As one of the most common gear failure modes, tooth wear can produce nonlinear modulation sidebands in the vibration frequency spectrum. However, limited research has been reported in monitoring the gear wear based on vibration due to the lack of tools which can effectively extract the small sidebands. In order to accurately monitor gear wear progression in a timely fashion, this paper presents a gear wear condition monitoring approach based on vibration signal analysis using the modulation signal bispectrum-based sideband estimator (MSB-SE) method. The vibration signals are collected using a run-to-failure test of gearbox under an accelerated test process. MSB analysis was performed on the vibration signals to extract the sideband information. Using a combination of the peak value of MSB-SE and the coherence of MSB-SE, the overall information of gear transmission system can be obtained. Based on the amplitude of MSB-SE peaks, a dimensionless indicator is proposed to assess the effects of gear tooth wear. The results demonstrated that the proposed indicator can be used to accurately and reliably monitor gear tooth wear and evaluate the wear severity.

Gears are important components of the transmission system. Tooth wear and bearing clearance are significant factors affecting the dynamics of the gear system. In order to reveal the effects of gradual wears and bearing clearance on the gear system dynamics, a six-degrees-of-freedom bending-torsion coupled model of gear-rotor-bearing which considers surface wear, bearing clearance and backlash is established. The Rung-Kutta method is used to solve the nonlinear dynamic system, and the dynamic responses of the system are obtained. The results show that the time-varying mesh stiffness decreases with the tooth surface from the unworn phase to severe wear phase. At the same time, the change of the mesh stiffness in the double-tooth mesh area and single-tooth area are different. Moreover, the amplitude of the X-displacement, Y-displacement and relative gear mesh displacement will be enlarged slightly with the increase of wear depth or bearing clearance. By analyzing variation tendency in the frequency domain, the different order harmonics show the different change characteristic with the variation of the wear phases or bearing clearances. This study provides a theoretical basis for improving the transmission performance and the selection of the bearing clearances in the gear system.

Camera-Light Detection and Ranging (LiDAR) calibration is a crucial aspect of perception systems and sensor fusion in various fields, facilitating the fusion of data from cameras and LiDAR sensors for applications such as autonomous vehicles, robotics, and augmented reality. In this paper, we presented a novel multi-modal fusion method that introduces an efficient camera-LiDAR joint calibration technique using a simple checkerboard. Our method estimates the 3D rigid transformation of the camera system with respect to the LiDAR frame by establishing 2D-to-3D point correspondences for geometric calibration. We improved the idea of the traditional Perspective-N-Point algorithm by employing point cloud segmentation and clustering to re-match the checkerboard. When compared with the calibration methods in the Autoware and MATLAB calibration toolbox, the reprojection error was improved by 38.13% and 58.30% respectively. The average translation error was improved by 78.26% and 51.61% respectively, while the average rotation error is improved by 75% and 60.78% respectively, which has a great improvement in all three areas. At the same time, we propose a new evaluation metric for joint calibration results based on reprojected images: pixel-level evaluation index to reflect the accuracy of the joint calibration of the camera and LiDAR. An automatic calibration software has been developed for the calibration of the camera’s intrinsic parameters as well as for the joint calibration of the camera-LIDAR extrinsic parameters. We have extensively validated our algorithm using the Intel RealSense Depth D435 Camera and LeiShen C16-151B LiDAR.

Tooth wear is one of the most common faults in gear transmission systems. However, due to its limited influences on gear dynamics, the condition monitoring of gear wear experiences more challenges. To investigate the relationship between tooth wear and gear dynamics, this study develops an eight-degree of freedom dynamic model which takes into account the gear mesh impact induced by tooth wear. In this model, the gear mesh impact force induced by tooth wear was calculated as an extra input excitation for the gear dynamic model. Besides, a comparative study was carried out to explore the real reason that causes the poor dynamic behaviour due to the wear from two points of view: 1) the reduction in gear mesh stiffness; 2) the increase in gear mesh impact force. The simulation results show that the wear causes a slight reduction in gear mesh stiffness, thus leading to the decrease in the amplitude of gear mesh frequency. This is not consistent with the phenomena observed in the lab experiment which shows the increase in the gear vibrations. The second simulation of impact force demonstrates that the wear causes an increase in the impact force which further leads to the increase in the amplitude of gear mesh frequency. This study investigates the cause for the deterioration in gear dynamics induced by gear wear, which can benefit the early condition monitoring and fault diagnosis of the gear tooth wear.