Multi-Parameter Fusion for Wireless Low-Power Condition Monitoring of Ring Rolling Machines

The ring rolling machine is a key piece of equipment in modern manufacturing for producing multi-specification, multi-form annular mechanical components, and seamless ring forgings. Accurate condition monitoring is essential for ensuring production efficiency and product quality. However, existing vibration monitoring methods are limited by single-parameter analysis, susceptible to signal interference from the forging process, and lack theoretical modeling for multi-parameter data fusion and optimization mechanisms for low-power wireless communication, leading to frequent false alarms. This paper focuses on the core components of the drive and transmission system of the ring rolling machine, designing a condition monitoring system based on multi-parameter condition assessment. It proposes a strategy for fusing core indicators by compressing vibration RMS and kurtosis parameters to reduce data transmission volume, achieving the goal of low-power wireless transmission. Additionally, a fuzzy comprehensive evaluation model is developed using a combined weighting method of analytic hierarchy process (AHP) and grey relational analysis (GRA), accurately classifying equipment degradation levels into “Good,” “Normal,” “Warning,” and “Severe.” This strategy enables comprehensive assessment of mechanical and electrical fault states. Experimental verification shows that the system outperforms traditional methods in accurately identifying early degradation of key components, such as gearboxes, and confirming gear wear, providing a theoretically rigorous and practically feasible solution for real-time condition monitoring in complex industrial scenarios.