Estimation of random vibration signals with small samples using bootstrap maximum entropy method

Wang, Yanqing; Zhou, Weihu; Dong, Decun; Wang, Zhongyu

doi:10.1016/j.measurement.2017.04.003

Cited by 9 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the existing data-analysis methods, the analysis result of using a single method often has one-sidedness in some aspects, so the evaluation results of PMR can be obtained more comprehensively by fusing several different methods. The bootstrap method can simulate the probability distribution of data samples through re-sampling [22][23][24][25], which can separate the systematic errors in dynamic evaluation process by using the nuclear concept, but it needs to take advantage of the prior information of some rules of data sampling. The maximum entropy principle is used to calculate the probability distribution of data samples while making the subjective estimation error minimum [17,24,26,27].…”

Section: Introductionmentioning

confidence: 99%

“…The bootstrap method can simulate the probability distribution of data samples through re-sampling [22][23][24][25], which can separate the systematic errors in dynamic evaluation process by using the nuclear concept, but it needs to take advantage of the prior information of some rules of data sampling. The maximum entropy principle is used to calculate the probability distribution of data samples while making the subjective estimation error minimum [17,24,26,27]. Bayesian theory fully combines the prior information with the current sample information to obtain the posterior sample information [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Model for Evaluating the Operation Performance Status of Rolling Bearings Based on Hierarchical Maximum Entropy Bayesian Method

Liang

Deng

et al. 2022

Lubricants

View full text Add to dashboard Cite

Information such as probability distribution, performance degradation trajectory, and performance reliability function varies with the service status of rolling bearings, which is difficult to analyze and evaluate using traditional reliability theory. Adding equipment operation status to evaluate the bearing operation performance status has become the focus of current research to ensure the effective maintenance of the system, reduce faults, and improve quality under the condition of traditional probability statistics. So, a mathematical model is established by proposing the hierarchical maximum entropy Bayesian method (HMEBM), which is used to evaluate the operation performance status of rolling bearings. When calculating the posterior probability density function (PPDF), the similarities between time series regarded as a weighting coefficient are calculated using overlapping area method, membership degree method, Hamming approach degree method, Euclidean approach degree method, and cardinal approach degree method. The experiment investigation shows that the variation degree of the optimal vibration performance status can be calculated more accurately for each time series relative to the intrinsic series.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Model for Evaluating the Operation Performance Status of Rolling Bearings Based on Hierarchical Maximum Entropy Bayesian Method

Liang

Deng

et al. 2022

Lubricants

View full text Add to dashboard Cite

show abstract

“…Manyi et al [13] derived the joint confidence interval by bootstrap method, regarded the uncertainty evaluation as a numerical analysis process, and obtained the uncertainty of the best fitting of the surface according to the central limit theorem. Yanqing et al [14] Creatively estimated the small sample frequency domain RVS by combining bootstrap and maximum entropy method and compared the grey theory with the traditional statistical method through the expected value and variance, with high accuracy and suitable for small sample estimation. This method has superiority.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty modeling and evaluation of profile measurement by structured light scanner

zhao¹,

Cheng²,

Xu³

et al. 2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

A structured light scanner is mainly used to collect three-dimensional (3D) data of an object; however, the uncertainty evaluation of a specific measurement task is not perfect. This study discusses the source of the uncertainty component of the measurement system of a structured blue-light scanner, including the instrument itself, data acquisition, data processing, and other factors. Using the surface contour as the evaluation object, Polyworks software was used to perform the data processing of the point cloud denoising, splicing, and registration. An uncertainty evaluation is performed based on the black-box evaluation model. The experimental results show that the uncertainty components introduced by the instrument indication error, point cloud stitching, and point cloud registration are the main components of the entire measurement system. The Bootstrap method does not need to consider the probability distribution type of the error sources, which improves computational efficiency. This method has several advantages when the distribution state is unknown.This work is conducive to improving the task-oriented uncertainty evaluation of 3D scanners.

show abstract

“…Commonly, the sample size is increased using certain methods to improve the results. The bootstrap algorithm [35] is a common resampling method for small sample set in chemometrics and its reliability has been demonstrated by many researchers [36][37][38] . We have previously investigated the feasibility of using the bootstrap method for the quantitative analysis of the maize moisture content at the grain-filling stage [39] and a similar study was conducted by other researchers [35] .…”

Section: Introductionmentioning

confidence: 99%

Monitoring model for predicting maize grain moisture at the filling stage using NIRS and a small sample size

Wang¹,

Yang²,

Song³

et al. 2019

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

The change in the maize moisture content during different growth stages is an important indicator to evaluate the growth status of maize. In particular, the moisture content during the grain-filling stage reflects the grain quality and maturity and it can also be used as an important indicator for breeding and seed selection. At present, the drying method is usually used to calculate the moisture content and the dehydration rate at the grain-filling stage, however, it requires large sample size and long test time. In order to monitor the change in the moisture content at the maize grain-filling stage using small sample set, the Bootstrap re-sampling strategy-sample set partitioning based on joint x-y distances-partial least squares (Bootstrap-SPXY-PLS) moisture content monitoring model and near-infrared spectroscopy for small sample sizes of 10, 20, and 50 were used. To improve the prediction accuracy of the model, the optimal number of factors of the model was determined and the comprehensive evaluation thresholds RVP (coefficient of determination (R 2), the root mean square error of cross-validation (RMSECV) and the root mean square error of prediction (RMSEP)) was proposed for sub-model screening. The model exhibited a good performance for predicting the moisture content of the maize grain at the filling stage for small sample set. For the sample sizes of 20 and 50, the R 2 values were greater than 0.99. The average deviations of the predicted and reference values of the model were 0.1078%, 0.057%, and 0.0918%, respectively. Therefore, the model was effective for monitoring the moisture content at the grain-filling stage for a small sample size. The method is also suitable for the quantitative analysis of different concentrations using near-infrared spectroscopy and small sample size.

show abstract

Estimation of random vibration signals with small samples using bootstrap maximum entropy method

Cited by 9 publications

References 22 publications

A Novel Model for Evaluating the Operation Performance Status of Rolling Bearings Based on Hierarchical Maximum Entropy Bayesian Method

A Novel Model for Evaluating the Operation Performance Status of Rolling Bearings Based on Hierarchical Maximum Entropy Bayesian Method

Uncertainty modeling and evaluation of profile measurement by structured light scanner

Monitoring model for predicting maize grain moisture at the filling stage using NIRS and a small sample size

Contact Info

Product

Resources

About