Experimental analysis identifies gear faults with high accuracy, suggesting convolutional neural networks can reduce environmental noise interference.
Among gearbox faults, gear tooth faults are dominant. Although the traditional vibration spectrum analysis method is the mainstream diagnostic method, it has limitations such as sensitivity to environmental noise and high sensor deployment cost. Based on the influence of the meshing stiffness of the faulty gear on the dynamic transmission error of the gear, this study innovatively proposes to use the transmission error to diagnose and identify typical gear tooth faults. This paper first calculates the time-varying stiffness of typical faulty gear teeth based on the potential energy method, and analyzes the influence of various faults and environmental noise on the dynamic transmission error signal and vibration signal by establishing a six-degree-of-freedom gear transmission dynamics model. Then, a gear transmission experimental platform is built to synchronously collect the vibration acceleration and transmission error data of the gearbox. The convolutional neural network is used to classify the data under different sample lengths and different noise intensities. The results show that the transmission error signal under the same conditions has a higher gear fault diagnosis accuracy. The proposed method can not only improve the accuracy and anti-interference of gear fault diagnosis but also reduce the deployment cost of signal acquisition, providing a new paradigm for gear condition monitoring.
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Wang et al. (2025) studied this question.
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