledge and domain expertise. Furthermore, existing feature extraction techniques are mostly confined to selective feature extraction methods namely, time-domain, frequency-domain, or time-frequency domain statistical parameters. Vibration signals of bearing fault are highly non-linear and non-stationary making it cumbersome to extract relevant information for existing methodologies. This process even became more complicated when the bearing operates at variable speeds and load conditions. To address these challenges, this study develops an autonomous diagnostic system that combines signal-to-image transformation techniques for multi-domain information with convolutional neural network (CNN)-aided multitask learning (MTL). To address variable operating conditions, a composite color image is created by fusing information from multi-domains, such as the raw time-domain signal, the spectrum of the time-domain signal, and the envelope spectrum of the time-frequency analysis. This 2-D composite image, named multi-domain fusion-based vibration imaging (MDFVI), is highly effective in generating a unique pattern even with variable speeds and loads. Following that, these MDFVI images are fed to the proposed MTL-based CNN architecture to identify faults in variable speed and health conditions concurrently. The proposed method is tested on two benchmark datasets from the bearing experiment. The experimental results suggested that the proposed method outperformed state-of-the-arts in both datasets.

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“…Therefore, the CACO algorithm takes on a global optimization ability. In addition, some new optimization algorithms have been proposed in recent years [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55].…”

Section: Chaotic Ant Colony Optimization Algorithmmentioning

confidence: 99%

Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

Cui

Guan

2021

Applied Sciences

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Aiming at the problems of poor decomposition quality and the extraction effect of a weak signal with strong noise by empirical mode decomposition (EMD), a novel fault diagnosis method based on cascaded adaptive second-order tristable stochastic resonance (CASTSR) and EMD is proposed in this paper. In the proposed method, low-frequency interference components are filtered by using high-pass filtering, and the restriction conditions of stochastic resonance theory are solved by using an ordinary variable-scale method. Then, a chaotic ant colony optimization algorithm with a global optimization ability is employed to adaptively adjust the parameters of the second-order tristable stochastic resonance system to obtain the optimal stochastic resonance, and noise reduction pretreatment technology based on CASTSR is developed to enhance the weak signal characteristics of low frequency. Next, the EMD is employed to decompose the denoising signal and extract the characteristic frequency from the intrinsic mode function (IMF), so as to realize the fault diagnosis of rolling bearings. Finally, the numerical simulation signal and actual bearing fault data are selected to prove the validity of the proposed method. The experiment results indicate that the proposed fault diagnosis method can enhance the decomposition quality of the EMD, effectively extract features of weak signals, and improve the accuracy of fault diagnosis. Therefore, the proposed fault diagnosis method is an effective fault diagnosis method for rotating machinery.

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Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

Cited by 126 publications

References 62 publications

Exploration of consumer preference based on deep learning neural network model in the immersive marketing environment

Exploration of consumer preference based on deep learning neural network model in the immersive marketing environment

Bearing Fault Diagnosis Using Multidomain Fusion-Based Vibration Imaging and Multitask Learning

Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

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