Condition Structural Index using Principal Component Analysis for undamaged, damage and repair conditions of carbon fiber–reinforced plastic laminate

Abstract

This article deals with the data reduction technique using the principal component analysis applied to the carbon fiber-reinforced plastic panels for structural health monitoring approaches. Two carbon fiber-reinforced plastic panels subjected to damage and repair coincide with typical aircraft repair procedures found in the aircraft structural repair manual. The panels were simulated with 30 mm diameter of partial and full penetration damages using a diamond-coated router. The data (50 observations) were captured for the undamaged, damaged, and repaired conditions by placing lead zirconate titanate smart sensors at 100 mm across the damaged and repaired structures. A time-based data response was captured for post analysis during the interrogation on the structure at each condition. The raw data were captured in a Lamb waveform, and the interested features were extracted using Morlet wavelet analysis to evaluate the Condition Structural Index and Amplitude-Based Assessment for each condition retrieved from the Gaussian-like distribution. The results were evaluated using the principal component analysis technique in order to distinguish the characteristic of the undamaged, damaged, and repaired conditions. The results showed that in all cases considered, it was possible to distinguish the conditions of undamaged, damaged, and repaired states with promising accuracy and repeatability of the data.

A Brief Review on Structural Health Monitoring Sensor Technology in Civil and Aviation Technology Applications

“…The advantage element of SHM is real-time monitoring. A lamb wave is also able to determine the undamaged, damaged and repaired characteristic of carbon fibre reinforced plastic (CFRP) laminate [32]. CFRP is replacing aluminum alloys as the primary and secondary structure for newly developed aircraft such as the Boeing 787 and Airbus A350.…”

“…The research reports that the intervals of interest were selected using Morlet wavelet analysis to evaluate the Condition Structural Index (CSI) and the Amplitude Based Assessment (ABA) for each condition. Next, the results were characterised using principal component analysis (PCA) to distinguish the characteristic -undamaged, damaged and repaired [32]. A case study between comparative vacuum monitoring (CVM) and lamb wave (LW) technique was compared in the Embraer-190 flight test aircraft [25].…”

“…Besides that, a brief discussion on sensor technology is presented as well. The sensor technology that widely use in SHM such as strain-based method [6], [12], [18], [26], Eddy current [27], guided waves [23], [28]- [32], acoustic emission [33]- [35]. and carbon nanotubes (CNT) [36].…”

New Discrimination Procedure of Location Model for Handling Large Categorical Variables

“…Variable extractions techniques have played an important role in the analysis of high dimensional data (Fan & Li 2006;Gervini & Rousson 2004). These techniques usually compute a system of b q variables which are linear combinations of the original b variables that contribute most of the variation (Mohd Aris et al 2014). These b q variables are called principal components.…”

“…Furthermore, unnecessary large number of variables will burden the computational effort and require longer computational time as well (Ramadevi & Usharaani 2013). For these reasons, most high dimensional data is extracted to present a more compact information with a better visualization and accuracy (Mohd Aris et al 2014). Variable extractions techniques have played an important role in the analysis of high dimensional data (Fan & Li 2006;Gervini & Rousson 2004).…”