Towards a better lifetime prediction of composite structures under in-service conditions : robust and real-time processing of acoustic emission time-series in presence of damage accumulation

Due to the difficulties encountered to predict the long-term behaviour of composite structures in operating conditions, a real-time monitoring of their integrity is required in order to anticipate catastrophic failures. Although the early detection of crack initiation and propagation is beyond the potential of most non-destructive techniques (NDT), acoustic emission (AE) is one of a limited number of methods that possess the capacity for continuously detecting the occurrence of damage in large composite components or structures. Even if promising and successfully exploited in several industrial fields using commercial systems, AE has not provided at this time an effective NDT tool for composite industry, in particular for mobile structures and in-service applications. For such applications, the main difficulties are related to the real-time processing of a huge amount of complex AE time-series originating from multiple distributed sensors. One major problem is the discrimination of AE signals generated by the different damage modes from other external AE sources such as electromagnetic and mechanical noises (rubbing and friction) which are mainly generated by the surrounding environment. Another important problem is the processing of continuous and complex AE signals resulting from high AE rates, from the superimposition of transients emitted from different sources and from the distortion induced by damage accumulation. In this context, we have developed a method able to objectively discriminate with robustness AE signals generated by a specific damage mode from other AE sources in carbon fibre reinforced composite materials submitted to complex loading (multiaxial fatigue). This method includes wavelet transform-based signal processing and unsupervised multivariate pattern recognition of massive AE streaming. Uncertainties on the estimation of clusters are quantified by fusing multiple clusterings. We demonstrate on real cases that the proposed method is able to efficiently process massive AE data, as encountered in operating conditions, and take into account the distortion of the AE signals as well as the evolution of the clusters shape induced by the wave attenuation, anisotropy and damage accumulation in composite materials submitted to cyclic loading.