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公开(公告)号：US06775641B2
公开(公告)日：2004-08-10
申请号：US09802482
申请日：2001-03-09
IPC分类号： G06F1130
CPC分类号： G05B23/024 , G05B17/02
摘要： In a machine for monitoring an instrumented process or for analyzing one or more signals, an empirical modeling module for modeling non-linearly and linearly correlated signal inputs using a non-linear angular similarity function with variable sensitivity across the range of a signal input. A different angle-based similarity function can be chosen for different inputs for improved sensitivity particular to the behavior of that input. Sections of interest within a range of a signal input can be lensed for particular sensitivity.
摘要翻译： 在用于监测仪器化过程或用于分析一个或多个信号的机器中，经验建模模块用于使用在信号输入范围内具有可变灵敏度的非线性角度相似度函数来建模非线性和线性相关的信号输入。 可以为不同的输入选择不同的基于角度的相似度函数，以改善特定于该输入行为的敏感度。 可以对信号输入范围内的感兴趣部分进行镜像，以获得特定的灵敏度。
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公开(公告)号：US07308385B2
公开(公告)日：2007-12-11
申请号：US11203853
申请日：2005-08-15
IPC分类号： G06F15/00
CPC分类号： G05B23/0254 , G05B23/0281 , G06K9/00536 , G06K9/6284
摘要： A system for empirically diagnosing a condition of a monitored system. Estimates of monitored parameters from a model of the system provide residual values that can be analyzed for failure mode signature recognition. Residual values can also be tested for alert (non-zero) conditions, and patterns of alerts thus generated are analyzed for failure mode signature patterns. The system employs a similarity operator for signature recognition and also for parameter estimation. Failure modes are empirically determined, and precursor data is automatically analyzed to determine differentiable signatures for failure modes.
摘要翻译： 用于经验诊断受监视系统状况的系统。 来自系统模型的监测参数的估计提供可以分析故障模式签名识别的残差值。 还可以对警报（非零）条件测试剩余值，并分析故障模式签名模式，从而生成的警报模式。 该系统采用相似性算子进行签名识别，并进行参数估计。 故障模式是经验确定的，并且自动分析前体数据以确定故障模式的可区分签名。
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公开(公告)号：US07539597B2
公开(公告)日：2009-05-26
申请号：US10681888
申请日：2003-10-09
IPC分类号： G06F17/00
CPC分类号： G06K9/00536 , G05B23/0254 , G05B23/0281 , G06K9/6284 , Y10S707/99945 , Y10S707/99948
摘要： A system for empirically diagnosing a condition of a monitored system. Estimates of monitored parameters from a model of the system provide residual values that can be analyzed for failure mode signature recognition. Residual values can also be tested for alert (non-zero) conditions, and patterns of alerts thus generated are analyzed for failure mode signature patterns. The system employs a similarity operator for signature recognition and also for parameter estimation. Failure modes are empirically determined, and precursor data is automatically analyzed to determine differentiable signatures for failure modes.
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