Fault Detection for Motor Drive Control System of Industrial Robots Using CNN-LSTM-based Observers
Tao Wang, Le Zhang, Xuefei Wang
Abstract
The complex working conditions and nonlinear characteristics of the motor drive control system of industrial robots make it difficult to detect faults. In this paper, a deep learning-based observer, which combines the convolutional neural network (CNN) and the long short-term memory network (LSTM), is employed to approximate the nonlinear driving control system. CNN layers are introduced to extract dynamic features of the data, whereas LSTM layers perform time-sequential prediction of the target system. In terms of application, normal samples are fed into the observer to build an offline prediction model for the target system. The trained CNN-LSTM-based observer is then deployed along with the target system to estimate the system outputs. Online fault detection can be realized by analyzing the residuals. Finally, an application of the proposed fault detection method to a brushless DC motor drive system is given to verify the effectiveness of the proposed scheme. Simulation results indicate the impressive fault detection capability of the presented method for driving control systems of industrial robots.