Application of Generalized Frequency Response Functions and Improved Convolutional Neural Network to Fault Diagnosis of Heavy-duty Industrial Robot

2022 ◽  
Vol 73 ◽  
pp. 102228
Author(s):  
Lerui Chen ◽  
Jianfu Cao ◽  
Kui Wu ◽  
Zerui Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Frequency Response ◽  
Industrial Robot ◽  
Response Functions ◽  
Heavy Duty ◽  
Frequency Response Functions

A novel analysis method for fault diagnosis of hydro-turbine governing system

2017 ◽  
Vol 231 (2) ◽  
pp. 164-171 ◽  
Author(s):  
Xin Xia ◽  
Wei Ni ◽  
Yingjun Sang
Keyword(s):  
Fault Diagnosis ◽  
Frequency Response ◽  
Experimental Studies ◽  
Response Functions ◽  
Output Frequency ◽  
Frequency Response Functions ◽  
Hydro Turbine ◽  
Identification Method ◽  
Governing System ◽  
Diagnosis Method

The fault diagnosis of hydro-turbine governing system is important to the operation of the hydropower station and the stability of the power grid. In order to improve the diagnostic accuracy and efficiency, a novel fault diagnosis method based on nonlinear output frequency response functions and a novel identification method of nonlinear output frequency response functions have been proposed and applied to the problem of hydro-turbine governing system fault diagnostics. First, the nonlinear model of hydro-turbine governing system is built. And the fault diagnosis principles based on nonlinear output frequency response functions are also introduced. Then, the disadvantages of the traditional identification method are discussed, and a novel identification method is proposed for nonlinear output frequency response functions according to the operation characteristic of hydro-turbine governing system. Finally, simulation verification and experimental studies have been presented to demonstrate the accuracy and efficiency of the proposed fault diagnosis method. The results indicate that the proposed method is simple and practical for fault diagnosis of hydro-turbine governing system.

scholarly journals Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network

2021 ◽  
Vol 67 (10) ◽  
pp. 489-500
Author(s):  
Shuai Yang ◽  
◽  
Xing Luo ◽  
Chuan Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Industrial Robot ◽  
Rotation Vector ◽  
Fault Classification ◽  
Economic Losses ◽  
Network Training ◽  
Rv Reducer

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.

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