System operational reliability evaluation based on dynamic Bayesian network and XGBoost

Guo, Yan; Wang, Hongdong; Yu, Xiao‐Qi; Zhong, Mingjun; Li, Qing; Gao, Chao

doi:10.1016/j.ress.2022.108622

Cited by 26 publications

(7 citation statements)

References 34 publications

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“…At the same time, in order to verify the accuracy of the Iowa operator combination model, GM (1, 1) model [ 44 ], SVM model [ 45 , 46 ] (Parameters Optimization of SVM Using RBF Kernel Function, setting parameter γ =10.023, C =32.121), entropy weight method combination model [ 47 ], and XGBoost model [ 48 ] (Determination of parameters by grid search method learning_rate = 0.05, max_depth = 4, subsample = 0.9, min_child_weight = 2, gamma = 0.5, colsample_bytree = 0.6) are used for comparison and analysis. The accuracy indexes of different models are shown in Table 4 .…”

Section: Comparison and Analysis Of Prediction Results Of Each Modelsmentioning

confidence: 99%

“…rough comparison, it can be seen that the IOWA operator model is better than the Shapley combination model as a whole, the Shapley combination model is better than the combination prediction model of the error sum of squares reciprocal method, and it is better than the single models, with higher performance, accuracy, and reliability. All analyses show that the combination forecasting model based on the IOWA operator is the best model, followed by the combination At the same time, in order to verify the accuracy of the Iowa operator combination model, GM (1, 1) model [44], SVM model [45,46] (Parameters Optimization of SVM Using RBF Kernel Function, setting parameter c � 10.023, C � 32.121), entropy weight method combination model [47], and XGBoost model [48] (Determination of parameters by grid search method learning_rate � 0.05, max_depth � 4, subsample � 0.9, min_child_weight � 2, gamma � 0.5, colsample_bytree � 0.6) are used for comparison and analysis.…”

Section: Comparison and Analysis Of Prediction Results Of Each Modelsmentioning

confidence: 99%

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Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting

Zhang

2022

Computational Intelligence and Neuroscience

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Effective prediction of aircraft failure rate has important guiding significance for formulating reasonable maintenance plans, carrying out reliable maintenance activities, improving health management levels, and ensuring the safety of aircraft flight, etc. Firstly, combining the advantages of time series model in eliminating random accidental factors interference, grey model in dealing with poor information, and the characteristics of artificial neural network in dealing with nonlinear data, the failure rate of aircraft equipment is predicted by ARIMA model, grey Verhulst model, and BP neural network model, and secondly, based on the idea of variable weight, the method of sum of squares of errors is used to reciprocate. Shapley value method and IOWA operator method determine the weighting coefficient and establish three combined forecasting models for aircraft failure rate prediction, so as to improve the accuracy of the algorithm. Finally, taking the data of actual aircraft failure rate as the research object, the performance indexes of design prediction model are judged by Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Index of Agreement (IA), Theil Inequality Coefficient (TIC), Equal Coefficient (EC), Nash-Sutcliffe Efficiency coefficient (NSE), Pearson test, and violin diagram of forecast error distribution. The experimental results show that: The forecasting precision of the combination model is better than that of the single model, and the evaluation index of combination forecasting model based on IOWA operator is better than that of other combination forecasting models, thus improving the forecasting accuracy and reliability. Compared with other typical prediction models simultaneously, it is verified that the proposed combined prediction model has strong applicability, high accuracy, and good stability, which provides a practical and effective technical method for aircraft fault prediction and has good application value.

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Section: Comparison and Analysis Of Prediction Results Of Each Modelsmentioning

confidence: 99%

Section: Comparison and Analysis Of Prediction Results Of Each Modelsmentioning

confidence: 99%

Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting

Zhang

2022

Computational Intelligence and Neuroscience

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“…In this paper, the prepared data set was applied to different classifiers, and the classification effects of different classifiers on maximum discrimination features and other features were compared. The Accuracy,P recision,Recall, and F 1 − score index [25] were selected to evaluate the classification effect.…”

Section: ) Comparison Of Classifiersmentioning

confidence: 99%

Fingertip Detection Algorithm Based on Maximum Discrimination HOG Feature in Complex Background

Feng

et al. 2023

IEEE Access

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“…Reliability is defined as the probability of an NR component completing a predetermined function within a specified time and under specified conditions. 25 Accordingly, the reliability of the NR component can be calculated through the following expression:…”

Section: P-s -N Curve Based On the Logarithmic Strain Amplitudementioning

confidence: 99%

“…Reliability is defined as the probability of an NR component completing a predetermined function within a specified time and under specified conditions 25 . Accordingly, the reliability of the NR component can be calculated through the following expression:

P ((), N (|, S)) goodbreak= 1 goodbreak- \frac{1}{σ \sqrt{2 π}} \int_{‐ \infty}^{\ln N} \exp ([], goodbreak- \frac{{((), \ln N goodbreak- u)}^{2}}{2 σ^{2}}) d ((), \ln N),

Equation (6) indicates that accurate estimation of parameters in the log‐normal distribution model is the key to calculate the fatigue reliability of NR components.…”

Section: P–s–n Curve Based On the Logarithmic Strain Amplitudementioning

confidence: 99%

A unified probabilistic fatigue life prediction model for natural rubber components considering strain ratio effect

Liu

Zhao

Liu

2022

Fatigue Fract Eng Mat Struct

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The fatigue life of natural rubber (NR) components under a constant‐amplitude load is dispersed. The dispersion can be reflected by establishing a probabilistic fatigue life prediction model (also called the P–S–N curve). However, the effect of strain ratio on fatigue life is typically ignored in conventional P–S–N curves. Aiming at improving the prediction accuracy of conventional models, a unified P–S–N curve for NR components is proposed in the present study, in which the strain ratio effect is considered in the calculations. Using the dumbbell‐shaped rubber specimen fatigue test data, a polynomial function is proposed to describe the correlation between the equivalent strain amplitude, strain amplitude, and strain ratio. Then a calculation model of equivalent strain amplitude is established. Furthermore, a unified P–S–N curve for NR is established with equivalent strain amplitude as the damage parameter. By comparing the measured and predicted fatigue life of NR under different reliabilities, the accuracy of the established unified P–S–N curve is verified.

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System operational reliability evaluation based on dynamic Bayesian network and XGBoost

Cited by 26 publications

References 34 publications

Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting

Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting

Fingertip Detection Algorithm Based on Maximum Discrimination HOG Feature in Complex Background

A unified probabilistic fatigue life prediction model for natural rubber components considering strain ratio effect

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