Multi-frequency probabilistic imaging fusion for impact localization on aircraft composite structures

Author:

Deng Deshuang1,Zeng Xu1,Yang Zhengyan2,Yang Yu3,Zhang Sheng3,Ma Shuyi4,Xu Hao1,Yang Lei1ORCID,Wu Zhanjun1ORCID

Affiliation:

1. State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Dalian University of Technology, Dalian, China

2. College of Transportation Engineering, Dalian Maritime University, Dalian, China

3. AVIC Aircraft Strength Research Institute, Xian, China

4. Dalian University of Science and Technology, Dalian, China

Abstract

Since the internal barely visible damage of aircraft composite structures caused by the impact is a critical problem, impact monitoring is essential for the integrity and reliability of aircraft composite structures. This paper presents a multi-frequency probabilistic imaging fusion method for localizing impacts on aircraft composite structures. To capture the impact signals, a network of distributed sensors is mounted on the structure. The impact signals are then processed using the continuous wavelet transform (CWT) to extract the multi-frequency narrowband Lamb wave signals. The time difference of arrival (TDOA), a key feature of the impact source, is measured using averaging techniques employed in the normalized variance sequence. Subsequently, a probabilistic imaging function is established, and the TDOA of narrowband Lamb wave signals at each frequency is used as the feature input to generate the multi-frequency probabilistic imaging results. To determine the performance of the imaging results at each frequency, an efficiency index is introduced, allowing for the retention or abandonment of the imaging results. By utilizing the retained multi-frequency probabilistic imaging results, the proposed method achieves impact localization through imaging fusion. Experimental verification is conducted on a stiffened aircraft composite panel, and a comparison is made with two existing methods: the hyperbolic locus imaging method and the virtual time reversal imaging method. The results show that the proposed method can significantly improve localization accuracy compared to the existing methods, and is effective even in the presence of measurement noise.

Funder

Natural Science Foundation of Liaoning Province

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

SAGE Publications

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