With the aim of increasing the efficiency of maintenance and fuel usage in airplanes, structural health monitoring (SHM) of critical composite structures is increasingly expected and required. The optimized usage of this concept is subject of intensive work in the framework of the EU COST Action CA18203 “Optimising Design for Inspection” (ODIN). In this context, a thorough review of a broad range of energy harvesting (EH) technologies to be potentially used as power sources for the acoustic emission and guided wave propagation sensors of the considered SHM systems, as well as for the respective data elaboration and wireless communication modules, is provided in this work. EH devices based on the usage of kinetic energy, thermal gradients, solar radiation, airflow, and other viable energy sources, proposed so far in the literature, are thus described with a critical review of the respective specific power levels, of their potential placement on airplanes, as well as the consequently necessary power management architectures. The guidelines provided for the selection of the most appropriate EH and power management technologies create the preconditions to develop a new class of autonomous sensor nodes for the in-process, non-destructive SHM of airplane components.

For the field of structural health monitoring (SHM), acoustic emission (AE) technology is important as a damage identification technique that does not cause secondary damage to concrete. Nowadays, applications of non-destructive concrete damage identification are mostly limited to commercial software or identification algorithms running on desktop computers. It has so far not been deployed in low-power embedded devices. In this study, a lightweight convolutional neural network (CNN) model for online non-destructive damage type recognition of concrete materials is presented and deployed on a resource-constrained microcontroller unit as a tiny machine learning (TinyML) application. The CNN model uses raw acoustic emission signals as input and damage recognition types as output. 15,000 acoustic emission signals are used as data sets divided into training, validation, and test sets in the ratio of 8:1:1. The experimental results show that an accuracy of 99.6% is achieved on the nRF52840 microcontroller (ARM Cortex M4) with only 166.822 ms and 0.555mJ for a single inference using only 20K parameters and 30.5KB model size. This work demonstrates the effectiveness and feasibility of the proposed model, which achieves a trade-off between high classification accuracy and deployability on resource-constrained MCUs. Consequently, it provides strong support for online continuous non-destructive structural health monitoring.

The characterization of light conditions plays an important role in the estimation of available energy levels to ambient light energy harvesting systems. Indoor light conditions are commonly described by illuminance levels. The same illuminance levels, however, can be generated by different light source types, which radiate different spectral components. This means that based on their spectral response, solar panels can produce different output powers even though identical illuminance levels are observed. We propose a method to distinguish these conditions based on limited spectral information. Using low-cost sensors, spectral characteristics of the light condition can be acquired and used to classify the underlying light source type, which allows for a more accurate estimation of the solar panel response. The method was evaluated experimentally for a number of common indoor light sources and under different conditions. Evaluation results have shown that the method can be used to distinguish the light sources under test with very high classification accuracy. Moreover, the method can be used accurately in situations with limited interference. This makes it a low-cost alternative to the characterization of light conditions using spectrometers, the use of which is infeasible in spatially distributed characterization applications.

Intelligent acoustic emission-based pipeline leak detection technology plays a critical role in Internet of Things structural health monitoring for offshore platforms. However, traditional deep networks possess large parameter counts and high computational complexity, making them infeasible for deployment on resource-constrained edge nodes, while lightweight methods universally adopt single-scale feature extraction and cannot simultaneously capture short-duration burst and long-range attenuation characteristics of acoustic emission signals, resulting in insufficient discriminative capability for adjacent valves. To address this, this article proposes Tiny Leak Sense Net (TinyLSN), a novel, lightweight leak localization framework specifically designed for Internet of Things edge nodes. TinyLSN achieves an optimal balance between computational efficiency and detection performance through three innovative components we designed, including the inverted residual block (IRB), multiscale dilated perception module (MSDPM), and large kernel feed-forward network (LK-FFN), which, respectively, enhance cross-channel interactions, capture multiscale temporal features, and extract global attenuation patterns. On our self-constructed experimental dataset simulating real offshore platform operational pipeline leakage, TinyLSN achieved detection accuracy of 97.11%–97.45% and an extremely low false positive rate of 0.27%–0.32%, significantly outperforming lightweight baseline methods. Validation on publicly available benchmark datasets further confirmed its generalization capability. When deployed on the STM32H7B3I-DK microcontroller, TinyLSN requires only 267.16-KiB flash memory and achieves 3.417-ms inference latency. Furthermore, TinyLSN maintains over 90% accuracy under strong noise and achieves 94.83% accuracy with only 10% training samples, fully validating its reliability in harsh industrial environments and providing an efficient and feasible solution for offshore platform Internet of Things edge intelligence.