Factors influence the lubrication characteristics investigation and optimization of bearing based on neural network

He, Zhenpeng; Gong, Wenqin

doi:10.1108/ilt-07-2015-0093

Cited by 3 publications

(2 citation statements)

References 21 publications

(16 reference statements)

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“…The ANOVA shows that, compared with the six factors, the PCR of errors are small, 6.62% of P max and 2.70% of P f , which indicates the interactions hidden in error are very limited. It can be found the interactions are also neglected in literatures [17][18][19].…”

Section: Resultsmentioning

confidence: 99%

“…In practical engineering, this method uses the concept of orthogonal array to reduce the numbers of experiments, which facilitates to research multi-parameters concurrently and evaluate the effects of each parameter. Some studies [17][18][19] have already adopted Taguchi method to optimize surface textures for journal bearing to maximize load carrying capacity, minimize side leakage and friction loss.…”

Section: Introductionmentioning

confidence: 99%

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A multiobjective optimization of journal bearing with double parabolic profiles and groove textures under steady operating conditions

Liu

Zhong²,

et al. 2020

Mechanics & Industry

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The double parabolic profiles can help journal bearing to reduce bushing edge wear, but it also reduces load carrying capacity and increases friction loss. To overcome these drawbacks, in this study, a multiobjective optimization of journal bearing with double parabolic profiles and groove textures is researched under steady operating conditions using Taguchi and grey relational analysis methods. Firstly, a lubrication model with journal misalignment, elastic deformation, asperity contact, thermal effect is established and formation cause of drawbacks is illustrated. Then, an orthogonal test with considering six factors, i.e., groove number, groove depth, groove length, axial width of double parabolic profiles, radial height of double parabolic profiles and groove location is conducted, meanwhile the effects and significances of each factor on response variables are revealed. Finally, an optimal parameters combination of six factors is determined by grey relational analysis, which gives maximum load carrying capacity and minimum friction loss. Overall, this study may give guidance on journal bearing design to enhance its tribological performance.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multiobjective optimization of journal bearing with double parabolic profiles and groove textures under steady operating conditions

Liu

Zhong²,

et al. 2020

Mechanics & Industry

View full text Add to dashboard Cite

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Analysis on the lubrication performance of spiral-grooved journal bearing using CFD technique

Chen

Zhong

et al. 2021

J. Phys.: Conf. Ser.

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As the main element, the lubrication characteristics of spiral-grooved journal bearing would decide the stability of mechanical systems. And, a effectivee model will represent the hydrodynamic characteristics of spiral-grooved journal bearing effectively. Based on the CFD technique, this work presents the simulation methodology for analyze the effects of spiral-grooved parameters on the lubrication performance of journal bearing. Compared with the published data, the proposed method is demonstrated effectiveness. Moreover, the influences of eccentricity ratio, groove number, spiral angle, groove width and groove depth are discussed by the orthogonal design method, which provide a reference for the optimized design of spiral-grooved journal bearing.

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Intelligent fault diagnosis method of rolling bearing based on stacked denoising autoencoder and convolutional neural network

Che¹,

Wang²,

Ni³

et al. 2020

ILT

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Purpose The purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing. Design/methodology/approach The vibration signal data of rolling bearing has long time series and strong noise interference, which brings great difficulties for the accurate diagnosis of bearing faults. To solve those problems, an intelligent fault diagnosis model based on stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed in this paper. The SDAE is used to process the time series data with multiple dimensions and noise interference. Then the dimension-reduced samples can be put into CNN model, and the fault classification results can be obtained by convolution and pooling operations of hidden layers in CNN. Findings The effectiveness of the proposed method is validated through experimental verification and comparative experimental analysis. The results demonstrate that the proposed model can achieve an average classification accuracy of 96.5% under three noise levels, which is 3-13% higher than the traditional models and single deep-learning models. Originality/value The combined SDAE–CNN model proposed in this paper can denoise and reduce dimensions of raw vibration signal data, and extract the in-depth features in image samples of rolling bearing. Consequently, the proposed model has more accurate fault diagnosis results for the rolling bearing vibration signal data with long time series and noise interference. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2019-0496/

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Factors influence the lubrication characteristics investigation and optimization of bearing based on neural network

Cited by 3 publications

References 21 publications

A multiobjective optimization of journal bearing with double parabolic profiles and groove textures under steady operating conditions

A multiobjective optimization of journal bearing with double parabolic profiles and groove textures under steady operating conditions

Analysis on the lubrication performance of spiral-grooved journal bearing using CFD technique

Intelligent fault diagnosis method of rolling bearing based on stacked denoising autoencoder and convolutional neural network

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