关键词: 辊压成形, 数字孪生, 故障预测, 多参数融合, 阈值模型

Abstract:

Roller forming is a typical continuous forming process for large-scale production of aviation structural parts. Its operation involves multiple key parameters with strong coupling, such as roller speed, forming temperature and roller pressure. The fault evolution is hidden and the early signs are not obvious. Therefore, a fault prediction method based on multi-parameter threshold fusion is proposed and integrated into the digital twin system of roll forming equipment. A three-module intelligent assessment system consisting of single parameter anomaly score, multi-parameter coupling effect score and score fusion decision is constructed, and a hierarchical threshold early warning mechanism is established to realize accurate perception of equipment running status and quantification of fault risk. Among them, the single parameter anomaly scoring module divided the three-level interval for the core parameter, and quantified the parameter deviation degree through the piecewise function. The multi-parameter coupling effect module introduces coupling rules with physical significance, such as pressure/speed ratio monitoring, low-temperature/high pressure, and low-speed/high pressure, to identify composite anomalies from the perspective of a single parameter. The score fusion decision module linearly fuses each abnormal score with adjustable weight to generate a comprehensive failure probability, and combines with the dual condition trigger decision logic to realize the hierarchical early warning of equipment. Relying on the hierarchical architecture of the digital twin system, the model realizes real-time parameter acquisition, online calculation of the whole process and result visualization, and forms a closed-loop operation and maintenance process. The experimental results show that the proposed method has high prediction accuracy and good engineering adaptability in the multi-condition test set, and the performance is significantly better than the traditional single parameter threshold method. It can effectively reduce false positives and false negatives, and provide reliable technical support for predictive maintenance of roll forming equipment.

Key words: roll forming, digital twin, fault prediction, multi-parameter fusion, threshold model