The frequent occurrence of motor faults has been a great disturbance to the development of production in various fields. Traditional fault diagnosis methods primarily use 1-D data or 2-D data. However, 3-D data hold significant promise for motor fault diagnosis due to its voluminous data and unique spatial information. This article aims to explore a motor fault diagnosis method leveraging 3-D data. Nonetheless, motor fault 3-D data exhibit the limitation of lacking geometric structure. To address this limitation, this article proposes a fault diagnosis method named SP-PointCNN. This method uses the proposed spherical projection (SP) method to mitigate the limitations present in the 3-D data of the motor. Meanwhile, a neural network based on PointCNN is constructed to enable the utilization of 3-D data in fault diagnosis. A series of experiments demonstrated the validity of the proposed method. After tested, the diagnosis accuracy of SP-PointCNN can reach 99.36%.
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