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Kalman-based load identification and full-field estimation analysis on industrial test case

机译：基于卡尔曼的工业测试用例的负荷识别和全场估计分析

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摘要

The potential of the Augmented Kalman Filter algorithm is tested in this paper for joint state-input estimation in structural dynamics field. In view of inverse load identification, the filter is compared with the Transfer Path Analysis Matrix Inversion technique, commonly used for industrial applications. An existing Optimal Sensor Placement strategy for Kalman Filter is adopted and validated on real experimental data. The advantages of the proposed methods, through strain measurements information, are identified in the effort needed for data-acquisition and data-processing. The effectiveness of the filter and the quality of the results are demonstrated in this paper for an industrial test-case, such as a rear twistbeam suspension. (C) 2018 Elsevier Ltd. All rights reserved.
机译：本文测试了增强卡尔曼滤波算法在结构动力学领域中联合状态输入估计的潜力。考虑到反向负载识别，将滤波器与通常用于工业应用的传输路径分析矩阵求逆技术进行比较。采用现有的卡尔曼滤波器最佳传感器放置策略，并在实际实验数据上进行了验证。通过应变测量信息，提出的方法的优势在数据采集和数据处理所需的努力中得以确定。本文针对工业测试用例（例如后部扭束悬架）证明了滤波器的有效性和结果的质量。 （C）2018 Elsevier Ltd.保留所有权利。

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