Predicting running-in wear volume with a SVMR-based model under a small amount of training samples

Zhang, Gengpei; Wang, Jian; Chang, Suping

doi:10.1016/j.triboint.2018.07.045

Cited by 15 publications

(8 citation statements)

References 39 publications

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“…The combined error size and model evaluation findings shown in Table 2 indicate that the SVM is the best method to model the five surface parameters required in this paper. This is consistent with the research results of other authors [ 63 ]. Therefore, to ensure the consistency of the running-in process research, the SVM is employed for test modeling in this paper.…”

Section: Model Establishment and Testingsupporting

confidence: 94%

Optimization of running-in surface morphology parameters based on the AutoML model

Liu

Zhang

2021

PLoS ONE

Self Cite

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Running-in is an important and relatively complicated process. The surface morphology prior to running-in affects the surface morphology following the running-in process, which in turn influences the friction and wear characteristics of the workpiece. Therefore, the establishment of a model for running-in surface morphology prediction is important to investigate the process and optimize the surface design. Black-box models based on machine learning have robust complex object simulation performance. In this paper, five common machine learning methods are applied to establish running-in modeling performance based on surface morphology parameters. The support vector machine has the best model performance. The change law of the surface morphology parameters is obtained based on model testing, and the surface morphology optimization is explored. When better oil storage capacity is required, the recommendation is to increase the Sq, Sdq and Sk surface parameter values while setting medium Sdc and Sdr surface parameter values. When a lower coefficient of friction (COF) is required, Sdc and Sdr should be decreased, and Sq and Sdq should be increased. When better support performance is required, Sdc, Sdq, and Sdr should be increased. This article provides a solution to establish a link between surface design and functional performance in the steady wear stage, further filling the gap in quality monitoring of lifecycles.

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Section: Model Establishment and Testingsupporting

confidence: 94%

Optimization of running-in surface morphology parameters based on the AutoML model

Liu

Zhang

2021

PLoS ONE

Self Cite

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“…In the field of regression, the method is called SVRM. Regression machines with supporting vectors work for linear and nonlinear regression [26,27].…”

Section: The Li-ion Battery Rul Prediction Using Svmrmentioning

confidence: 99%

Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques

Andrioaia,

Gaitan,

Culea

et al. 2024

Computers

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Over the past decade, Unmanned Aerial Vehicles (UAVs) have begun to be increasingly used due to their untapped potential. Li-ion batteries are the most used to power electrically operated UAVs for their advantages, such as high energy density and the high number of operating cycles. Therefore, it is necessary to estimate the Remaining Useful Life (RUL) and the prediction of the Li-ion batteries’ capacity to prevent the UAVs’ loss of autonomy, which can cause accidents or material losses. In this paper, the authors propose a method of prediction of the RUL for Li-ion batteries using a data-driven approach. To maximize the performance of the process, the performance of three machine learning models, Support Vector Machine for Regression (SVMR), Multiple Linear Regression (MLR), and Random Forest (RF), were compared to estimate the RUL of Li-ion batteries. The method can be implemented within UAVs’ Predictive Maintenance (PdM) systems.

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“…In reference [31], the condition monitoring method for on-machine tool was proposed and the support vector regression (SVR) was used to predict the cutting tool of the flank wear. Zhang et al [32] used SVR to predict wear volume at the runningin, the optimization method was used to obtain the optimal results of different parameters. For predicting the wear rate, an ANN-SVR model was developed [33], and the results showed the proposed model has a superior performance than ANN model.…”

Section: Introductionmentioning

confidence: 99%

Wear assessment model for cylinder liner of internal combustion engine under fuzzy uncertainty

Kang

Lü

Luo

et al. 2021

Mechanics & Industry

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The wear of the piston ring-cylinder system is inevitable in the operation of the internal combustion engines (ICEs). If wear exceeds the maximum, the piston ring-cylinder system will be failure. A novel wear assessment model is proposed based on the support vector regression, and the fuzzy uncertainty is modeled to describe the random behavior under small sample. To verify the proposed model, the sample data of cylinder liner wear is applied. For best results, the particle swarm optimization (PSO) algorithm is used to optimize the model parameters. A back propagation neural network (BPNN) is employed to verify the effectiveness of the proposed model. The results show that the novel support vector regression has better prediction accuracy than other methods for cylinder wear in this paper, the proposed model can evaluate the cylinder liner wear of the ICEs effectively. The work provides a technical support for evaluating the service performance of the piston ring-cylinder liner and a reference for regular maintenance of the ships.

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Predicting running-in wear volume with a SVMR-based model under a small amount of training samples

Cited by 15 publications

References 39 publications

Optimization of running-in surface morphology parameters based on the AutoML model

Optimization of running-in surface morphology parameters based on the AutoML model

Predicting the RUL of Li-Ion Batteries in UAVs Using Machine Learning Techniques

Wear assessment model for cylinder liner of internal combustion engine under fuzzy uncertainty

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