Self-Supervised Transfer Learning for Remote Wear Evaluation in Machine Tool Elements With Imaging Transmission Attenuation

The Industrial Internet of Things (IIoT) has significantly advanced traditional industrial systems, especially in facilitating remote monitoring and predictive maintenance for computer numerical control (CNC) machines. Ball screw drives (BSDs), crucial in CNC machining, require regular upkeep, often challenged by environmental influences and the limitations of wired sensor-based diagnostic procedures. Wireless sensors offer a cost-effective solution but struggle with data integrity during transmission, impacting remote wear evaluation of BSDs. Current recovery methods are not always adequate, often relying on extensive historical data and suffering from accumulating errors. Addressing these limitations, a novel self-supervised transfer learning (SSTL) model is proposed for remote wear assessment of machine tool components. This model integrates an image capture module into CNC surveillance systems and employs a LocalMIM module, which is pretrained and fine-tuned to adapt to various domains, especially for image quality deterioration during data transmission. The SSTL model is designed to function effectively despite significant pixel data loss, eliminating the need for historical data for image restoration. This innovation is particularly adept at evaluating wear in environments with compromised image transmission, providing a robust predictive maintenance strategy for BSDs that is less affected by data loss, aiding a pressing industrial requirement for consistent and dependable remote monitoring solutions.