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为了提高矿井风机故障诊断的准确率,使用UCI数据库的机械故障分析数据集进行分析。根据矿井风机的特点确定通频垂直振幅、基频垂直振动速率、振动频率是检测矿井风机故障的关键检测参数。因此,提出了采用粗糙集进行矿井风机故障数据挖掘的方法,通过对数据集中数据选择、离散、决策表的构建及约简方法的介绍,以期提取出矿井风机故障诊断的规则,实现矿井风机故障诊断专家系统规则库的建立。
In order to improve the accuracy of mine fan fault diagnosis, UCI database is used to analyze the mechanical fault analysis data set. According to the characteristics of mine fan to determine the vertical frequency of vertical pass, the vertical frequency of vertical vibration, vibration frequency is the key detection parameters to detect mine fan failure. Therefore, a method based on rough sets for mine fan fault data mining is proposed. By selecting and discretizing data sets, the construction of decision table and the introduction of reduction methods, the rules of mine fan fault diagnosis are extracted to realize mine fan failure Diagnosis expert system rule base establishment.