Damage identification of thin plate-like structures combining improved singular spectrum analysis and multiscale cross-sample entropy (ISSA-MCSEn)

Abstract

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.

Data-Driven Structural Health Monitoring: Leveraging Amplitude-Aware Permutation Entropy of Time Series Model Residuals for Nonlinear Damage Diagnosis

“…Therefore, a damaged system exhibits a different level of complexity compared to a baseline system. Over the past few decades, several entropy-based methods have been introduced in the field of SHM, such as sample entropy [ 27 , 28 ], fuzzy entropy [ 29 ], permutation entropy [ 30 ], and spectral entropy [ 31 ]. Information theory-based methods have emerged as a new approach to novelty detection [ 32 ].…”

Automatic Detection of Maintenance Scenarios for Equipment and Control Systems in Industry

“…In addition, as the name of the method suggests, SSA is used to decompose the time series into several interpretable components that enable us to draw some conclusions and obtain new insights on the time series. Such research includes work in the field of equipment fault investigation [41][42][43][44][45]. For example, in [46], scientists determine the nature of defects in induction motor bearings by analyzing current and voltage [47].…”