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针对状态参数随时间流逝变化的复杂系统,提出一种基于大数据、异步信息融合的多尺度状态监测方法,构建对象状态参数反映设备运行状态。通过对某机组锅炉辐射受热面灰污程度检测的实例分析,利用所提出的多尺度状态监测方法,构建了污染度指数,消除了煤破碎变化等噪声对状态监测结果的干扰,有效反映了受热面积灰程度,取得了良好的效果。
Aiming at the complicated system with changing state parameters over time, a multiscale state monitoring method based on big data and asynchronous information fusion is proposed. The object state parameters are constructed to reflect the running status of equipment. Through the example analysis of detecting the degree of ash pollution on the heating surface of a unit boiler, the proposed pollution degree index is constructed by using the proposed multi-scale condition monitoring method, which can eliminate the interference of the noise such as coal breakage changes on the condition monitoring results, effectively reflecting the heat Area gray level, and achieved good results.