Topological data analysis for lamb waves based shm method in operational conditions

Abstract

Structural Health Monitoring (SHM) based on Lamb wave propagation is a promising solution to optimize maintenance, safety and enlarge service life of aeronautical structures. However, it remains a significant challenge to solve requirements for performance and accuracy. In this paper, an original method based on Topological Data Analysis (TDA) is intro- duced. TDA is a multi-dimensional method which can extract the topological features from time series and point cloud. First, the TDA tool is applied to raw 1D data in order to detect damages. Then, specific pre-processing of the measured time-series based on slicing is developed to improve the persistence homology perception and to leverage topological descriptors to classify different damages. Using a Lamb wave based SHM approach, it is shown that with specific pre-processing of the measured time-series data, the topological analysis (persistent homology) for damage detection and classification can be greatly improved. The temperature of the material has an impact on wave propagation and attenuation properties. It is important to ensure the capacity to detect and classify the damages on material on operational conditions of aerospace structures. The proposed approach enables to consider a priori physical information and provides another way to categorize damages than the traditional approaches. This work aims to characterize the temperature influence on the TDA performance to cluster damages. Finally, a strategy robust to temperature evolution is suggested to classify the plate health state. The dataset used to apply both methods comes from experimental campaigns performed on aeronautical composite plates with embedded piezoelectric transducers where different damage types have been investigated such as delamination and different impacts. In summary, this paper demonstrates that manipulating the topological the features of time-series signals using TDA provides an efficient mean to separate and classify the damage natures. It opens the way for further developments on the use of TDA in SHM.