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Due to extreme operating conditions such as high-speed and heavy loads, ball screws are prone to damages, that affect the accuracy and operational safety of the mechanical equipment. As strong background noise and weak fault characteristics, it is difficult to capture the inherent fault state only depending on the time-domain or frequency-domain information from the vibration signal. In this paper, a fault diagnosis method for the ball screw based on continuous wavelet transform (CWT) and two-dimensional convolutional neural network (2DCNN) is proposed. The noise-reducing vibration signal is obtained via CWT. The time-frequency graph of the noise reduction signal can more comprehensively reflect the fault information of the ball screw. The time-frequency graph is used as the input to train and test the 2DCNN. Finally, diagnosis results of different types of faults reveal that the proposed CWT-2DCNN fault diagnosis method can achieve an average recognition rate of 99.67%. Compared with one-dimensional convolutional neural network (1DCNN) and traditional BP neural network, the proposed method has fast network convergence and high recognition accuracy. Time-frequency graphs of the noise-reduced signal used as fault features for classification can effectively avoid the problem of uncertainty due to the manual extraction of features. The proposed method has high application potential in the field of ball screw pair fault diagnosis.

Since rolling bearings determine the stable operation of industrial equipment, it is necessary to carry out their fault diagnosis. To improve fault diagnosis accuracy, a fault diagnosis method based on a stacked sparse autoencoder (SSAE) combined with a softmax classifier is proposed in this paper. Firstly, SSAE is used to extract frequency-domain features of vibration signals. Then, the improved K-fold cross-validation is employed to obtain the features' pre-train set, train set, and test set. Finally, the SSAE- model is constructed via the pre-train set, while the tuned model is built via the train set. The model performance is evaluated based on Accuracy, Macro-precision, Macro-recall, and Macro-F1score. The proposed model is validated by Case Western Reserve University and XJTU-SY data with 99.15% and 100% accuracy, respectively.

As a new energy harvesting technology, triboelectric nanogenerators are widely used for vibration mechanical energy harvesting. However, the current schemes ignore the composite characteristics of vibration, with problems such as utilization and low collection efficiency. In this paper, a random resonance cantilever beam triboelectric nanogenerator (RCB-TENG) with dual-mode coupled is presented, the working mode is a coupling form of in-plane sliding and vertical contact-separation that can effectively collect complex vibration energy in transverse and longitudinal directions. The influences of the structural parameters of the RCB-TENG and different dielectric materials on the output performance are systematically investigated. The single vibration module achieved a power density of 463.56 mW/m 2 and a transfer charge of 10.7 μC at a vibration frequency of 46 Hz, an increase in power density, and a transfer charge of 4.94 and 3.82 times, respectively, compared to the conventional contact-separation mode. Finally, the RCB-TENG was tested in practice, and it was observed that nine 1 W commercial LED bulbs and 500 5 mm diameter LED lamps were successfully lit. This work offers new ideas for distributed energy harvesting technologies and holds broad promise in the field of energy harvesting from wind, water, wave, and random vibrations caused by mechanical energy.

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