矫直机的自动故障预报技术

Automatic Early Warning Technology for Straightener

由于人们无法充分理解生产设备的行为特性,缺乏有效的预测模型来监测设备劣化的变化趋势,这成为解决故障预报问题的主要障碍。本文将采用k均值聚类和支持向量机预测方法,实现了对矫直机轧制力状态趋势的预测和故障预报。

As the behaviors of production equipment are hard to fully understand, there is no effective prediction model to monitor the changing trend of equipment degradation, which becomes the major obstacle to fault forecast. In the article, k-means clustering and SVM prediction are used to achieve prediction of the rolling force state and fault early warning of strip straightener.