determines product surface quality and production efficiency. Hidden faults such as bearing overheating, grinding wheel wear, and abnormal vibration can lead to workpiece scrap in mild cases or cause equipment shutdown in severe cases, resulting in significant economic losses. Therefore, condition monitoring of grinding machines has become a critical link in the upgrade of smart manufacturing, enabling the transition from "post-event maintenance" to "predictive maintenance" through technological innovation, thereby laying a solid foundation for high-quality industrial development.

The core of grinding machine condition monitoring lies in the deep integration of multi-dimensional perception and intelligent analysis. The perception layer captures key data such as wheel wear, bearing temperature, and operational vibration in real time by deploying vibration sensors, force sensors, temperature sensors, and other devices. Among these, vibration signals serve as the primary monitoring metric, as they directly reflect faults like imbalance and cracks—normal vibration amplitude is merely 0.014g, while it can surge to 0.078g under imbalance conditions, showing a significant difference. The analysis layer leverages AI algorithms to enhance data value, with the random forest model achieving 98.5% classification accuracy for four wheel states, and Hilbert-Huang transform technology enabling precise extraction of wear characteristics, leaving no hidden faults undetected.

The practical application of intelligent monitoring systems has demonstrated significant industrial value. In the manufacturing scenario of inverter housings, Still AI's flexible polishing solution achieves 24-hour continuous operation through a fully closed-loop monitoring process of "sensing, analysis, decision-making, and execution," improving single-piece processing efficiency by threefold and achieving a surface consistency compliance rate of 98%. While the traditional five-minute rapid diagnosis method, reliant on manual experience, can initially identify issues such as bearing overheating and abnormal noises, the intelligent monitoring system enables millisecond-level response, enabling early warning of potential faults and reducing unplanned downtime by over 60%, significantly enhancing production continuity.

R1: "Edge computing" must be translated as edge computing.  Looking ahead, the condition monitoring of grinders is evolving toward a multi-technology collaboration approach. The integration of cloud computing and edge computing enables real-time data processing and remote control, while digital twin technology constructs virtual mappings for full lifecycle monitoring. 5G technology ensures high-speed transmission of massive data. This technological convergence will further enhance monitoring accuracy and versatility, applicable not only to conventional grinding equipment but also to complex workpiece grinding scenarios such as aircraft engine blades. As these technologies become more widespread, grinder condition monitoring will become a foundational standard in smart manufacturing, driving the precision manufacturing industry toward a new phase of higher quality and greater efficiency.