Low-Velocity Impact Localization on Composites Under Sensor Damage by Interpolation Reference Database and Fuzzy Evidence Theory

• Main Authors: Hongyang Li, Zhongyu Wang, Jeffrey Yi-Lin Forrest, Wensong Jiang
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Low-velocity impact localization; fiber Bragg grating (FBG); sensor damage; interpolation reference database; fuzzy evidence theory
• Online Access: https://ieeexplore.ieee.org/document/8374415/

Composites are widely used in aeronautical manufacturing. Despite their excellent properties, composites suffer from barely visible impact damage caused by low-velocity objects. Random impacts need to be detected and located to alert pilots and engineers of the need for maintenance. Generally, fiber Bragg grating (FBG) sensors are installed in aerospace composites, and a reference database is established by recording the reference signals from different impact positions. The random impact is located by comparing its signal to the reference signals in the database. The performance of current localization algorithms mainly relies on the repeatability of FBG signals. However, the FBG sensors or their installation structures may be damaged by repeated impacts during the monitoring process, and the localization accuracy will decrease. In this paper, a new algorithm is proposed based on the interpolation reference database and fuzzy evidence theory to realize accurate impact localization under sensor damage. More correlation coefficients are obtained from the basic reference database by interpolation, and the influence of damaged sensors on localization results is reduced by fuzzy evidence theory. The proposed algorithm was tested on a carbon fiber reinforced polymer plate with four surface-attached FBG sensors. A parametric study was conducted to determine the coefficients of the algorithm. The localization performance was analyzed with both properly functioning sensors and damaged sensors. The results showed that the localization accuracy was better than the existing algorithms, especially in the case of sensor damage.