Recurrence-plot (RP) analysis is a graphical tool to visualize and analyze the recurrence of nonlinear dynamic systems. By combining the advantages of the RP and a convolutional neural network (CNN), a fault-classification scheme for planetary gear sets is proposed in this paper. In the proposed approach, a vibration is first picked up from the planetary-gear test rig and converted into an angular-domain quasistationary signal through computed order tracking to eliminate the frequency blur caused by speed fluctuations. Then, the signal in the angular domain is divided into several segments, and each segment is processed by the RP to constitute the training sample. Moreover, a two-dimensional CNN model was developed to adaptively extract faulty features. Experiments on a planetary-gear test rig with four conditions under three operating speeds were carried out. The results of measured vibration demonstrated the validity of CNN and recurrence plot analysis for the fault classification of planetary-gear sets.

To address the problems of existing methods that struggle to effectively extract fault features and unstable model training using unbalanced data, this paper proposes a new fault diagnosis method for rolling bearings based on a Markov Transition Field (MTF) and Mixed Attention Residual Network (MARN). The acquired vibration signals are transformed into two-dimensional MTF feature images as network inputs to avoid the loss of the original signal information, while retaining the temporal correlation; then, the mixed attention mechanism is inserted into the residual structure to enhance the feature extraction capability, and finally, the network is trained and outputs diagnostic results. In order to validate the feasibility of the MARN, other popular deep learning (DL) methods are compared on balanced and unbalanced datasets divided by a CWRU fault bearing dataset, and the proposed method results in superior performance. Ultimately, the proposed method achieves an average recognition accuracy of 99.5% and 99.2% under the two categories of divided datasets, respectively.

To study the variation law of static tensile properties and damage mechanism of fiber metal laminates, this paper conducted static tensile tests on six types of GLARE specimens. During the testing process, the full-field strain data of the specimen were captured using Digital Image Correlation technology. A comparative analysis was conducted on the elastic modulus, tensile strength, stress-strain curve, and Poisson's ratio change curve. The fracture morphology of the specimen, strain cloud map before final fracture, and the process of strain difference formation were compared and analyzed. The results show that the bridging effect of fibers needs to be properly considered when using the hybrid model method to predict the tensile strength of the ±45°fiber-metal laminates. The role of the prepreg layer in the GLARE sample gradually increases after the metal material yields. For the same type of prepreg, the damage forms in different layers of the board are not exactly the same. Using the DIC method is helpful to study the damage process and boundary effects inside the fiber metal laminate.