In this research, a new method based on singular spectrum analysis (SSA) and fuzzy entropy is developed for damage detection on thin wall-like structures, and the normalized fuzzy entropy is employed as an indicator to identify the severity of the damage. The Lead Zirconate Titanate (PZT) transducers are used in this research to generate and detect the Lamb waves. During the detection, the collected signals from the PZT sensors are firstly decomposed and reconstructed by SSA to extract the feature of the damage, and then the reconstructed signals with the feature of the damage are processed to obtain the normalized fuzzy entropy. An experimental setup of an aluminium plate with added magnets is fabricated to validate the proposed method. The experimental results show that when magnets are attached on the aluminium plate, the normalized fuzzy entropy is smaller than that when there are no magnets. That is because when magnets are placed on the plate, the movement and some vibration modes of Lamb waves are disturbed by the added magnets and this disturbing effect can be enhanced by increasing the number and locations of the added magnets, and eventually the complexity and nonlinearity of the waves are weakened. The experimental results of a single damage with different number of magnets indicate that the normalized fuzzy entropy decreases linearly as the number of the added magnets increases, which demonstrates that the proposed method can be used to detect the severity of the damage. Moreover, the experimental results of multi-damage on different locations indicate that the normalized fuzzy entropy is relevant with both the total number and locations of the added magnets. The normalized fuzzy entropy decreases linearly as the total number of the magnets increases, and the entropy of a single damage is smaller than that of the multi-damage with the same total number of magnets, which demonstrates that the proposed method also can be used for multi-damage detection on a thin plate. This study provides us a new approach to identifying a single or multiple damages on thin wall-like structures.

A new type of smart aggregate using piezoceramic stack (SAPS) was developed for improved output, as compared with a conventional smart aggregate with a single piezoceramic patch. Due to the better output, the proposed smart aggregate is preferred where the attenuating effect is strong. In this research, lead zirconate titanate (PZT) material in the form of discs was used due to its strong piezoelectric performance. For analysis, the proposed SAPS was simplified to a one-dimensional axial model to investigate its electromechanical and displacement output characteristics, and an experimental setup was designed to verify the simplified model. Moreover, the influence of the structural parameters, including the number of the PZT discs, the dimensions of the PZT disc, protective shell, and copper lids, and the elastic modulus of the epoxy on the electromechanical and displacement output performance of SAPSs, were numerically studied by using the one-dimensional axial model. The numerical analysis results indicate that the structural dimension of the PZT discs has a greater effect on the electromechanical performance of SAPSs than that of the protective shell and copper lids. Moreover, the results show that the number of the PZT discs and the outer diameter of the protective shell have a much greater influence on the displacement output of SAPSs than other parameters. The analysis results of SAPSs with different elastic moduli of the epoxy demonstrate that the SAPSs’ first resonance frequency, first electromechanical coupling factor, and displacement output change less than 1.79% when the epoxy’s elastic modulus changes from 1.28 GPa to 5.12 GPa, which indicates that the elastic modulus of the epoxy has a limited influence on the property of SAPSs, and it will be helpful for their fabrication. This study provides an approach to increasing the output of SAPS and also develops a method to design the structure of SAPSs.

In this paper, a new method integrating the improved singular spectrum analysis and the multiscale cross-sample entropy (ISSA-MCSEn) is developed to identify the size of early damages in thin plate-like structures. In the algorithm, with the help of ISSA, the principal components relevant to the reference and damage-induced signals are successfully extracted, and then the components related to the damage are reconstructed for damage size detection. Lastly, the MCSEn of the reconstructed signal is computed as a new damage index to evaluate the size of the damage. To validate the proposed ISSA-MCSEn algorithm, two different experiments are conducted on aluminum and CFRP plates to detect simulated crack and through-hole, respectively. Comparative performance analysis of ISSA and SSA demonstrates that the total increment of the normalized MCSEn by using ISSA is 30%~81% while the one by using SSA is only 6.5%~9%, which demonstrates that the performance of the proposed ISSA is much better than SSA. The experimental results also show that the average of the normalized MCSEn of the proposed algorithm increases by over 77% and 28% as the size of the two damages in CFRP and aluminium plates changes from 0 to 8mm and 0 to 1.2mm, respectively. Moreover, the relationship between the normalized MCSEn and damages’ size is well linear, and the Pearson’s coefficient of their fitting curves is more than 0.99, which demonstrates that this linear relationship can be employed for damage size detection in both CRFP and aluminium plates. The linear relationship between the damage size and normalized MCSEn is used for damage detection, and the relative error between the actual and detected size is 1.64%~6.92%. In addtion, the performance comparison of ISSA-MCSEn and SSA-FuzzyEn shows that the total increment of the ISSA-MCSEn algorithm due to the damage is 30%~81% while the one of SSA-FuzzyEn is only 4%~15%, which indicates that the proposed ISSA-MCSEn is more sensitive to the damage than SSA-FuzzyEn and it is more suitable for detection of small-size damages.