ENEA E-nose Setup

Before detailing the findings obtained with the ENEA e-nose for the ENDT inspection of CFRP coupon level samples, we report in Table3.3the number of panels and corresponding features (37) sampled in the frame of the production user case by means of two different sampling methods.

Concerning the fingerprint (FP), moisture (MO), and release agent (RA) contam-ination scenarios within the production use case, the results presented in Fig.3.43 which were obtained with the 0 method for the sampling of analytes did not reveal the capability to distinguish between reference and contaminated samples, at least in the considered PCA subspace.

Based on these preliminary results, we decided not to go further in the investiga-tions of CFRP specimens from the production use case but instead focused on coupon panels from the defined repair user case to test the performance of e-nose ver.1. In

Table 3.3 Number of sample measurements with the CFRP coupon sample-based production use case for both the applied e-nose sampling methods; more details are given in the text

Production use case

0 method 25

PC method 44

Fig. 3.43 E-nose findings when performing PCA on the 0 method sampled coupon level CFRP panels

Table 3.4 Number of sample measurements with the CFRP coupon sample based on the repair use case; more details are given in the text

Repair use case

0 method 25

PC method 41

Table3.4, we present the number of samples taken for the analysis of specimens from the repair user case.

Assuming a two-class classification problem (FP/Skydrol versus ALL), we performed a preliminary data analysis using PCA so as to highlight the capabili-ties of the e-nose ver.1 to discriminate fingerprint/Skydrol hydraulic oil contamina-tion at each contaminacontamina-tion level from any other contaminated and reference CFRP samples. In this way, both sampling methods showed a limited capability to discrim-inate FP/Skydrol contamdiscrim-inated samples from the others in the PCA subspace. Mean-while, the PC sampling method enhanced the separation capabilities, obtaining a clear separation for FP level 3 contaminated samples, as shown in Fig.3.44.

Encouraged by these results, we performed a feature selection step for the samples investigated and recorded with both methods. Based on the ranking feature algorithm relief-f, ten relevant features were extracted for the PC method. Specifically, a subset

Fig. 3.44 E-nose findings when performing PCA on the PC method sampled flat CFRP panels

of steady-state and desorption rate features for MOX sensors was selected by the algo-rithm. By using the selected feature vector along with a logistic regression classifier, thereceiver operator characteristic(ROC) curve was drawn (see Fig.3.45). Its area under the curve (AUC) performance indicator was then computed, obtaining a value of 0.84. In this way, we have shown that a total correct classification (CC) rate of 78% can be achieved, at a false negative rate of 31% (Table3.5).

Fig. 3.45 AUC of the logistic regression classifier when evaluating the e-nose findings for the CFRP coupons

Table 3.5 Parameters of the classifier performance assessment for evaluating e-nose data obtained for CFRP coupons. One class representing FP-contaminated samples (at all contaminated levels)

Fig. 3.46 Neural network regression results obtained with e-nose ver.1 data, considering all samples (note 0 level contamination). The results have been obtained using a leave-1-out cross-validation procedure. Note that the contamination level is underestimated and that the regressor is basically capable to discriminate the highest level of contamination samples from uncontaminated samples

Further analyses were conducted to compute the contamination quantification capabilities of the e-nose ver.1. In particular, a simple multivariate regression algo-rithm (FFNN) was used to estimate the level of contamination by a linear continuous encoding of the level score ranging from 0 to 3. Results shown in Fig.3.46show that the selected regression algorithm when applied to all e-nose ver.1 captured data is not capable to directly estimate the contamination level of a sample. Actually, it is only capable to discriminate the third (highest) FP contaminant level, and its mean absolute error (MAE) evaluation resulted in a 0.69 value.

In Fig.3.47, we show the results obtained for the correctly identified contaminated samples. In this case, the regression method offers much better performance, high-lighting a progressive behavior when evaluating contamination levels of increasingly contaminated samples. The MAE score, in this case, reaches a 0.5 value. This suggests that experimental conditions variability severely hampers the inherent discrimina-tion and quantificadiscrimina-tion capability for some of the samples. However if the condidiscrimina-tions favor the correct measuring of the samples, then its identification as contaminated and the consequential contamination level quantification may be successful.

Samples recorded with the 0 method were screened with a discriminant analysis (DA) approach in order to select the most discriminative features. In particular, we restricted our analysis to the three features obtaining the best DA scores. Specifically, all the selected features were uptake derivatives of MOX sensor responses. Two of these were selected for the final classification task, namely the uptake derivatives

Fig. 3.47 Regression results obtained with the ENEA e-nose ver.1 when considering correctly identified contaminated samples

with respect to MOX1 and MOX5, which were used in the classification task to differentiate the FP/Skydrol contaminated coupons from the CFRP reference (RE) samples. This selection allowed a simple tree classifier to achieve a correct classifi-cation rate of 86.44% (false negatives FN=0%, false positives FP=27%, as listed in Table3.6).

Table 3.6 Confusion matrix as obtained by a classification tree (CT) when discriminating uncontaminated reference samples from FP/Skydrol contaminated samples

Fig. 3.48 E-nose with the desorber device imaged with the sampling head approaching CFRP parts with different sample geometries