A Deep Comparison Network for Visual Prognosis of a Linear Slide

Linear Slides are important components widely adopted in the manufacturing sector, particularly automated production lines. Damage to the linear slide can cause abnormal machine vibration and result in production line failure. Common failure modes of the linear slide include ball slider wear and severe rail surface contamination or abrasion. Monitoring the condition of the linear slide is of great value and importance in the Industry 4.0 era. There is an emerging need for developing a prognosis method for the linear slide to prevent the unexpected breakdown of the production line. The most common online inspection method utilizes an accelerometer to monitor the system's vibration. However, as abnormal vibration is the result and not the cause of the slide damage, it does not serve well as a prognostic signal. This article proposed an innovative prognosis method by recruiting low-resolution cameras to monitor the rail surface condition. We conducted endurance tests on several linear slides and determined the end of life by the vibration measurements and the pre-compression values. We then annotated the rail surface images with the service percentages and formed the training set for a deep convolutional neural network (CNN). We design the CNN architecture as a dual-input comparison network that compares the initial image and the current image to predict the service percentage of the linear slide. The method appeared promising, judging by the preliminary test results; however, the prediction accuracy needs further improvements before actual application. The comparison network presented the advantage of generalization to various illumination conditions. The cost of low-resolution cameras is also much lower than accelerometers.