Advances in composite technology led to the substitution of conventional, metallic construction material by composites. However, the more widespread application of composites is currently restricted by complex fracture mechanisms, which are not well understood. One approach to overcome this challenge is structural health monitoring systems which provide a lot of information on the current system state as well as state of health in real time. In this context, reliability assessment of structural health monitoring systems is currently an open issue. The reliability of conventional nondestructive testing systems is evaluated, measured, and partly standardized using widely accepted methods such as the probability of detection rate. Frequently, the a 90|95 value, which is determined from the probability of detection curves, is used as a performance measure indicating the minimum damage size that is detected with a probability of 90% and 95% confidence. In contrast to non-destructive testing, structural health monitoring involves additional data analysis steps, that is, statistical pattern recognition, where the classification results are also subject to uncertainty. Because similar methods are not available, the reliability of structural health monitoring systems is usually not quantified. To investigate the influences on the classification performance, experiments were conducted. In particular, the effect of variable loading conditions and the evolution of damage over time are considered. To this end, acoustic emission measurements were performed, while the specimens of the composite material were subjected to different cyclic loading patterns. Here, acoustic emission refers to elastic stress waves in the ultrasound regime, which emerge from the structure on damage initiation and propagation. Furthermore, a frequency-based damage classification scheme for acoustic emission measurements is proposed. Time-frequency domain features are extracted from the measurement signals using shorttime Fourier transform. Classification is performed using support vector machine. Both choices serve as typical examples to discuss the effects which apply equally to other approaches. Experimental results presented in this article regarding fault diagnosis and discrimination of delamination, matrix crack, debonding, and fiber breakage in carbon-fiberreinforced polymer material show that good performance applying support vector machine could be achieved using 10fold cross validation. However, during model deployment, strong dependency of the classification reliability on loading conditions can be clearly stated, which could not be seen from the previous evaluation. Concluding from these results, it can be stated that the application of classifier-based structural health monitoring is more complex than generally assumed. The relations between the classification approaches, testing conditions, measurement devices, and filters have to be discussed with respect to the ability to provide reliable statements about the actual damage state.

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Today, the demand for continuous monitoring of valuable or safety critical equipment is increasing in many industrial applications due to safety and economical requirements. Therefore, reliable in-situ measurement techniques are required for instance in Structural Health Monitoring (SHM) as well as process monitoring and control. Here, current challenges are related to the processing of sensor data with a high data rate and low latency. In particular, measurement and analyses of Acoustic Emission (AE) are widely used for passive, in-situ inspection. Advantages of AE are related to its sensitivity to different micro-mechanical mechanisms on the material level. However, online processing of AE waveforms is computationally demanding. The related equipment is typically bulky, expensive, and not well suited for permanent installation. The contribution of this paper is the development of a Field Programmable Gate Array (FPGA)-based measurement system using ZedBoard devlopment kit with Zynq-7000 system on chip for embedded implementation of suitable online processing algorithms. This platform comprises a dual-core Advanced Reduced Instruction Set Computer Machine (ARM) architecture running a Linux operating system and FPGA fabric. A FPGA-based hardware implementation of the discrete wavelet transform is realized to accelerate processing the AE measurements. Key features of the system are low cost, small form factor, and low energy consumption, which makes it suitable to serve as field-deployed measurement and control device. For verification of the functionality, a novel automatically realized adjustment of the working distance during pulsed laser ablation in liquids is established as an example. A sample rate of 5 MHz is achieved at 16 bit resolution.

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Recently, acoustic emission-based damage classification schemes gained attention for health monitoring of composites. Here, the reliable detection of different micro-mechanical damage mechanisms is important because of the adverse effect on fatigue life. It is well known that classical parameters obtained from acoustic emission measurements in time domain are strongly dependent on the propagation path and testing conditions. However, signal attenuation, which can be observed due to geometric spreading, material-related damping, and dispersion, is typically neglected. Therefore, it is generally assumed that frequency domain features are reliable descriptors of damage due to invariance of peak frequencies to the propagation path. Based on this assumption, several data-driven approaches for damage detection are developed. However, in contrast to metallic materials, where low attenuation is observed, acoustic emission signals are strongly attenuated in polymer matrix composites due to viscoelastic behavior of the matrix. For instance, it is reported in literature that at high frequencies most of the acoustic emission signal energy is attenuated after a propagation distance of 250~mm. Therefore, new experimental results of acoustic emission attenuation in composites are presented in this paper. Particular focus is placed on the frequency dependence of acoustic emission attenuation and the effect of different loading conditions. The specimens are manufactured from aerospace material. Carbon fiber reinforced polymer plates are used as a typical specimen geometry. Different acoustic emission sources are considered and the related attenuation coefficients are determined. Furthermore, full waveform data are analyzed in time and time-frequency domain using wavelet transform. From the experimental results it can be concluded that consideration of wave propagation-related signal attenuation is important for the interpretation of acoustic emission measurements for health monitoring of composites. Consequently, the impact on the detectability of different physical damage mechanisms using data-driven classification approaches has to be considered.