Degradation State Recognition of Piston Pump Based on ICEEMDAN and XGBoost

Guo, Rui; Zhao, Zhiqian; Wang, Tao; Liu, Guangheng; Zhao, Jun; Gao, Dianrong

doi:10.3390/app10186593

Cited by 43 publications

(12 citation statements)

References 49 publications

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“…Two main advantages of this algorithm are that the XGBoost model has strong generalization ability and provides further insight into feature importance. For one thing, the strong generalization ability of the XGBoost model is attributed to the design idea mentioned and the hyperparameters used for avoiding overfitting, including general parameters, learning task parameters, and booster parameters . For another, the XGBoost model provides further insight into the importance of the input features that affected the water flux for users with three methods including “Gain”, “Weight” and “Coverage”.…”

Section: Resultsmentioning

confidence: 99%

“…For one thing, the strong generalization ability of the XGBoost model is attributed to the design idea mentioned and the hyperparameters used for avoiding overfitting, including general parameters, learning task parameters, and booster parameters. 29 For another, the XGBoost model provides further insight into the importance of the input features that affected the water flux for users with three methods including "Gain", "Weight" and "Coverage". However, the inconsistencies in results calculated from the above three methods could make users confused.…”

Section: Prediction Of Permeate Flux Using the Xgboost Modelmentioning

confidence: 99%

See 1 more Smart Citation

Modeling and Evaluation of the Permeate Flux in Forward Osmosis Process with Machine Learning

Shi

et al. 2022

Ind. Eng. Chem. Res.

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Predicting the permeate flux is critical for evaluating and optimizing the performance of the forward osmosis (FO) process. However, the solution diffusion models have poor applicability in accessing the FO process. Recently, the data-driven eXtreme Gradient Boosting (XGBoost) algorithm has been proven to be effective in processing structure data in engineering problems and has not been utilized to assess the FO process. Herein, a combination of the XGBoost model with a genetic algorithm (GA) was first proposed to predict the permeate flux, highlighting its superiority in the FO process through comparison of the support vector regression (SVR) model, the artificial neural network (ANN), and the multiple linear regression (MLR). Moreover, the performance of these models was optimized by tuning hyperparameters with a genetic algorithm (GA) and compared via Taylor Diagram. Among these machine learning (ML) models, the GA-based XGBoost model is superior to the other three models in terms of mean square error (MSE, 2.7326) and coefficient of determination (R 2, 0.9721) on the test data, and its prediction power was compared to that of the solution diffusion (SD) model in the literature. Finally, further insight into the feature importance that affects the permeate flux in the FO process was examined by utilizing the SHapley Additive exPlanations (SHAP) to estimate the contribution value of various variables. The results demonstrated that the XGBoost model could predict the permeate flux in the FO system with high accuracy and good generalization ability for the given data set and even on the unseen data. Furthermore, the findings of the SHAP method show that the osmotic pressure difference, the osmotic pressure difference of draw solution and FS solution, the crossflow velocity of the feed solution and draw solution, and the water permeability coefficient have a significant impact on water flux.

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Section: Resultsmentioning

confidence: 99%

Section: Prediction Of Permeate Flux Using the Xgboost Modelmentioning

confidence: 99%

Modeling and Evaluation of the Permeate Flux in Forward Osmosis Process with Machine Learning

Shi

et al. 2022

Ind. Eng. Chem. Res.

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“…This method subtracts the mean value of the residuals in this iteration from the residuals calculated in the previous iteration. It defines the result as the IMF component generated in each iteration [42]. The ICEEMDAN method effectively reduces the influence of residual noise in the original algorithm and optimizes the defect that false modal components are prone to appear in the decomposition process.…”

Section: Iceemdan Algorithm Principlementioning

confidence: 99%

A Cutting Pattern Recognition Method for Shearers Based on ICEEMDAN and Improved Grey Wolf Optimizer Algorithm-Optimized SVM

2021

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When the shearer is cutting, the sound signal generated by the cutting drum crushing coal and rock contains a wealth of cutting status information. In order to effectively process the shearer cutting sound signal and accurately identify the cutting mode, this paper proposed a shearer cutting sound signal recognition method based on an improved complete ensemble empirical mode decomposition with adaptive noise (ICCEMDAN) and an improved grey wolf optimizer (IGWO) algorithm-optimized support vector machine (SVM). First, the approach applied ICEEMDAN to process the cutting sound signal and obtained several intrinsic mode function (IMF) components. It used the correlation coefficient to select the characteristic component. Meanwhile, this paper calculated the composite multi-scale permutation entropy (CMPE) of the characteristic components as the eigenvalue. Then, the method introduced a differential evolution algorithm and nonlinear convergence factor to improve the GWO algorithm. It used the improved GWO algorithm to realize the adaptive selection of SVM parameters and established a cutting sound signal recognition model. According to the proportioning plan, the paper made several simulation coal walls for cutting experiments and collected cutting sound signals for cutting pattern recognition. The experimental results show that the method proposed in this paper can effectively process the cutting sound signal of the shearer, and the average accuracy of the cutting pattern recognition model reached 97.67%.

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“…Under different degradation conditions, the complexity of natural oscillation of the piston pump will change, as stated by the authors of [8]. Given the difference in the characteristic values of the vibration signal under different degradation states, this paper presented a degradation state recognition method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and extreme gradient boosting (XGBoost) to improve the accuracy of state recognition.…”

Section: Review Of Issue Contentsmentioning

confidence: 99%