Intelligent monitoring of linear stages with ensembles of improved LeNET DCNN and random forest classifiers

The linear stages are the most critical component of machine tools and additive manufacturing equipment. The accuracy of the linear stages directly affects the quality of the parts produced. Misalignment is a common problem in the linear stages. This paper presents a sensorless approach for detecting misalignment by monitoring the motor current. A linear stage was designed to simulate various angular misalignment problems between the ball screw and the motor shaft. The sensorless current-based method monitored the motor current at the Programmable Logic Controller (PLC) to detect the misalignment of the linear stage. Different forces were applied to the linear stage under different misalignment conditions. The acquired signal was processed using Continuous Wavelet Transform (CWT). The Lenet DCNN (Deep Convolutional Neural Network) model structure was improved by hyper-parametertuning and ensemble. The ensemble method combined the Convolutional Neural Network (CNN) model with a random forest (RF) classifier. The developed anomalydetection system was trained when different forces, with and without misalignment, were applied. The results showed that the proposed method was feasible for estimating the misalignment even when different external forces were applied to the linear stage.