Identifying trending model coefficients with an ensemble Kalman filter – a demonstration on a force model for milling

M. Schwenzer; G. Visconti; M. Ay; T. Bergs; M. Herty; D. Abel

首页> 外文期刊>IFAC PapersOnLine >Identifying trending model coefficients with an ensemble Kalman filter – a demonstration on a force model for milling

【24h】

Identifying trending model coefficients with an ensemble Kalman filter – a demonstration on a force model for milling

机译：用集合卡尔曼滤波器识别趋势模型系数 - 用于研磨力模型的演示

获取原文

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

This paper extends the ensemble Kalman filter (EnKF) for inverse problems to identify trending model coefficients. This is done by repeatedly inflating the ensemble while maintaining the mean of the particles. As a benchmark serves a classic EnKF and a recursive least squares (RLS). As an example serves the identification of a force model in milling, which changes due to the progression of tool wear. For a proper comparison, the true values are simulated and augmented with white Gaussian noise. The results demonstrate the feasibility of the approach for dynamic identification while still achieving good accuracy in the static case. Further, the inflated EnKF shows a remarkably insensitivity on the starting set but a less smooth convergence compared to the classic EnKF.

机译：本文扩展了Ensemble Kalman滤波器（ENKF），以识别趋势模型系数的逆问题。这是通过反复膨胀整体，同时保持粒子的平均值来完成。作为基准测试，作为经典ENKF和递归最小二乘（RLS）。作为示例，该示例用于识别研磨中的力模型，这是由于刀具磨损的进展而变化。为了适当的比较，真正的值是模拟和增强白色高斯噪声的。结果表明了动态识别方法的可行性，同时仍在静态案例中实现良好的准确性。此外，膨胀的ENKF在起始集上显示出显着的不敏感性，而是与经典ENKF相比的较小趋同的收敛性。

著录项

来源
《IFAC PapersOnLine》 |2020年第2期|共7页
作者
M. Schwenzer; G. Visconti; M. Ay; T. Bergs; M. Herty; D. Abel;
展开▼
作者单位

展开▼
收录信息
原文格式 PDF
正文语种
中图分类
关键词
Ensemble Kalman filterRecursive least squaresManufacturingMillingParameter identificationIdentificationTime-invariant identification;

机译：Ensemble Kalman Filtersrive最小二乘曼努法uplingmillingParameter IditionidificationIdutifiedTimity Iditiontime;

相似文献

外文文献
中文文献
专利

1. Ensemble Kalman filtering for force model identification in milling [J] . Max Schwenzer, Sebastian Stemmler, Muzaffer Ay, Procedia CIRP . 2019,第2期

机译：集成卡尔曼滤波在铣削力模型识别中的应用
2. Data assimilation in integrated hydrological modeling using ensemble Kalman filtering: evaluating the effect of ensemble size and localization on filter performance [J] . Rasmussen J., Madsen H., Jensen K. H., Hydrology and Earth System Sciences . 2015,第7期

机译：使用集合卡尔曼滤波的综合水文模型中的数据同化：评估集合大小和局部化对过滤器性能的影响
3. Data assimilation in integrated hydrological modeling using ensemble Kalman filtering: evaluating the effect of ensemble size and localization on filter performance [J] . Rasmussen J., Madsen H., Jensen K. H., Hydrology and Earth System Sciences . 2015,第7期

机译：使用集合卡尔曼滤波的综合水文模型中的数据同化：评估集合大小和局部化对滤波器性能的影响
4. Ensemble Kalman filtering for force model identification in milling [C] . Max Schwenzer, Sebastian Stemmler, Muzaffer Ay, CIRP Conference on Modelling of Machining Operations . 2019

机译：Ensemble Kalman滤波，用于铣削中的力模型识别
5. Hybrid - Nudging Ensemble Kalman Filter and Ensemble Adjustment Kalman Filter Approach to Subsurface Water Contaminant Transport Modeling. [D] . Hokey, Wisdom Mawuli. 2016

机译：混合-集成整体卡尔曼滤波和整体调整卡尔曼滤波方法在地下水污染物运移建模中的应用。
6. Ensemble Learning Approach via a Kalman Filtered Markov Model for a Passive Wearable Respiratory Monitor [O] . Sayandeep Acharya, William M. Mongan, Ilhaan Rasheed, -1

机译：通过卡尔曼滤波马尔可夫模型的被动式可穿戴式呼吸监测仪的集成学习方法
7. Data assimilation in integrated hydrological modeling using ensemble Kalman filtering: evaluating the effect of ensemble size and localization on filter performance [O] . J. Rasmussen, H. Madsen, K. H. Jensen, 2015

机译：使用集合卡尔曼滤波进行综合水文模拟的数据同化：评估集合大小和定位对滤波器性能的影响
8. Representer Method, the Ensemble Kalman Filter and the Ensemble Kalman Smoother: A Comparison Study Using a Nonlinear Reduced Gravity Ocean Model [R] . Ngodock, H. E. , Jacobs, G. A. , Chen, M. 2006

机译：代表法，集合卡尔曼滤波器和集合卡尔曼平滑器：非线性减少重力海洋模型的比较研究

Identifying trending model coefficients with an ensemble Kalman filter – a demonstration on a force model for milling

摘要

著录项

相似文献

相关主题

期刊订阅