首页> 外国专利> OPTIMIZING ACCURACY OF MACHINE LEARNING ALGORITHMS FOR MONITORING INDUSTRIAL MACHINE OPERATION

OPTIMIZING ACCURACY OF MACHINE LEARNING ALGORITHMS FOR MONITORING INDUSTRIAL MACHINE OPERATION

机译:优化机器学习算法精度监控工业机械运行

摘要

A system and method for a method for optimizing machine learning algorithms for monitoring industrial machine operation, including: monitoring at least one industrial machine behavioral model of at least one industrial machine; identifying at least a first ambiguous segment of the at least one industrial machine behavioral model having a first set of characteristics, and identifying a corrective solution recommendation associated with the first ambiguous segment; identifying at least a second ambiguous segment of the at least one industrial machine behavioral model having a second set of characteristics; determining if a similarity between the first set of characteristics and the second set of characteristics exceed a predetermined threshold; and updating a machine learning algorithm of the at least one industrial machine behavioral model to associate the corrective solution recommendation to the second ambiguous segment when it is determined that the similarity has exceed the predetermined threshold.
机译:一种用于优化用于监控工业机械操作的机器学习算法的方法的系统和方法,包括:监测至少一个工业机器的至少一个工业机器行为模型;识别至少一个具有第一组特性的至少一个工业机器行为模型的第一模糊段,并识别与第一模糊段相关的校正解决方案推荐;识别至少一个具有第二组特征的至少一个工业机器行为模型的第二模糊段;确定第一组特征和第二组特征之间的相似性是否超过预定阈值;并更新至少一个工业机器行为模型的机器学习算法,以便在确定相似度超过预定阈值时将校正解决方案推荐与第二模糊段相关联。

著录项

  • 公开/公告号US2021158220A1

    专利类型

  • 公开/公告日2021-05-27

    原文格式PDF

  • 申请/专利权人 SKF AI LTD.;

    申请/专利号US202117165432

  • 发明设计人 DAVID LAVID BEN LULU;WASEEM GHRAYEB;

    申请日2021-02-02

  • 分类号G06N20;G06N5/04;

  • 国家 US

  • 入库时间 2022-08-24 18:55:07

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