中文摘要：

针对现有滚动轴承故障诊断技术中,存在输入属性冗余过多、故障识别率不高等缺点,提出了基于改进邻域粗糙集与S_Kohonen神经网络的故障诊断方法.由于传感器采集的故障信息大多为数值型数据且数据维数较大,文中引入邻域粗糙集理论并对基于邻域粗糖集的经典前向贪心算法进行改进,利用改进算法约简故障数据,大大减小了算法复杂度;对Kohonen神经网络进行改进,在其原有结构基础上添加输出层构成S_Kohonen神经网络,使其输出类别满足给定分类要求;分别采用前向贪心算法、改进算法约简故障数据,将约简前、后的故障数据分别输入S_Kohonen神经网络、BP神经网络识别滚动轴承故障状态,试验结果证明邻域粗糖集可有效消除属性之间的重复信息,改进算法提取故障属性信息更能反映故障状态的本质,S_Kohonen神经网络具有良好的故障识别能力,两者配合使用,改进邻域粗糙集——S_Kohonen神经网络模型具有很好的故障诊断能力.

英文摘要：

In view of the shortcomings of inputs, redundancy and low fault recognition rate existing in rol l ing bearings, fault diag-nosis technology, a method was proposed based on improved neighborhood rough set and s_kononen neural network. Since the fault information obtained by sensors mostly was numeric data and the dimension was always very large, the neighborhood rough set was imported, and the forward greedy algorithm was improved which was based on it. The improved method was applied to reduce the original failure data, which reduced the algorithm complexity greatly. The Kohonen neural network was improved, adding an output layer based on the original structure to compel the output classifications to meet the given requirements and making the unsupervised neural network into S_Kohonen neural network. Then forward greedy algorithm and improved algorithm were used to divide the original failure data into two parts： one is not reduced, the other is reduced. The two parts were extracted as the inputs of S_Kohonen neural network and BP neural network to identify fault state of roller bearing. The test result illustrates that neighborhood rough set can efficiently eliminate duplicate information between attributes, the fault information extracted by improved method can reflect even better the essence of fault state, and S_Kohonen neural network has a good ability to identify faults. Combined both approaches for applicaton, this model has a good capability of fault diagnosis.