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  • Fault diagnosis of rotation vector reducer for industrial robot based on a convolutional neural network
    Yang, Shuai ; Luo, Xing ; Li, Chuan, strojnik
    As a key component of a mechanical drive system, the failure of the reducer will usually cause huge economic losses and even lead to serious casualties in extreme cases. To solve this problem, a ... two-dimensional convolutional neural network (2D-CNN) is proposed for the fault diagnosis of the rotation vector (RV) reducer installed on the industrial robot (IR). The proposed method can automatically extract the features from the data and reduce the connections between neurons and the parameters that need to be trained with its local receptive field, weight sharing, and subsampling features. Due to the aforementioned characteristics, the efficiency of network training is significantly improved, and verified by the experimental simulations. Comparative experiments with other mainstream methods are carried out to further validate the fault classification accuracy of the proposed method. The results indicate that the proposed method out-performs all the selected methods.
    Source: Strojniški vestnik = Journal of mechanical engineering. - ISSN 0039-2480 (Vol. 67, no. 10, Okt. 2021, str. 489-500)
    Type of material - article, component part
    Publish date - 2021
    Language - english
    COBISS.SI-ID - 82633475

    Link(s):

    https://www.sv-jme.eu/sl/article/fault-diagnosis-of-rotation-vector-reducer-for-industrial-robot-based-on-convolutional-neural-network/
    Repository of the University of Ljubljana – RUL
    Digitalna knjižnica Slovenije - dLib.si
    DOI

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