基于机器学习的产品质量在线智能监控方法

Online intelligent product quality monitoring method based on machine learning

为了提高产品质量的稳定性和可靠性,利用机器学习方法实现产品质量在线监控、在线优化和在线预设定,是钢铁企业目前亟待解决的关键技术.针对企业需求,提出基于软超球体算法的产品质量异常在线识别和异常原因诊断方法、基于流形学习的工艺参数在线优化方法和基于多变量统计过程控制的工艺规范制定方法.通过将上述方法进行系统集成,并利用工业互联网技术和大数据分析方法,研发了产品质量在线智能监控系统.目前该系统已在钢铁企业十余条生产线上推广应用,质量在线判定的准确率达到99.2%,在线检测时间不到0.1 s.

In recent years,Chinese iron and steel enterprises have mainly adopted the“sampling after the event”method to inspect the product quality before it leaves the factory.Due to the inability to achieve quality inspection for all products,customers often claim and return defective products,leading to major economic losses in steel enterprises.To improve the stability and reliability of product quality,the use of machine learning methods to realize the online monitoring,optimization,and preset of product quality is the key technology to be solved in iron and steel enterprises.Therefore,the online identification and diagnosis of abnormal product quality based on the soft hypersphere,online optimization of the process parameters based on manifold learning and process specification formulation based on the multivariate statistical process control were proposed.In this study,integrated methods of online monitoring,diagnosis,and optimization of product quality were proposed in which the abnormal point of the product quality by the soft hypersphere method,based on the support vector data description,was identified online,and the process parameters were diagnosed through the contribution chart.Optimizing in real time,abnormal process parameters via a local projective transformation of neighbor points was then achieved.The process parameter setting model based on manifold learning by multiclass neighborhoods to extract the manifold of process parameters was established.Meanwhile,the process specification model,based on the maximum inner rectangle of the soft hypersphere,was established to obtain an effective control interval of the process parameters.Through system integration with the proposed methods and using industrial internet technology and big data analysis methods,the system of intelligent online monitoring of product quality has been successfully developed.At present,the system has been applied to more than ten production lines in iron and steel enterprises.The accuracy rate of online quality determination is 99.2%,and the online detection time is less than 0.1 s.