An aircraft is a multilayer structure that is assembled by rivets. Under extreme working conditions, corrosion appears and quickly propagates at the rivet sites of the layers; thus, it threads the integrity and safety of the aircraft. Corrosion usually occurs at the hidden layer around the rivet, making it difficult to detect. This paper proposes a machine learning approach incorporating an electromagnetic testing system to detect the hidden corrosion at the riveting site effectively. Several machine learning methods will be investigated for the detection of different sizes and locations of corrosion. The training strategy of the machine-learning models on the small numbers of data will also be investigated. The result shows that the proposed approach could effectively detect 89.48% of the hidden corrosion having from 2.8 to 195.4 mm³ with only 20% of training data and could be increased to 99.0% with 60–80% of the training data.

Le Dang Khanh and Pham Xuan Duong. IJFIS 2020;20:211-8. https://doi.org/10.5391/IJFIS.2020.20.3.211

Corrosion detection in critical structural components is a persistent challenge in the aerospace and structural health monitoring industries. This work introduces an advanced Gaussian-Pulsed Eddy Current Testing (GPECT) system integrated with a Spiking Neural Network (SNN) for precise and efficient detection of corrosion. Unlike conventional square-pulse excitation, the proposed GPECT system employs a Gaussian pulse, enabling selection of the frequency bandwidth to enhance defect detection sensitivity. The system’s sensor probe features a coil for magnetic excitation and a Hall sensor centrally positioned to capture the resulting magnetic field signals. These signals are transformed into the frequency domain using Short-Time Fourier Transform (STFT), facilitating the extraction of key spectral features indicative of corrosion. Leveraging the temporal and energy-efficient processing capabilities of the SNN, which incorporates spike generation, convolutional feature extraction, and robust classification, the system achieves significant improvements in detectability and energy savings (10 time smaller than conventional NN model). Experimental evaluations on aluminum specimens with artificially induced corrosion of varying depths and diameters validate the system’s effectiveness, demonstrating high accuracy and robustness in differentiating corroded and non-corroded regions. This work establishes a robust foundation for next-generation nondestructive testing techniques, pushing the frontiers of corrosion detection in high-stakes applications.

This paper considers the DCSK system under jamming environment. We consider both fast and slow switching pulse jamming (PJ). We derive approximations of the system bit error rates (BERs) in analytic expressions containing only well-known special functions, from which we show that for both jamming cases increasing the spreading factor can enhance the BER. In addition, we reveal that under the fast switching PJ, the BER does not depend on the jamming duty cycle ρ when ρ ≤ 0.5, however when ρ > 0.5, increasing its value degrades the BER. Moreover, we find out that under the slow switching PJ, increasing ρ may either enhance or degrade the BER.

Moisture separator reheater (MSR) tubing systems are an important part of a pressurized-water power plant to increase the efficiency of the heat transfer rate. The MSR tubes are finned tubes which are made of ferritic stainless steel (SS439) with a high strength and corrosion resistance characteristics. However, corrosion can appear along with the fins after a long period of operation of the MSR tubes that requires nondestructive testing (NDT) of the MSR tubes’ periodically. Electromagnetic testing (ET) is an efficient NDT method for the inspection of far-side corrosion in the MSR tubes. However, the ET sensor signal is affected by signal noise from the fins. Material degradation that make it challenging to inspect and evaluate the corrosion. In this study, we proposed three ET methods, including magnetic flux leakage testing, eddy current testing and partial saturation eddy current testing, and incorporated with a multivariate singular spectral analysis (MSSA) filter to improve the detectability of the corrosion in the MSR tubes. The proposed MSSA filter was compared with the multivariate wavelet transform filter and Gabor transform filter, and the results showed more efficient and stable results of the MSSA filter in the extraction of the corrosion signal.