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The rapid development of unmanned aerial vehicle (UAV) market has triggered the demand for UAV structural health monitoring technology, and blade damage has received extensive attention as a common mechanical fault. In this study, the spatial vibration modulation method (SVMM) is proposed to assist in blade damage localization for an industrial quadrotor UAV. The key to SVMM is that, by arranging silicone-made ring-shaped vibrators with randomly selected structural parameters on the beams of the UAV, the frequency transfer functions of these beams with low cross-correlation are achieved. With SVMM, high-accuracy blade damage localization capability can be realized under single sensor strategies on the symmetrical UAV. The study considers the situations of one and two damage locations, and the recognition accuracies are both 97%, whereas those without SVMM are 90% and 86%, respectively. The results show that SVMM can effectively enhance the localization capability for blade damage. Due to the customized frequency transfer functions, the technique proposed in this study shows potential in the fields of UAVs or other mechanical equipment for structural health monitoring and damage assessment.

Intelligent machine fault diagnosis technique has recently exploded interest in digital health, energy power, and industrial maintenance. Collecting machine fault data in engineering practice is usually costly, causing big challenges in the intelligent diagnosis of most fresh-from-the-factory machines that are missing fault data. Inspired by the main idea of the digital twin, in this article, we propose a dynamic model-embedded intelligent machine fault diagnosis framework. Specifically, a machine dynamic modeling and parameter identification approach is introduced for building the machine digital model. The digital model is used to predict machine fault data from the measured healthy vibration signals. Furthermore, a parameterized convolutional neural network structure is designed for learning optimization features and recognizing the machine's healthy state. Experimental investigation demonstrates the effectiveness of the framework. The results show that the proposed framework enables intelligent machine fault diagnosis without fault data. The diagnosis effect outperforms supervised, unsupervised, and small-sample learning approaches and can be generalized to another load and speed with acceptable accuracy.