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Gears are fundamental components used to transmit power and motion in modern industry. Their health condition monitoring is crucial to ensure reliable operations, prevent unscheduled shutdowns, and minimize human casualties. From this standpoint, the present study proposed a one-dimensional convolutional neural network (1-D CNN) model to diagnose tooth root cracks for standard and asymmetric involute spur gears. A 6-degrees-of-freedom dynamic model of a one-stage spur gear transmission was established to achieve this end and simulate vibration responses of healthy and cracked (25%–50%–75%–100%) standard (20°/20°) and asymmetric (20°/25° and 20°/30°) spur gear pairs. Three levels of signal-to-noise ratios were added to the vibration data to complicate the early fault diagnosis task. The primary consideration of the present study is to investigate the asymmetric gears’ dynamic characteristics and whether tooth asymmetry would yield an advantage in detecting tooth cracks easier to add to the improvements it affords in terms of impact resistance, bending strength, and fatigue life. The findings indicated that the developed 1-D CNN model’s classification accuracy could be improved by up to 12.8% by using an asymmetric (20°/30°) tooth profile instead of a standard (20°/20°) design. Full article

Noncircular gear and curve face gear are collectively referred to as spatial noncircular gear. Combined with the transmission characteristics of spatial noncircular gear, a new classification design method of spatial noncircular gear continuously variable transmission (CVT) pattern was proposed, including two categories named addition type and multiplication type, with a total of five subcategories. Through the combination of the transmission ratio changing mechanism and transmission selection mechanism, it can realize the CVT. The overall transmission ratio depends on the phase angle between the spatial noncircular gear pairs and is independent of the input rotation angle. Firstly, the CVT principle and transmission ratio characteristics of each pattern were analyzed. Then the tooth profile classification design of spatial noncircular gear was carried out, and the general parametric design equations of the tooth surface with different tooth profiles were obtained. Finally, through simulation, the overall transmission ratio of each pattern was compared between theory and simulation, which verified the correctness of the CVT classification design method. The advantages and disadvantages of the two categories were analyzed. Moreover, through the transmission experiments of curve face gear pairs with different tooth profiles, the transmission ratios were compared between theory and experiment. Considering the influencing factors such as machining error, assembly error and measurement error, the experimental error is within a reasonable error range, which verified the correctness of the tooth profile classification design method. This study provides a new idea for the further research and application of spatial noncircular gear CVT. Full article