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Compared with the deepening development of time-space coding metasurfaces in the electromagnetic and acoustic field, the research on time-space coding metasurface in the field of elastic vibration is still in the infancy. In order to enrich the designed schemes of spatiotemporal coding for elastic vibration, a time-space coding metasurface based on the random modulation strategy of wavefront phase is proposed in this paper. Compared with traditional homogeneous materials with highly correlated vibration transmission, the proposed coding metasurfaces with homogeneous materials realizes extremely low correlated vibration transmission in different vibration directions, which provides a physical realization for the measurement matrix in compression sensing. Furthermore, combined with compressed sensing technology, the potential application value of the proposed time-space coding metasurfaces in single-sensor vibration location and impact identification is verified through simulation.

Gearbox diagnosis is critical for avoiding catastrophic failure and minimizing financial damages. Aiming at the problem that the vibration-based fault diagnosis methods cannot effectively identify the non-structural failure mode and the diagnosis model based on the infrared thermal image is not robust enough, a fusion fault diagnosis method for gearboxes using vibration signals and infrared images is proposed. By fusing these two kinds of heterogeneous data, the proposed method can identify both structural and unstructured health states while maintaining high robustness. In addition, CNN has powerful image processing capabilities, which can directly process two-dimensional infrared images and achieve high accuracy. Finally, a gearbox experiment is carried out to test the performance of our method. The results suggest that the proposed fusion CNN can obtain the highest accuracy compared with some methods based on single signals, shallow learning methods SVM and deep unsupervised learning methods SAE.

To overcome the problem that traditional feature extraction algorithms are sensitive to noise, a bearing fault signature extraction scheme is proposed in this paper with the help of oscillation-based signal decomposition and time frequency manifold (TFM) learning. Firstly, an oscillation-based signal component separation method based on tunable Q factor wavelet transform (TQWT) is utilized to separate the low oscillatory component from vibration signals. Then, concept of TFM is utilized on the separated low oscillatory component to generate the low oscillatory time frequency manifold signature. The proposed method is termed as oscillatory time frequency manifold (OTFM). Compared to that of traditional short time Fourier transform (STFT) and original TFM algorithm, results of experiment show that the proposed algorithm has better time frequency characterization ability for bearing fault signature.

Planetary gearboxes play a critical role in aerospace and heavy industry fields, such as wind turbines, heavy vehicles and construction machines. Intelligent fault diagnosis is significant for safe operation and fault prevention of planetary gearboxes. Recently, multiscale diversity entropy and related entropy methods are proposed to extract features of time series and applied for the fault diagnosis. However, there are still some limitations in fault feature representation and stability for multiscale diversity entropy. To solve the problem, in this paper, a novel planetary gearboxes fault diagnosis method via refined time-shift multiscale diversity entropy (RTSMDE) is proposed. First, a novel entropy algorithm called RTSMDE is proposed to measure the complexity of time series and extract fault features of the vibration signals, which is robust and efficient in performance. Then, the obtained features are utilized to fulfil automatically the fault pattern identifications using support vector machine. To confirm the superiority of the RTSMDE-based fault diagnosis method, simulated signals and experimental studies are constructed and three used widely methods are employed to present a comprehensive comparison. The results indicate that RTSMDE performs best and obtains the highest accuracy.

According to the performance degradation law of aeroengine in operation, we proposed a method of aeroengine performance degradation evaluation based on the fusion decision of Hierarchical Bayes method (HB) and deep neural network (DNN). Hierarchical Bayes method was used to build the aeroengine reliability model. Then Based on the aeroengine operation monitoring parameters and reliability model, we used deep learning method to extract the performance degradation law and to evaluate aeroengine performance level, realized the fusion decision of engine performance degradation assessment. 9467 monitoring samples, contaminated with sensor noise, collected from 50 aeroengines was used to evaluate the quality of the network, and the average accuracy is 92.01%. The results showed that this method showed good robustness and reduced the risk of the evaluation results error caused by noise.