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Anomaly detection and neural network algorithms for PST hydrocyclone condition monitoring

机译:异常检测和神经网络算法PST水力旋流性能监测

摘要

A system includes a learning network having a signal processor configured to: receive learned signaling containing information about representative samples of conditions related to operating states of a hydrocyclone and characterized as learned samples of each condition when the learning network is trained, and raw signaling containing information about raw samples containing information about the current operation of the hydrocyclone; and determine corresponding signaling containing information about an operating state of the current operation of the hydrocyclone based upon a comparison of the learned signaling and the raw signaling.
机译:系统包括具有信号处理器的学习网络,该信号处理器被配置为:接收有关涉及水力旋流器的操作状态的有关条件的代表性样本的信息的学习信令,并且当训练学习网络时的每个条件的学习样本,以及包含信息的原始信令 关于包含有关水力旋流器目前操作的信息的原料样本; 基于学习信令和原始信号传导的比较,确定包含关于水力旋流器的当前操作的操作状态的相应信令。

著录项

  • 公开/公告号US11125593B2

    专利类型

  • 公开/公告日2021-09-21

    原文格式PDF

  • 申请/专利权人 CIDRA CORPORATE SERVICES LLC;

    申请/专利号US201716334895

  • 发明设计人 MICHAEL A. DAVIS;

    申请日2017-09-21

  • 分类号G01F1/32;B04C9;B04C11;G06N3/02;

  • 国家 US

  • 入库时间 2022-08-24 21:09:31

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