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Simulation and analysis of grinding wheel based on Gaussian mixture model
Yulun CHI, Haolin LI
《机械工程前沿(英文)》 2012年 第7卷 第4期 页码 427-432 doi: 10.1007/s11465-012-0350-3
This article presents an application of numerical simulation technique for the generation and analysis of the grinding wheel surface topographies. The ZETA 20 imaging and metrology microscope is employed to measure the surface topographies. The Gaussian mixture model (GMM) is used to transform the measured non-Gaussian field to Gaussian fields, and the simulated topographies are generated. Some numerical examples are used to illustrate the viability of the method. It shows that the simulated grinding wheel topographies are similar with the measured and can be effective used to study the abrasive grains and grinding mechanism.
关键词: grinding wheel 3D topographies measurement Gaussian mixture model simulation
Xin Peng, Yang Tang, Wenli Du, Feng Qian
《化学科学与工程前沿(英文)》 2017年 第11卷 第3期 页码 429-439 doi: 10.1007/s11705-017-1675-6
关键词: non-Gaussian processes subspace projection independent component analysis locality preserving projection finite mixture model
An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings
Yanfeng PENG, Junsheng CHENG, Yanfei LIU, Xuejun LI, Zhihua PENG
《机械工程前沿(英文)》 2018年 第13卷 第2期 页码 301-310 doi: 10.1007/s11465-017-0449-7
A novel data-driven method based on Gaussian mixture model (GMM) and distance evaluation technique (DET) is proposed to predict the remaining useful life (RUL) of rolling bearings. The data sets are clustered by GMM to divide all data sets into several health states adaptively and reasonably. The number of clusters is determined by the minimum description length principle. Thus, either the health state of the data sets or the number of the states is obtained automatically. Meanwhile, the abnormal data sets can be recognized during the clustering process and removed from the training data sets. After obtaining the health states, appropriate features are selected by DET for increasing the classification and prediction accuracy. In the prediction process, each vibration signal is decomposed into several components by empirical mode decomposition. Some common statistical parameters of the components are calculated first and then the features are clustered using GMM to divide the data sets into several health states and remove the abnormal data sets. Thereafter, appropriate statistical parameters of the generated components are selected using DET. Finally, least squares support vector machine is utilized to predict the RUL of rolling bearings. Experimental results indicate that the proposed method reliably predicts the RUL of rolling bearings.
关键词: Gaussian mixture model distance evaluation technique health state remaining useful life rolling bearing
《能源前沿(英文)》 doi: 10.1007/s11708-023-0906-4
关键词: lithium-ion batteries RUL prediction double exponential model neural network Gaussian process regression (GPR)
Simulation of abrasive flow machining process for 2D and 3D mixture models
Rupalika DASH,Kalipada MAITY
《机械工程前沿(英文)》 2015年 第10卷 第4期 页码 424-432 doi: 10.1007/s11465-015-0366-6
Improvement of surface finish and material removal has been quite a challenge in a finishing operation such as abrasive flow machining (AFM). Factors that affect the surface finish and material removal are media viscosity, extrusion pressure, piston velocity, and particle size in abrasive flow machining process. Performing experiments for all the parameters and accurately obtaining an optimized parameter in a short time are difficult to accomplish because the operation requires a precise finish. Computational fluid dynamics (CFD) simulation was employed to accurately determine optimum parameters. In the current work, a 2D model was designed, and the flow analysis, force calculation, and material removal prediction were performed and compared with the available experimental data. Another 3D model for a swaging die finishing using AFM was simulated at different viscosities of the media to study the effects on the controlling parameters. A CFD simulation was performed by using commercially available ANSYS FLUENT. Two phases were considered for the flow analysis, and multiphase mixture model was taken into account. The fluid was considered to be a Newtonian fluid and the flow laminar with no wall slip.
关键词: abrasive flow machining (AFM) computational fluid dynamics (CFD) modeling mixture model
融合显著性模型和高斯网模型的视网膜血管分割方法 Research Articles
Lan-yan XUE, Jia-wen LIN, Xin-rong CAO, Shao-hua ZHENG, Lun YU
《信息与电子工程前沿(英文)》 2019年 第20卷 第8期 页码 1075-1086 doi: 10.1631/FITEE.1700404
近似高斯共轭:非线性、多模态、不确定以及约束下的参数递归滤波等 Review
Tian-cheng LI, Jin-ya SU, Wei LIU, Juan M. CORCHADO
《信息与电子工程前沿(英文)》 2017年 第18卷 第12期 页码 1913-1939 doi: 10.1631/FITEE.1700379
基于双层多目标分割的超高速撞击航天器损伤红外检测算法 Research Article
杨晓1,殷春1,Sara DADRAS2,雷光钰1,谭旭彤1,邱根1
《信息与电子工程前沿(英文)》 2022年 第23卷 第4期 页码 571-586 doi: 10.1631/FITEE.2000695
一种基于高斯过程与粒子群算法的CNN超参数自动搜索混合模型优化算法 Research Article
闫涵,仲崇权,吴玉虎,张立勇,卢伟
《信息与电子工程前沿(英文)》 2023年 第24卷 第11期 页码 1557-1573 doi: 10.1631/FITEE.2200515
Ke GUO, Xia-bi LIU, Lun-hao GUO, Zong-jie LI, Zeng-min GENG
《信息与电子工程前沿(英文)》 2018年 第19卷 第5期 页码 639-650 doi: 10.1631/FITEE.1700007
A study on quality evaluation for bituminous mixture using X-ray CT
Satoshi TANIGUCHI, Keiichiro OGAWA, Jun OTANI, Itaru NISHIZAKI
《结构与土木工程前沿(英文)》 2013年 第7卷 第2期 页码 89-101 doi: 10.1007/s11709-013-0197-7
关键词: asphalt concrete mixture aggregate bitumen bitumen content quality evaluation X-ray CT
基于混合驱动高斯过程学习的强机动多目标跟踪方法 Research Article
国强1,滕龙1,2,尹天祥3,郭云飞3,吴新良2,宋文明2
《信息与电子工程前沿(英文)》 2023年 第24卷 第11期 页码 1647-1656 doi: 10.1631/FITEE.2300348
Pijush Samui, Jagan J
《结构与土木工程前沿(英文)》 2013年 第7卷 第2期 页码 133-136 doi: 10.1007/s11709-013-0202-1
关键词: unsaturated soil effective stress parameter Gaussian process regression (GPR) artificial neural network (ANN) variance
Hao QIN, Shenwei ZHANG, Wenxing ZHOU
《结构与土木工程前沿(英文)》 2013年 第7卷 第3期 页码 276-287 doi: 10.1007/s11709-013-0207-9
关键词: pipeline metal-loss corrosion inverse Gaussian process measurement error hierarchical Bayesian Markov Chain Monte Carlo (MCMC)
Fatigue of asphalt binder, mastic and mixture at low temperature
Dong WANG, Linbing WANG, Guoqing ZHOU
《结构与土木工程前沿(英文)》 2012年 第6卷 第2期 页码 166-175 doi: 10.1007/s11709-012-0157-7
关键词: fatigue asphalt mixture asphalt binder mastic finite element method (FEM) X-ray tomography
标题 作者 时间 类型 操作
Performance monitoring of non-gaussian chemical processes with modes-switching using globality-locality
Xin Peng, Yang Tang, Wenli Du, Feng Qian
期刊论文
An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings
Yanfeng PENG, Junsheng CHENG, Yanfei LIU, Xuejun LI, Zhihua PENG
期刊论文
prediction method for remaining useful life of lithium-ion batteries based on a neural network and Gaussian
期刊论文
Simulation of abrasive flow machining process for 2D and 3D mixture models
Rupalika DASH,Kalipada MAITY
期刊论文
A study on quality evaluation for bituminous mixture using X-ray CT
Satoshi TANIGUCHI, Keiichiro OGAWA, Jun OTANI, Itaru NISHIZAKI
期刊论文
Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach
Pijush Samui, Jagan J
期刊论文
Inverse Gaussian process-based corrosion growth modeling and its application in the reliability analysis
Hao QIN, Shenwei ZHANG, Wenxing ZHOU
期刊论文