基于粗糙集与支持向量机的加工过程异常检测

Processing anomaly detection based on rough set and support vector machine

为提高加工过程异常模式检测的自动化程度,在建立控制图数学描述的基础上,利用蒙特卡洛法构建了控制图数据集,研究了基于邻域粗糙集的控制图时域特征约简方法,提出了基于支持向量机的控制图异常模式识别模型。通过仿真实验,使用遗传算法优化了异常识别模型的主要参数,并对不同核函数、不同分类模型的识别精度进行了分析与对比。通过实际生产数据测试验证了所构建模型的有效性与可用性。

To improve the automation of anomaly detection in machining process, the original datasetsbased on math- ematic description of control chart were constructed by Monte-Carlo method, a novel method baaed on neighborhood rough set was introduced to reduce the control chart time domain features, and an abnormal pattern recognition mod- el of control chart based on support vector machine was proposed. Through the simulation experiment, the main i- dentification model parameters were optimized with genetic algorithm, and the recognition accuracy of different ker- nel functions and classification models were analyzed and compared. The effectiveness and availability of the pro-Dosed model were verified with the use of actual production data.