Reliability analysis of thermal error model based on DBN and Monte Carlo method

2021 ◽  
Vol 146 ◽  
pp. 107020 ◽  
Author(s):  
Kuo Liu ◽  
Jiakun Wu ◽  
Haibo Liu ◽  
Mingjia Sun ◽  
Yongqing Wang
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Reliability Analysis ◽  
Thermal Error ◽  
Error Model ◽  
Model Based ◽  
Thermal Error Model

scholarly journals Thermal Error Prediction of Ball Screws Based on PSO-LSTM

2021 ◽  
Author(s):  
Xiangsheng Gao ◽  
Yueyang Guo ◽  
Dzonu Ambrose Hanson ◽  
Zhihao Liu ◽  
Min Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
High Speed ◽  
Thermal Characteristic ◽  
Thermal Error ◽  
Error Model ◽  
Cnc Machine Tools ◽  
Error Prediction ◽  
Model Based ◽  
Thermal Error Model

Abstract Thermal error of ball screws seriously affects the machining precision of CNC machine tools especially in high speed and precision machining. Compensation technology is one of the most effective methods to address the thermal issue, and the effect of compensation depends on the accuracy and robustness of the thermal error model. Traditional modeling approaches have major challenges in time-series thermal error prediction. In this paper, a novel thermal error model based on Long Short-Term Memory (LSTM) neural network and Particle Swarm Optimization (PSO) algorithm is proposed. A data-driven model based on LSTM neural network is established according to the time-series collected data. The hyperparameters of LSTM neural network are optimized by PSO and then a PSO-LSTM model is established to precisely predict the thermal error of ball screws. In order to verify the effectiveness and robustness of the proposed model, two thermal characteristic experiments based on step and random speed are conducted on a self-designed test bench. The results show that the PSO-LSTM model has higher accuracy compared with the RBF model and BP model with high robustness. The proposed method can be implemented to predict the thermal error of ball screws, and provide a foundation for thermal error compensation.

Reliability Analysis of Embankment Slope in Yaan-Luku Expressway

2011 ◽  
Vol 71-78 ◽  
pp. 1360-1365
Author(s):  
Jian Quan Ma ◽  
Guang Jie Li ◽  
Shi Bo Li ◽  
Pei Hua Xu
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Reliability Analysis ◽  
Failure Probability ◽  
Latin Hypercube Sampling ◽  
Natural State ◽  
State Function ◽  
Embankment Slope ◽  
The Times ◽  
Distribution Type

Take a typical cross-section of rockfill embankment slope in Yaan-Luku highway as the research object, reliability analysis is studied under the condition of water table of 840.85m, 851.50m, and loading condition of natural state and horizontal seismic acceleration of 0.2g, respectively. Raw data use Kolmogorov-Smirnov test (K-S test) to determine the distribution type of parametric variation. And the parameters were sampling with Latin hypercube sampling (LHS) method and Monte Carlo (MC) method, respectively, to obtain state function and determine safety factors and reliability indexes. A conclusion is drawn that the times of simulation based on LHS method were less than Monte Carlo method. Also the convergence of failure probability is better than the Monte Carlo method. The safety factor is greater than one and the failure probability has reached to 35.45% in condition of earthquake, which indicating that the instability of rockfill embankment slope is still possible.

Uncertainty metric in model-based eddy current inversion using the adaptive Monte Carlo method

Measurement ◽  
2019 ◽  
Vol 137 ◽  
pp. 323-331 ◽  
Author(s):  
Mengbao Fan ◽  
Genlong Wu ◽  
Binghua Cao ◽  
Thomson Sarkodie-Gyan ◽  
Zhixiong Li ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Eddy Current ◽  
Adaptive Monte Carlo ◽  
Model Based ◽  
Adaptive Monte Carlo Method ◽  
Eddy Current Inversion ◽  
Current Inversion

Reliability Analysis on Yuwangbian Slop Based on Monte-Carlo Simulation

2011 ◽  
Vol 250-253 ◽  
pp. 3934-3940
Author(s):  
Yi Fang Feng ◽  
Hua Zhi Zhang ◽  
Yu Wang ◽  
Qing Jun Zuo
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Reliability Analysis ◽  
Working Condition ◽  
Actual Condition ◽  
Stability Factor ◽  
Analysis Method ◽  
Loess Slope ◽  
The Monte Carlo Method ◽  
The Stability

Based on the Yuwangbian high loess slope, which is located in Xi'an Yanta District, the basic principle of Monte-Carlo method is presented. By means of geotechnical engineering and geotechnical environment emulation software Geostudio-slope/w and based on Morgenstern-Price slope stability analysis method, the reliability and stability of the slope are analyzed under different kinds of working condition. The stability factor, reliability index and failure probability under the corresponding working conditions has been obtained. The results coincide with the actual condition, which makes the Geostudio software combine with the Monte-Carlo method and provides reference for the reliability analysis of loess slope.

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