Paper Title
Intelligent Monitoring and Fault Prediction System for Drives of an Industrial, 6-Axis Robot

This paper presents the results of an intelligent system for monitoring and prediction of defects in the drives of an industrial robot. A neural algorithm based on a convolution neural network has been implemented to detect robot malfunctions. The algorithm receives input data thanks to sensors mounted in the robot's drives. The collected data - current and torque - are stored in the cloud. This makes it possible to apply advanced data processing algorithms and, as a result, to detect early failures and wear of the drive. The article presents the architecture of the implemented neural network, how the data is prepared for learning and the results of learning the algorithm. Keywords - Artificial Intelligence, Predictive Maintenance, Industry 4.0, Convolutional Neural Network