Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals, which has certain limitations. Conversely, deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency. Recently, utilizing the respective advantages of convolution neural network (CNN) and Transformer in local and global feature extraction, research on cooperating the two have demonstrated promise in the field of fault diagnosis. However, the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model. This complexity results in high computational costs and limited industrial applicability. To tackle the above challenges, this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis. First, a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals. Then, an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective. Finally, experimental results on the planetary gearbox dataset and the motor roller bearing dataset prove that the proposed framework can balance the advantages of robustness, generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer. This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.

To achieve multi-mode fault sample generation and fault diagnosis of bearings in a complex operating environment with scarce labeled data. Combining a semi-supervised generative adversarial network (SGAN) and an auxiliary classifier generative adversarial network (ACGAN), a semi-supervised auxiliary classifier generative adversarial network (SACGAN) is constructed in this paper. The network structure and the loss function are improved. A fault diagnosis method based on STFT-SACGAN is also proposed. The method uses a short-time Fourier transform (STFT) to convert one-dimensional time-domain vibration signals of bearings into two-dimensional time-frequency images, which are used as the input of SACGAN. Two multi-mode fault data generation and intelligent diagnosis cases for bearings are studied. The experimental results show that the proposed method generates high-quality multi-mode fault samples with high fault diagnosis accuracy, generalization, and stability.

To realize applications involving low speed and high torque in the high-performance actuator industry, especially in the aerospace field, we propose a novel 70H traveling wave rotary ultrasonic motor (TRUM) with an outer diameter of 70 mm and an aperture ratio of 53% (the ratio between the aperture and outer diameter). The power transmission mechanism between the stator and the rotor is analyzed, and a method for realizing low-speed–high-torque characteristics of TRUMs is proposed. ANSYS software is used to simulate the modal parameters of the stator, and the ratio β between the normal amplitude and the tangential vibration velocity of the stator is proposed. The larger the value of β, the lower the speed and the higher the torque. Furthermore, two prototype motors are fabricated, namely, 70HA and 70HB. Compared with 70HA, the maximum no-load speed of the improved 70HB (50 rpm) is decreased by 23.1% and the maximum stall torque (2.4 N∙m) is increased by 100%. Besides this, with a smaller mass of 210 g and a higher torque density of 11.43 N∙m/kg, 70HB has fundamentally different mechanical characteristics.