Generating synthetic data for data-driven solutions via a digital twin for condition monitoring in machine tools

Abstract

Machine tools (MT) are critical to modern manufacturing. They allow precision manufacturing of complex components at high volumes. MTs are large capital investments that require maintenance and monitoring to ensure they remain in good working condition. To best achieve reliability and high performance it is necessary to implement condition monitoring, fault detection and predictive maintenance. One solution for implementing these is by utilizing data-driven methods such as neural networks. One issue with any data-driven method is that they require large quantities of labeled data. This is especially difficult for fault detection applications as faults tend to be rare, and as a result, the datasets tend to be very imbalanced. One emerging technology that can be implemented to solve this issue is the digital twin (DT). DTs provide a solution for data collection, modeling, simulation, and smart services. One way that DTs can be used is to generate synthetic data which can be used for various data-driven methods. This data can be validated on a test bench to ensure its accuracy before implementation in production. Synthetic data generated from the DT model can be used to create a dataset for various condition monitoring DT services. This study involved the use of simulation software to generate synthetic data which was used to implement a fault detection algorithm for preload loss monitoring. This method has been demonstrated to be effective at identifying the current operating conditions of the system. This method shows promise to improve reliability and performance in MTs, and could be adapted to condition monitoring in other systems such as vehicles, buildings, and power generation.