公开(公告)号：WO9749011A9

公开(公告)日：1999-04-15

申请号：PCT/US9710430

申请日：1997-06-13

Applicant: ARCH DEV CORP

Inventor： GROSS KENNETH C ,  WEGERICH STEPHAN ,  SINGER RALPH M ,  MOTT JACK

IPC: G05B13/02 ,  G05B9/02 ,  G05B13/04 ,  G05B23/02

CPC classification number: G05B23/0254 ,  G05B23/0262

Abstract: A system (10) and method for monitoring an industrial process and/or industrial data source (10). The system (10) includes a time correlation module (20), a training module (30), a system state estimation module (40) and a pattern recognition module (50). The system (10) generating time varying data sources, processing the data to obtain time correlation of the data (20), determining the range of data, determining learned states of normal operation (30) and using these states to generate expected values to identify a current state of the process closest to a learned, normal state (40); generating a set of modeled data, and processing the modeled data to identify a data pattern and generating an alarm (50) upon detecting a deviation from normalcy.

Abstract translation: 一种用于监测工业过程和/或工业数据源（10）的系统（10）和方法。 系统（10）包括时间相关模块（20），训练模块（30），系统状态估计模块（40）和模式识别模块（50）。 所述系统（10）产生时变数据源，处理数据以获得数据（20）的时间相关性，确定数据范围，确定正常操作的学习状态（30）并使用这些状态来产生期望值以识别 该过程的当前状态最接近学习的正常状态（40）; 生成一组建模数据，并且在检测到与正常偏差之后处理建模数据以识别数据模式并产生报警（50）。

公开(公告)号：WO9964962A9

公开(公告)日：2000-03-23

申请号：PCT/US9913054

申请日：1999-06-09

Applicant: ARCH DEV CORP

Inventor： GROSS KENNETH C ,  SINGER RALPH M ,  VAN ALSTINE ROLLIN G ,  WEGERICH STEPHAN W ,  YUE YONG

IPC: G21D3/04 ,  G06F15/18

CPC classification number: G21D3/04 ,  Y02E30/40

Abstract: A method and system for monitoring a process and determining its condition. Initial data is sensed, a first set of virtual data is produced by applying a system state (20) analyzation to the initial data, a second set of virtual data is produced by applying a neural network (40) analyzation to the initial data and a parity space (50) analyzation is applied to the first and second set of virtual data and also to the initial data to provide a parity space (50) decision about the condition of the process. A logic test (60) can further be applied to produce a further system decision about the state of the process.

Abstract translation: 一种用于监控过程并确定其状况的方法和系统。 感测初始数据，通过对初始数据应用系统状态（20）分析来产生第一组虚拟数据，通过对初始数据应用神经网络（40）分析产生第二组虚拟数据，并且 将奇偶校验空间（50）分析应用于第一和第二组虚拟数据以及初始数据，以提供关于处理条件的奇偶校验空间（50）决定。 可以进一步应用逻辑测试（60）来产生关于过程状态的进一步的系统决策。

公开(公告)号：EP0906593A4

公开(公告)日：1999-09-15

申请号：EP97930046

申请日：1997-06-13

Applicant: ARCH DEV CORP

Inventor： GROSS KENNETH C ,  WEGERICH STEPHAN ,  SINGER RALPH M ,  MOTT JACK

IPC: G05B13/02 ,  G05B9/02 ,  G05B13/04 ,  G05B23/02

CPC classification number: G05B23/0254 ,  G05B23/0262

Abstract: A system (10) and method for monitoring an industrial process and/or industrial data source (10). The system (10) includes a time correlation module (20), a training module (30), a system state estimation module (40) and a pattern recognition module (50). The system (10) generating time varying data sources, processing the data to obtain time correlation of the data (20), determining the range of data, determining learned states of normal operation (30) and using these states to generate expected values to identify a current state of the process closest to a learned, normal state (40); generating a set of modeled data, and processing the modeled data to identify a data pattern and generating an alarm (50) upon detecting a deviation from normalcy.

Abstract translation: 一种用于监视工业过程和/或工业数据源（10）的系统（10）和方法。 系统（10）包括时间相关模块（20），训练模块（30），系统状态估计模块（40）和模式识别模块（50）。 系统（10）生成随时间变化的数据源，处理数据以获得数据（20）的时间相关性，确定数据范围，确定正常操作（30）的学习状态并使用这些状态来生成期望值以识别 该过程的当前状态最接近学习的正常状态（40）; 生成一组建模数据，并处理该建模数据以识别数据模式，并在检测到与正常偏差时生成警报（50）。

公开(公告)号：EP0855061A4

公开(公告)日：1999-01-13

申请号：EP96936263

申请日：1996-10-08

Applicant: ARCH DEV CORP

Inventor： GROSS KENNETH C ,  SINGER RALPH M

IPC: G05B19/418 ,  G06F17/14

CPC classification number: G05B19/4184 ,  G05B2219/34048 ,  G05B2219/37325 ,  G05B2219/37326 ,  Y02P90/14 ,  Y10S706/906

Abstract: A method and system (110) for monitoring both an industrial process and a sensor (104). The method and system include determining a minimum number of sensor pairs needed to test the industrial process as well as the sensor (104) for evaluating the state of operation of both. After obtaining two signals associated with one physical variable, a difference function is obtained by determining the arithmetic difference between the pair of signals over time. A frequency domain transformation is made of the difference function to obtain Fourier modes describing a composite function. A residual function is obtained by subtracting the composite function from the difference function and the residual function (free of nonwhite noise) is analyzed by a statistical probability ratio test.

公开(公告)号：CA2234452A1

公开(公告)日：1997-04-17

申请号：CA2234452

申请日：1996-10-08

Applicant: ARCH DEV CORP

Inventor： SINGER RALPH M ,  GROSS KENNETH C

IPC: G05B19/418 ,  G06F17/14 ,  G06F15/18

Abstract: A method and system (110) for monitoring both an industrial process and a sensor (104). The method and system include determining a minimum number of sensor pairs needed to test the industrial process as well as the sensor (104) for evaluating the state of operation of both. After obtaining two signals associated with one physical variable, a difference function is obtained by determining the arithmetic difference between the pair of signals over time. A frequency domain transformation is made of the difference function to obtain Fourier modes describing a composite function. A residual function is obtained by subtracting the composite function from the difference function and the residual function (free of nonwhite noise) is analyzed by a statistical probability ratio test.

公开(公告)号：DE69631594D1

公开(公告)日：2004-03-25

申请号：DE69631594

申请日：1996-10-08

Applicant: ARCH DEV CORP

Inventor： GROSS KENNETH C ,  SINGER RALPH M

IPC: G05B19/418 ,  G06F17/14 ,  G05B9/03 ,  G05B23/02

Abstract: A method and system for monitoring both an industrial process and a sensor. The method and system include determining a minimum number of sensor pairs needed to test the industrial process as well as the sensor for evaluating the state of operation of both. The technique further includes generating a first and second signal characteristic of an industrial process variable. After obtaining two signals associated with one physical variable, a difference function is obtained by determining the arithmetic difference between the pair of signals over time. A frequency domain transformation is made of the difference function to obtain Fourier modes describing a composite function. A residual function is obtained by subtracting the composite function from the difference function and the residual function (free of nonwhite noise) is analyzed by a statistical probability ratio test.

公开(公告)号：CA2234452C

公开(公告)日：2004-02-24

申请号：CA2234452

申请日：1996-10-08

Applicant: ARCH DEV CORP

Inventor： SINGER RALPH M ,  GROSS KENNETH C

IPC: G05B19/418 ,  G06F17/14 ,  G06F15/18

Abstract: A method and system (110) for monitoring both an industrial process and a sensor (104). The method and system include determining a minimum number of sensor pairs needed to test the industrial process as we ll as the sensor (104) for evaluating the state of operation of both. After obtaining two signals associated with one physical variable, a difference function is obtained by determining the arithmetic difference between the pair of signals over time. A frequency domain transformation is made of the difference function to obtain Fourier modes describing a composite function. A residual function is obtained by subtracting the composite function from the difference function and the residual function (free of nonwhite noise) is analyzed by a statistical probability ratio test.

公开(公告)号：AU4431999A

公开(公告)日：1999-12-30

申请号：AU4431999

申请日：1999-06-09

Applicant: ARCH DEV CORP

Inventor： GROSS KENNETH C ,  SINGER RALPH M ,  ALSTINE ROLLIN G VAN ,  WEGERICH STEPHAN W ,  YUE YONG

IPC: G21D3/04 ,  G06F15/18

Abstract: A method and system for monitoring a process and determining its condition. Initial data is sensed, a first set of virtual data is produced by applying a system state analyzation to the initial data, a second set of virtual data is produced by applying a neural network analyzation to the initial data and a parity space analyzation is applied to the first and second set of virtual data and also to the initial data to provide a parity space decision about the condition of the process. A logic test can further be applied to produce a further system decision about the state of the process.

公开(公告)号：ES2205244T3

公开(公告)日：2004-05-01

申请号：ES97930046

申请日：1997-06-13

Applicant: ARCH DEV CORP

Inventor： GROSS KENNETH C ,  WEGERICH STEPHAN ,  SINGER RALPH M ,  MOTT JACK

IPC: G05B13/02 ,  G05B9/02 ,  G05B13/04 ,  G05B23/02

Abstract: A system and method for monitoring an industrial process and/or industrial data source. The system includes generating time varying data from industrial data sources, processing the data to obtain time correlation of the data, determining the range of data, determining learned states of normal operation and using these states to generate expected values, comparing the expected values to current actual values to identify a current state of the process closest to a learned, normal state; generating a set of modeled data, and processing the modeled data to identify a data pattern and generating an alarm upon detecting a deviation from normalcy.

公开(公告)号：CA2257881C

公开(公告)日：2004-02-10

申请号：CA2257881

申请日：1997-06-13

Applicant: ARCH DEV CORP

Inventor： SINGER RALPH M ,  MOTT JACK ,  WEGERICH STEPHAN ,  GROSS KENNETH C

IPC: G05B13/02 ,  G05B9/02 ,  G05B13/04 ,  G05B23/02

Abstract: A system (10) and method for monitoring an industrial process and/or industrial data source (10). The system (10) includes a time correlation module (20), a training module (30), a system state estimation module (40) and a pattern recognition module (50). The system (10) generating time varying data sources, processing the data to obtain time correlation of the data (20), determining the range of data, determining learned states of normal operation (30) and using these states to generate expected values to identify a current state of the process closest to a learned, normal state (40); generating a set of modeled data, and processing the modeled data to identify a data pattern and generating an alarm (50) upon detecting a deviation from normalcy.