矿渣粉磨健康状态识别模型及系统设计

提出一种基于数据挖掘的装备健康状态识别模型,实现对矿渣粉磨系统的运行健康预测。首先,利用一种多算法综合的特征筛选方法对工况数据进行挖掘分析,确定影响设备运行健康状态的评估指标参数;其次,以健康运行状态评估指标为对象,开展系统运行工况状态聚类挖掘,分析历史样本运行数据中的健康工况模式分布,以此为依据定义工况的运行状态类别,建立健康工况模式库;然后,将实时运行数据与健康工况模式库比对,利用自回归积分滑动平均算法(auto-regressive integrated moving average,简称ARIMA)训练预测评估模型,对健康评估指标参数的变化趋势进行预测,获取系统健康状态评估结果;最后,基于上述模型开发了矿渣粉磨健康状态识别系统软件,并应用于某生产现场,验证了该模型及系统的有效性和实用性。

Health Status Recognition Model and System Design for Slag Grinding System

Slag grinding systems are complicated and are usually working under demanding environments. Long-term high-load operation could compromise the production of these systems, result in various malfunctions and raise the maintenance costs. In this study, with the purpose of predicting the operation conditions of slag grinding systems, a health pattern recognition system that based on data mining is proposed. Combining several algorithms, a feature filtering method is developed for analyzing the operating conditions, and for determining the indicators that affect the operations. Using healthy operating conditions as references, cluster analysis is carried out to discover the distribution of healthy conditions, and then set up a reference base. Comparing the operating data against the reference base, auto-regressive integrated moving average (ARIMA) algorithm is used to train the predicting model for forecasting the changing trends of the indicators. A corresponding software system is developed, and it is applied to real case study for proving the effectiveness and practicability of this method.