煤矿机械

针对刮板输送机断链监测难以实现动态可视化的问题,提出一种基于特征事件驱动的数字孪生可视化监测系统。该系统以断链监测算法提取的特征事件作为数字孪生模型更新依据,构建数据采集、算法解算和界面渲染三级流水线异步解耦架构,实现链环姿态与三维模型的实时映射;结合故障快照黑匣子机制,对故障发生前后30 s关键数据进行自动缓存与锁定,支持断链过程回溯分析;采用Min-Max降采样算法,实现长时波形数据的高效显示。试验结果表明,该系统在200 Hz采样率下渲染负载降低约80%,画面帧率稳定保持在60帧/s,故障前后数据完整留存。该系统实现了断链监测结果与数字孪生模型的实时联动,为刮板输送机链条状态监测和故障分析提供了新的技术途径。

Aiming at the problem that it is difficult to realize dynamic visualization of chain breakage monitoring of scraper conveyor, a digital twin visual monitoring system based on feature event driven was proposed. This system uses the characteristic events extracted by the chain breakage monitoring algorithm as the basis for updating the digital twin model, and constructs a three-stage pipeline asynchronous decoupling architecture of data acquisition, algorithm calculation and interface rendering to realize the real-time mapping of the chain attitude and the three-dimensional model. Combined with the fault snapshot black box mechanism, the key data of 30 s before and after the fault is automatically cached and locked, which supports the backtracking analysis of the chain breaking process. Min-Max down-sampling algorithm is used to achieve efficient display of long-term waveform data. The test results show that the rendering load of this system is reduced by about 80 % at 200 Hz sampling rate,the frame rate maintains 60 frames/s stably, and the data before and after the fault is completely retained. This system realizes the real-time linkage between the chain breakage monitoring results and the digital twin model, and provides a new technical approach for condition monitoring and fault analysis of scraper conveyor chain.