Gaussian process regression for tool wear prediction

Kong, Dongdong; Chen, Yongjie; Li, Ning

doi:10.1016/j.ymssp.2017.11.021

Cited by 234 publications

(94 citation statements)

References 24 publications

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“…A Gaussian process (GP) is an infinite group of random variables of which any of the finite subsets has a constant joint Gaussian distribution [24][25][26]. A GP is represented by a mean function and a covariance function.…”

Section: Gaussian Process Regressionmentioning

confidence: 99%

Investigating the Predictive Performance of Gaussian Process Regression in Evaluating Reservoir Porosity and Permeability

et al. 2018

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In this paper, a new predictive model based on Gaussian process regression (GPR) that does not require iterative tuning of user-defined model parameters has been proposed to determine reservoir porosity and permeability. For this purpose, the capability of GPR was appraised statistically for predicting porosity and permeability of the southern basin of the South Yellow Sea using petrophysical well log data. Generally, the performance of GPR is deeply reliant on the type covariance function utilized. Therefore, to obtain the optimal GPR model, five different kernel functions were tested. The resulting optimal GPR model consisted of the exponential covariance function, which produced the highest correlation coefficient (R) of 0.85 and the least root mean square error (RMSE) of 0.037 and 6.47 for porosity and permeability, respectively. Comparison was further made with benchmark methods involving a back propagation neural network (BPNN), generalized regression neural network (GRNN), and radial basis function neural network (RBFNN). The statistical findings revealed that the proposed GPR is a powerful technique and can be used as a supplement to the widely used artificial neural network methods. In terms of computational speed, the GPR technique was computationally faster than the BPNN, GRNN, and RBFNN methods in estimating reservoir porosity and permeability.

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Section: Gaussian Process Regressionmentioning

confidence: 99%

Investigating the Predictive Performance of Gaussian Process Regression in Evaluating Reservoir Porosity and Permeability

et al. 2018

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“…The GPR is a set of infinitely random variants in which any finite subset has a common Gaussian distribution. The Gaussian multivariate distribution can be explained as a natural extension of functions, that is, the mean vector is infinitely long and the covariance matrix has infinite size [25]. The vector n x indicates a specific position in the input field, and…”

Section: Gaussian Process Regressionmentioning

confidence: 99%

Prediction of wear loss quantities of ferro-alloy coating using different machine learning algorithms

et al. 2019

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In this study, experimental wear losses under different loads and sliding distances of AISI 1020 steel surfaces coated with (wt.%) 50FeCrC-20FeW-30FeB and 70FeCrC-30FeB powder mixtures by plasma transfer arc welding were determined. The dataset comprised 99 different wear amount measurements obtained experimentally in the laboratory. The linear regression (LR), support vector machine (SVM), and Gaussian process regression (GPR) algorithms are used for predicting wear quantities. A success rate of 0.93 was obtained from the LR algorithm and 0.96 from the SVM and GPR algorithms.

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“…The GP method combined with a numerical weather prediction model was used for wind speed prediction up to 72 h ahead in Reference [38]. The GP method performed better than ANN and support vector machine (SVM) in tool wear prediction in Reference [39]. In this paper, the GP method is used in RUL prediction of wind turbine bearings.…”

Section: The Basic Theory Of Gaussian Processmentioning

confidence: 99%

Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions

et al. 2018

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Wind-powered electricity generation has grown significantly over the past decade. While there are many components that might impact their useful life, the gearbox and generator bearings are among the most fragile components in wind turbines. Therefore, the prediction of remaining useful life (RUL) of faulty or damaged wind turbine bearings will provide useful support for reliability evaluation and advanced maintenance of wind turbines. This paper proposes a data-driven method combining the interval whitenization method with a Gaussian process (GP) algorithm in order to predict the RUL of wind turbine generator bearings. Firstly, a wavelet packet transform is used to eliminate noise in the vibration signals and extract the characteristic fault signals. A comprehensive analysis of the real degradation process is used to determine the indicators of degradation. The interval whitenization method is proposed to reduce the interference of non-stationary operating conditions to improve the quality of health indicators. Finally, the GP method is utilized to construct the model which reflects the relationship between the RUL and health indicators. The method is assessed using actual vibration datasets from two wind turbines. The prediction results demonstrate that the proposed method can reduce the effect of non-stationary operating conditions. In addition, compared with the support vector regression (SVR) method and artificial neural network (ANN), the prediction accuracy of the proposed method has an improvement of more than 65.8%. The prediction results verify the effectiveness and superiority of the proposed method.

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Gaussian process regression for tool wear prediction

Cited by 234 publications

References 24 publications

Investigating the Predictive Performance of Gaussian Process Regression in Evaluating Reservoir Porosity and Permeability

Investigating the Predictive Performance of Gaussian Process Regression in Evaluating Reservoir Porosity and Permeability

Prediction of wear loss quantities of ferro-alloy coating using different machine learning algorithms

Prediction of Remaining Useful Life of Wind Turbine Bearings under Non-Stationary Operating Conditions

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