Abstract
To solve the problem of low accuracy in traditional fault diagnosis methods, a novel method of combining generalized frequency response function(GFRF) and convolutional neural network(CNN) is proposed. In order to accurately characterize system state information, this paper proposed a variable step size least mean square (VSSLMS) adaptive algorithm to calculate the second-order GFRF spectrum values under normal and fault states; In order to improve the ability of fault feature extraction, a convolution neural network (CNN) with gradient descent learning rate and alternate convolution layer and pooling layer is designed to extract the fault features from GFRF spectrum. In the proposed method, the second-order GFRF spectrum of each state of Permanent Magnet Synchronous Motor (PMSM) is obtained by VSSLMS; Then, the two-dimension GFRF spectrum, which is regarded as the gray value of the image,will be further transformed into image. Finally, the CNN is trained with learning rate by gradient descent way to realize the fault diagnosis of PMSM. Experimental results indicate that the accuracy of proposed method is 98.75%, which verifies the reliability of the proposed method in application of PMSM fault diagnosis.
Highlights
Permanent magnet synchronous motor (PMSM) is widely used in industrial robot drive system because of its small size, light weight and simple structure
Nonlinear frequency spectrum can well explain the characteristics of system, the accuracy of fault diagnosis can be improved by using it to characterize the state information, and achievements have been made in the field of fault diagnosis which can be shown in Refs. [10]-[15].up to now, it is very rare to study the fault diagnosis for permanent magnet synchronous motor (PMSM) based on generalized frequency response function (GFRF) spectrum
A new method of fault diagnosis for permanent magnet synchronous motor(PMSM) is proposed. It combines the nonlinear frequency spectrum based on GFRF and convolution neural network(CNN) to improve the diagnosis accuracy
Summary
Permanent magnet synchronous motor (PMSM) is widely used in industrial robot drive system because of its small size, light weight and simple structure. In order to solve the problem of fault feature extraction is not deep enough and fault diagnosis accuracy is low, a new fault diagnosis method for permanent magnet synchronous motor (PMSM) combining non-linear spectrum based on generalized frequency response function (GFRF) and convolution neural network(CNN) is proposed. In this method, CNN network has strong ability of feature extraction and classification, but the input network is required to be the form of image, of all nonlinear spectrum based on Volterra kernel, only the secondorder GFRF spectrum can meet the input requirement of CNN.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.