scholarly journals Life prediction of heavy-load self-lubricating liners

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199215
Author(s):  
Xiuhong Hao ◽  
Shuqiang Wang ◽  
Panqiang Huo ◽  
Deng Pan
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Service Life ◽  
Small Sample Size ◽  
Small Sample ◽  
Support Vector ◽  
Wear Depth ◽  
Distribution Models ◽  
Heavy Load ◽  
Transform Method

To address the issues of long testing periods and small sample sizes while evaluating the service life of heavy-load self-lubricating liners, we propose a succinct method based on Monte Carlo simulation that is significantly fast and requires a small sample size. First, the support vector regression algorithm was applied to fit the degradation trajectories of the wear depth, and the first and second characteristic parameter vectors of the wear depth as well as the corresponding distribution models were obtained. Next, sample expansion was performed using Monte Carlo simulation and the inverse transform method. Finally, based on the failure criterion of the self-lubricating liner, the service lives and distribution models of the expanded samples were obtained; subsequently, the corresponding reliability life indices were provided. Our results indicate that when the expanded sample was large enough, the proposed prediction method exhibited a relatively high prediction accuracy. Therefore, these results provide theoretical support for shortening the testing cycle used to evaluate the service life of self-lubricating liners and for accelerating the research and development of self-lubricating spherical plain bearing products.

Design Curve Construction Based on Monte Carlo Simulation

2013 ◽  
Author(s):  
Zhigang Wei ◽  
Limin Luo ◽  
Burt Lin ◽  
Dmitri Konson ◽  
Kamran Nikbin
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Sample Size ◽  
Test Data ◽  
Confidence Level ◽  
Small Sample Size ◽  
Test Sample ◽  
Small Sample ◽  
Tolerance Limit ◽  
Design Curve

Good durability/reliability performance of products can be achieved by properly constructing and implementing design curves, which are usually obtained by analyzing test data, such as fatigue S-N data. A good design curve construction approach should consider sample size, failure probability and confidence level, and these features are especially critical when test sample size is small. The authors have developed a design S-N curve construction method based on the tolerance limit concept. However, recent studies have shown that the analytical solutions based on the tolerance limit approach may not be accurate for very small sample size because of the assumptions and approximations introduced to the analytical approach. In this paper a Monte Carlo simulation approach is used to construct design curves for test data with an assumed underlining normal (or lognormal) distribution. The difference of factor K, which measures the confidence level of the test data, between the analytical solution and the Monte Carlo simulation solutions is compared. Finally, the design curves constructed based on these methods are demonstrated and compared using fatigue S-N data with small sample size.

scholarly journals New findings on protected and strictly protected species confirm the value of the prime hoverfly area network

2018 ◽  
pp. 63-71
Author(s):  
Marina Jankovic ◽  
Marija Milicic ◽  
Dimitrije Radisic ◽  
Dubravka Milic ◽  
Ante Vujic
Keyword(s):  
Protected Areas ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Small Sample Size ◽  
Small Sample ◽  
Area Network ◽  
Distribution Models ◽  
Environmental Pressures ◽  
New Findings ◽  
Using Data

With environmental pressures on the rise, the establishment of pro?tected areas is a key strategy for preserving biodiversity. The fact that many species are losing their battle against extinction despite being within protected areas raises the question of their effectiveness. The aim of this study was to evaluate established Priority Hoverfly Areas (PHAs) and areas that are not yet but could potentially be included in the PHA network, using data from new field surveys. Additionally, species distribution models have been created for two new species recognized as important and added to the list of key hoverfly species. Maps of potential distribution of these species were superimposed on maps of protected areas and PHAs to quantify percentages of overlap. The results of this study are not statisti?cally significant, which could be influenced by a small sample size. However, the results of species distribution models and the extent of overlap with PHAs confirm the utility of these expert-generated designations.

Probability Methods for Detecting Randomness in Small Sample Spatial Distributions

1994 ◽  
Vol 78 (3) ◽  
pp. 715-720 ◽  
Author(s):  
Frank O'brien
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Statistical Methods ◽  
Two Dimensions ◽  
Small Sample ◽  
Spatial Distributions ◽  
Theoretical Assumptions ◽  
Simulation Results ◽  
Probability Methods ◽  
Spatial Randomness

Several probability and statistical methods are discussed for detecting spatial randomness in two dimensions. One method is derived and proposed for its ease of application. Monte Carlo simulation results are presented in support of the theoretical assumptions of the proposed method.

scholarly journals Optimization Performance Comparison of Three Different Group Intelligence Algorithms on a SVM for Hyperspectral Imagery Classification

2019 ◽  
Vol 11 (6) ◽  
pp. 734 ◽  
Author(s):  
Xiufang Zhu ◽  
Nan Li ◽  
Yaozhong Pan
Keyword(s):  
Convergence Rate ◽  
Sample Size ◽  
Parameter Optimization ◽  
Classification Accuracy ◽  
Small Sample Size ◽  
Original Data ◽  
Performance Comparison ◽  
Small Sample ◽  
Support Vector ◽  
Remote Sensing Classification

Group intelligence algorithms have been widely used in support vector machine (SVM) parameter optimization due to their obvious characteristics of strong parallel processing ability, fast optimization, and global optimization. However, few studies have made optimization performance comparisons of different group intelligence algorithms on SVMs, especially in terms of their application to hyperspectral remote sensing classification. In this paper, we compare the optimization performance of three different group intelligence algorithms that were run on a SVM in terms of five aspects by using three hyperspectral images (one each of the Indian Pines, University of Pavia, and Salinas): the stability to parameter settings, convergence rate, feature selection ability, sample size, and classification accuracy. Particle swarm optimization (PSO), genetic algorithms (GAs), and artificial bee colony (ABC) algorithms are the three group intelligence algorithms. Our results showed the influence of these three optimization algorithms on the C-parameter optimization of the SVM was less than their influence on the σ-parameter. The convergence rate, the number of selected features, and the accuracy of the three group intelligence algorithms were statistically significant different at the p = 0.01 level. The GA algorithm could compress more than 70% of the original data and it was the least affected by sample size. GA-SVM had the highest average overall accuracy (91.77%), followed by ABC-SVM (88.73%), and PSO-SVM (86.65%). Especially, in complex scenes (e.g., the Indian Pines image), GA-SVM showed the highest classification accuracy (87.34%, which was 8.23% higher than ABC-SVM and 16.42% higher than PSO-SVM) and the best stability (the standard deviation of its classification accuracy was 0.82%, which was 5.54% lower than ABC-SVM, and 21.63% lower than PSO-SVM). Therefore, when compared with the ABC and PSO algorithms, the GA had more advantages in terms of feature band selection, small sample size classification, and classification accuracy.

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