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一种基于序列的复合材料转子损伤识别方法,该方法应用了库尔贝克-莱布勒散度、双样本柯尔莫哥洛夫-斯米尔诺夫检验和统计隐马尔可夫模型。

A Sequence-Based Damage Identification Method for Composite Rotors by Applying the Kullback-Leibler Divergence, a Two-Sample Kolmogorov-Smirnov Test and a Statistical Hidden Markov Model.

作者信息

Filippatos Angelos, Langkamp Albert, Kostka Pawel, Gude Maik

机构信息

Institute of Lightweight Engineering and Polymer Technology (ILK), Technische Universität Dresden, 01307 Dresden, Germany.

出版信息

Entropy (Basel). 2019 Jul 15;21(7):690. doi: 10.3390/e21070690.

Abstract

Composite structures undergo a gradual damage evolution from initial inter-fibre cracks to extended damage up to failure. However, most composites could remain in service despite the existence of damage. Prerequisite for a service extension is a reliable and component-specific damage identification. Therefore, a vibration-based damage identification method is presented that takes into consideration the gradual damage behaviour and the resulting changes of the structural dynamic behaviour of composite rotors. These changes are transformed into a sequence of distinct states and used as an input database for three diagnostic models, based on the Kullback-Leibler divergence, the two-sample Kolmogorov-Smirnov test and a statistical hidden Markov model. To identify the present damage state based on the damage-dependent modal properties, a sequence-based diagnostic system has been developed, which estimates the similarity between the present unclassified sequence and obtained sequences of damage-dependent vibration responses. The diagnostic performance evaluation delivers promising results for the further development of the proposed diagnostic method.

摘要

复合材料结构经历从初始纤维间裂纹到扩展损伤直至失效的逐渐损伤演化过程。然而,大多数复合材料尽管存在损伤仍可继续服役。延长服役期的前提是进行可靠的、针对特定部件的损伤识别。因此,提出了一种基于振动的损伤识别方法,该方法考虑了复合材料转子的逐渐损伤行为以及由此导致的结构动力学行为变化。这些变化被转化为一系列不同的状态,并用作基于库尔贝克-莱布勒散度、双样本柯尔莫哥洛夫-斯米尔诺夫检验和统计隐马尔可夫模型的三种诊断模型的输入数据库。为了基于与损伤相关的模态特性识别当前损伤状态,开发了一种基于序列的诊断系统,该系统估计当前未分类序列与获得的与损伤相关的振动响应序列之间的相似度。诊断性能评估为所提出的诊断方法的进一步发展提供了有希望的结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0875/7515192/c2982ad706b9/entropy-21-00690-g001.jpg

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