基于局部权重角度离群算法的球磨机故障诊断

Fault Diagnosis of Ball Mill Based on LW-FastVOA Algorithm

矿用球磨机故障诊断是典型的复杂工业过程多维数据挖掘问题,难点在于多维数据挖掘准确度低且算法时间复杂度高,为此提出基于局部权重角度离群算法(LW-FastVOA)的数据挖掘方法.首先采用角度离群算法(ABOD)在多维空间中衡量数据点的离群度,并针对ABOD算法时间复杂度算法较高问题,采用FastVOA算法将数据集正交投影于随机超平面上,利用AMS草图推导出各点的方差,归纳将其投影到随机超平面上作为频矩参数,算法的时间复杂度降低.最后提出LWFastVOA算法增加数据点的局部权重,降低多聚簇间离群点遗漏率,从而提高了算法精度.仿真实验结果表明,所提出的LW-FastVOA算法提高了精确率与召回率,验证了算法的有效性和可行性.

Fault diagnosis of a ball mill is a typical multi-dimensional data mining problem in complex industrial processes. The difficulty of this problem lies in the low accuracy and high time complexity of multi-dimensional data mining. We propose a FastVOA with local weight( LW-FastVOA) to solve the problem. First,we apply the angle-based outlier detection( ABOD) to measure the outlier factor. Then,we use the FastVOA algorithm to reduce the time complexity of ABOD. The algorithm projects the dataset on random hyperplanes orthogonally and then derives the variance with AMS sketches. The frequency moments of the points are approximated by summarizing and projecting on the random hyperplanes. Finally,we propose the LW-FastVOA algorithm to add the local weight of the data points and reduce the omission rate of outliers among clusters to improve the accuracy. Simulation results show that the LW-FastVOA algorithm improves the precision rate and recall rate in fault diagnosis,thereby verifying the effectiveness and feasibility of the algorithm.