Fatigue crack diagnosis (FCD) is of great significance for ensuring safe operation, prolonging service time and reducing maintenance cost in aircrafts and many other safety-critical systems. As a promising method, the guided wave (GW)-based structural health monitoring method has been widely investigated for FCD. However, reliable FCD still meets challenges, because uncertainties in real engineering applications usually cause serious change both to the crack propagation itself and GW monitoring signals. As one of deep learning methods, convolutional neural network (CNN) owns the ability of fusing a large amount of data, extracting high-level feature expressions related to classification, which provides a potential new technology to be applied in the GW-structural health monitoring method for crack evaluation. To address the influence of dispersion on reliable FCD, in this paper, a GW-CNN based FCD method is proposed. In this method, multiple damage indexes (DIs) from multiple GW exciting-acquisition channels are extracted. A CNN is designed and trained to further extract high-level features from the multiple DIs and implement feature fusion for crack evaluation. Fatigue tests on a typical kind of aircraft structure are performed to validate the proposed method. The results show that the proposed method can effectively reduce the influence of uncertainties on FCD, which is promising for real engineering applications.

Prognostics and health management techniques have drawn widespread attention due to their ability to facilitate maintenance activities based on need. On-line prognosis of fatigue crack propagation can offer information for optimizing operation and maintenance strategies in real-time. This paper proposes a Lamb wave-particle filter (LW-PF)-based method for on-line prognosis of fatigue crack propagation which takes advantages of the possibility of on-line monitoring to evaluate the actual crack length and uses a particle filter to deal with the crack evolution and monitoring uncertainties. The piezoelectric transducers (PZTs)-based active Lamb wave method is adopted for on-line crack monitoring. The state space model relating to crack propagation is established by the data-driven and finite element methods. Fatigue experiments performed on hole-edge crack specimens have validated the advantages of the proposed method.

On-line fatigue crack evaluation is crucial for ensuring the structural safety and reducing the maintenance costs of safety-critical systems. Among structural health monitoring (SHM), guided wave (GW)-based SHM has been deemed as one of the most promising techniques. However, the traditional damage index-based method and machine learning methods require manual processing and selection of GW features, which depend highly on expert knowledge and are easily affected by complicated uncertainties. Therefore, this paper proposes a fatigue crack evaluation framework with the GW–convolutional neural network (CNN) ensemble and differential wavelet spectrogram. The differential time–frequency spectrogram between the baseline signal and the monitoring signal is processed as the CNN input with the complex Gaussian wavelet transform. Then, an ensemble of CNNs is trained to jointly determine the crack length. Real fatigue tests on complex lap joint structures were carried out to validate the proposed method, in which several structures were tested preliminarily for collecting the training dataset and a new structure was adopted for testing. The root mean square error of the training dataset is 1.4 mm. Besides, the root mean square error of the evaluated crack length in the testing lap joint structure was 1.7 mm, showing the effectiveness of the proposed method.