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1. 发明授权

US5301101A Receding horizon based adaptive control having means for minimizing operating costs 失效
标题翻译：基于地平线的自适应控制具有最小化操作成本的手段
公开(公告)号：US5301101A
公开(公告)日：1994-04-05
申请号：US47768
申请日：1993-04-14
申请人： J. Ward MacArthur , David A. Wahlstedt , Michael A. Woessner , Wendy K. Foslien
发明人： J. Ward MacArthur , David A. Wahlstedt , Michael A. Woessner , Wendy K. Foslien
IPC分类号： G05B13/02 , G05B13/04
CPC分类号： G05B13/026
摘要： Disclosed is an adaptive and predictive controller having self tuning capabilities. The receding horizon window and sample interval are determined by the tunning mechanism automatically, based on the relationship between the controlled and manipulated variables. The interval and window may be set by the user as well. Constraints on the manipulated and controlled variables at the end of the horizon window are accommodated automatically. In addition to providing servo regulatory control the invention teaches simultaneous minimization of operating costs.
摘要翻译：公开了具有自调整能力的自适应和预测控制器。根据控制和操纵变量之间的关系，自动调整机制确定后退水平窗口和采样间隔。间隔和窗口也可以由用户设置。水平窗口末端的受控和受控变量的约束被自动容纳。除了提供伺服调节控制之外，本发明还教导同时使操作成本最小化。

2. 发明授权

US07734451B2 System, method, and computer program for early event detection 有权
标题翻译：用于早期事件检测的系统，方法和计算机程序
公开(公告)号：US07734451B2
公开(公告)日：2010-06-08
申请号：US11583219
申请日：2006-10-18
申请人： J. Ward MacArthur , Wendy K. Foslien , Dinkar Mylaraswamy , Mohan N. Srinivasrao
发明人： J. Ward MacArthur , Wendy K. Foslien , Dinkar Mylaraswamy , Mohan N. Srinivasrao
IPC分类号： G06F17/10
CPC分类号： G06N7/02 , G05B13/0295 , G05B17/02
摘要： Various methods, devices, systems, and computer programs are disclosed relating to the use of models to represent systems and processes (such as manufacturing and production plants). For example, a method may include generating a first model and a second model using operating data associated with a system or process. The method may also include using the first and second models to predict one or more events associated with the system or process. The one or more events are predicted by generating one or more initial event predictions using the first model and adjusting the one or more initial event predictions using the second model. The first model may represent a Principal Component Analysis (PCA) model, and the second model may represent a Fuzzy Logic model.
摘要翻译：公开了关于使用模型来表示系统和过程（例如制造和生产设备）的各种方法，装置，系统和计算机程序。例如，方法可以包括使用与系统或过程相关联的操作数据来生成第一模型和第二模型。该方法还可以包括使用第一和第二模型来预测与系统或过程相关联的一个或多个事件。通过使用第一模型生成一个或多个初始事件预测并使用第二模型调整一个或多个初始事件预测来预测一个或多个事件。第一模型可以表示主成分分析（PCA）模型，第二模型可以表示模糊逻辑模型。

3. 发明授权

US07533070B2 Automatic fault classification for model-based process monitoring 有权
标题翻译：基于模型的过程监控的自动故障分类
公开(公告)号：US07533070B2
公开(公告)日：2009-05-12
申请号：US11442857
申请日：2006-05-30
申请人： Valerie Guralnik , Wendy K. Foslien
发明人： Valerie Guralnik , Wendy K. Foslien
IPC分类号： G06E1/00 , G06E3/00 , G06F15/18 , G06G7/00
CPC分类号： G05B23/0254 , G05B23/0281 , G06K9/622
摘要： A computer implemented method, system and program product for automatic fault classification. A set of abnormal data can be automatically grouped based on sensor contribution to a prediction error. A principal component analysis (PCA) model of normal behavior can then be applied to a set of newly generated data, in response to automatically grouping the set of abnormal data based on the sensor contribution to the prediction error. Data points can then be identified, which are indicative of abnormal behavior. Such an identification step can occur in response to applying the principal component analysis mode of normal behavior to the set of newly generated data in order to cluster and classify the data points in order to automatically classify one or more faults thereof. The data points are automatically clustered, in order to identify a set of similar events, in response to identifying the data points indicative of abnormal behavior.
摘要翻译：一种用于自动故障分类的计算机实现方法，系统和程序产品。可以根据对预测误差的传感器贡献自动分组一组异常数据。因此，正常行为的主成分分析（PCA）模型可以应用于一组新生成的数据，以响应于基于传感器对预测误差的贡献自动地对该组异常数据进行分组。然后可以识别数据点，这表示异常行为。这样的识别步骤可以响应于将正常行为的主成分分析模式应用于新生成的数据集，以便对数据点进行聚类和分类，以便自动分类其一个或多个故障。响应于识别指示异常行为的数据点，数据点被自动聚类，以便识别一组类似的事件。

4. 发明申请

US20080294374A1 PRINCIPAL COMPONENT ANALYSIS BASED FAULT CLASSIFICATION 有权
标题翻译：基于主成分分析的故障分类
公开(公告)号：US20080294374A1
公开(公告)日：2008-11-27
申请号：US12187975
申请日：2008-08-07
申请人： Valerie Guralnik , Wendy K. Foslien
发明人： Valerie Guralnik , Wendy K. Foslien
IPC分类号： G06F17/18
CPC分类号： G06K9/622 , G05B23/0254 , G06K9/6284
摘要： Principle Component Analysis (PCA) is used to model a process, and clustering techniques are used to group excursions representative of events based on sensor residuals of the PCA model. The PCA model is trained on normal data, and then run on historical data that includes both normal data, and data that contains events. Bad actor data for the events is identified by excursions in Q (residual error) and T2 (unusual variance) statistics from the normal model, resulting in a temporal sequence of bad actor vectors. Clusters of bad actor patterns that resemble one another are formed and then associated with events.
摘要翻译：原理分量分析（PCA）用于对过程进行建模，采用聚类技术对基于PCA模型的传感器残差代表事件的偏移进行分组。 PCA模型对正常数据进行培训，然后运行包含正常数据的历史数据和包含事件的数据。事件的不良演员数据通过来自正常模型的Q（残差）和T2（异常方差）统计的偏移来识别，导致不良演员向量的时间序列。形成彼此类似的不良演员模式的群集，然后与事件相关联。

5. 发明申请

US20090021517A1 Method and system for visualizing multivariate statistics 有权
标题翻译：多变量统计可视化的方法和系统
公开(公告)号：US20090021517A1
公开(公告)日：2009-01-22
申请号：US11897408
申请日：2007-08-30
申请人： Wendy K. Foslien
发明人： Wendy K. Foslien
IPC分类号： G06T11/20
CPC分类号： G06T11/206
摘要： A method, apparatus and module for visualizing multivariate statistical measurements. A processing system receives multivariate statistical output data, such as scores or contributions from a multivariate statistical model and renders the multivariate statistical output data as a function of time as a color map on a display. Each multivariate statistical output data can be obtained at each time sample and rendered as a corresponding color patch on the display. The color, height and width of the corresponding color patch can be adjusted to correspond to the magnitude of the output data, the length of time of the time sample and the number of the output data. Normalized scores/contributions at each time sample can be rendered as a corresponding color patch on the display in response to said Q statistic exceeding said predetermined threshold.
摘要翻译：一种用于可视化多变量统计测量的方法，装置和模块。处理系统接收多变量统计输出数据，例如来自多变量统计模型的分数或贡献，并将多变量统计输出数据作为时间的函数作为显示器上的颜色图。每个多变量统计输出数据可以在每个时间点获得，并在显示器上呈现为相应的色标。相应色块的颜色，高度和宽度可以根据输出数据的大小，时间采样的时间长度和输出数据的数量进行调整。响应于超过所述预定阈值的所述Q统计量，可以将每个时间样本的归一化分数/贡献呈现为显示器上的对应色标。

6. 发明授权

US07243048B2 Fault detection system and method using multiway principal component analysis 有权
标题翻译：故障检测系统和使用多路主成分分析的方法
公开(公告)号：US07243048B2
公开(公告)日：2007-07-10
申请号：US11288818
申请日：2005-11-28
申请人： Wendy K. Foslien , Satya Varaprasad Allumallu
发明人： Wendy K. Foslien , Satya Varaprasad Allumallu
IPC分类号： G06F17/18
CPC分类号： G05B23/024
摘要： A fault detection system and method is provided that facilitates detection of faults that are manifest over a plurality of different operational phases. The fault detection system and method use multiway principal component analysis (MPCA) to detect fault from turbine engine sensor data. Specifically, the fault detection system uses a plurality of load vectors, each of the plurality of load vectors representing a principal component in the turbine engine sensor data from the multiple operational phases. The load vectors are preferably developed using sets of historical sensor data. When developed using historical data covering multiple operational phases, the load vectors can be used to detect likely faults in turbine engines. Specifically, new sensor data from the multiple operational phases is projected on to the load vectors, generating a plurality of statistical measures that can be classified to determine if a fault is manifest in the new sensor data.
摘要翻译：提供了一种故障检测系统和方法，其有助于检测在多个不同操作阶段上显现的故障。故障检测系统和方法使用多路主成分分析（MPCA）来检测涡轮发动机传感器数据的故障。具体地，故障检测系统使用多个负载向量，多个负载矢量中的每一个表示来自多个操作阶段的涡轮发动机传感器数据中的主要分量。优选利用历史传感器数据集开发负载矢量。当使用涵盖多个操作阶段的历史数据开发时，可以使用负载向量来检测涡轮发动机中的可能故障。具体地说，来自多个操作阶段的新的传感器数据被投影到负载向量上，产生多个统计测量值，这些统计测量值可被分类以确定新传感器数据中是否存在故障。

7. 发明授权

US08013864B2 Method and system for visualizing multivariate statistics 有权
标题翻译：多变量统计可视化的方法和系统
公开(公告)号：US08013864B2
公开(公告)日：2011-09-06
申请号：US11897408
申请日：2007-08-30
申请人： Wendy K. Foslien
发明人： Wendy K. Foslien
IPC分类号： G06T11/20 , G09G5/00 , G06K9/00 , G06K9/62 , G05B11/01 , G06F19/00 , G01D1/00 , G09G1/28 , H03F1/26
CPC分类号： G06T11/206
摘要： A method, apparatus and module for visualizing multivariate statistical measurements. A processing system receives multivariate statistical output data, such as scores or contributions from a multivariate statistical model and renders the multivariate statistical output data as a function of time as a color map on a display. Each multivariate statistical output data can be obtained at each time sample and rendered as a corresponding color patch on the display. The color, height and width of the corresponding color patch can be adjusted to correspond to the magnitude of the output data, the length of time of the time sample and the number of the output data. Normalized scores/contributions at each time sample can be rendered as a corresponding color patch on the display in response to said Q statistic exceeding said predetermined threshold.
摘要翻译：一种用于可视化多变量统计测量的方法，装置和模块。处理系统接收多变量统计输出数据，例如来自多变量统计模型的分数或贡献，并将多变量统计输出数据作为时间的函数作为显示器上的颜色图。每个多变量统计输出数据可以在每个时间点获得，并在显示器上呈现为相应的色标。相应色块的颜色，高度和宽度可以根据输出数据的大小，时间采样的时间长度和输出数据的数量进行调整。响应于超过所述预定阈值的所述Q统计量，可以将每个时间样本的归一化分数/贡献呈现为显示器上的对应色标。

8. 发明申请

US20090125825A1 APPARATUS AND METHOD FOR DISPLAYING ENERGY-RELATED INFORMATION 有权
标题翻译：用于显示能量相关信息的装置和方法
公开(公告)号：US20090125825A1
公开(公告)日：2009-05-14
申请号：US12259959
申请日：2008-10-28
申请人： Jeffrey M. Rye , Wendy K. Foslien , Steven D. Gabel , Geoffrey Ho , Karel Marik , Josef Rieger
发明人： Jeffrey M. Rye , Wendy K. Foslien , Steven D. Gabel , Geoffrey Ho , Karel Marik , Josef Rieger
IPC分类号： G06F3/048
CPC分类号： G05B23/0216
摘要： A method includes receiving energy-related information associated with multiple elements in a hierarchically-arranged domain. The method also includes determining a value of an energy-related metric for each of the elements using the energy-related information. The method further includes generating a graphical user interface using the metric values and presenting the graphical user interface to a user. The graphical user interface includes a treemap having multiple sections, each associated with one of the elements. The graphical user interface also includes a graph displaying energy-related information associated with a selected element. A size of each section in the treemap could be based on a size, importance, energy usage, and/or carbon emission of the associated element. A color and a color intensity of each section in the treemap could be based on the metric value of the associated element and/or a comparison of the absolute energy usage to a baseline.
摘要翻译：一种方法包括接收与分层布置的域中的多个元素相关联的能量相关信息。该方法还包括使用能量相关信息确定每个元件的能量相关度量的值。该方法还包括使用度量值生成图形用户界面并向用户呈现图形用户界面。图形用户界面包括具有多个部分的树状图，每个部分与一个元素相关联。图形用户界面还包括显示与所选元素相关联的能量相关信息的图。树状图中每个部分的大小可以基于相关元素的大小，重要性，能量使用和/或碳排放。树状图中每个部分的颜色和颜色强度可以基于相关元素的度量值和/或绝对能量使用与基线的比较。

9. 发明申请

US20080167842A1 Method and system for detecting, analyzing and subsequently recognizing abnormal events 审中-公开
标题翻译：用于检测，分析和随后识别异常事件的方法和系统
公开(公告)号：US20080167842A1
公开(公告)日：2008-07-10
申请号：US11649987
申请日：2007-01-04
申请人： Edward L. Cochran , Wendy K. Foslien
发明人： Edward L. Cochran , Wendy K. Foslien
IPC分类号： G06F17/18 , G06F11/07
CPC分类号： G06K9/6284 , G05B23/024 , G05B23/0281
摘要： A system and method for detecting and subsequently recognizing abnormal events. A variety of discrete process event data and continuous process data can be collected over an extended period and then incorporated into a principal component analysis (PCA). The PCA model describes the variability associated with characteristics of normal and abnormal operations. Information embedded in process alarms, operation actions and event journals can then be extracted in order to identify periods of normal and abnormal operations. Operator logs can be used to label each upset with a characteristic cause and/or recovery technique.
摘要翻译：一种用于检测和随后识别异常事件的系统和方法。各种离散过程事件数据和连续过程数据可以在一段较长时期内收集，然后并入主成分分析（PCA）。 PCA模型描述与正常和异常操作的特征相关的变异性。然后可以提取嵌入在过程报警，操作操作和事件日志中的信息，以识别正常和异常操作的周期。操作员日志可用于使用特征原因和/或恢复技术标记每个镦粗。

10. 发明授权

US08924877B2 Apparatus and method for displaying changes in statistical parameters in a process control system 有权
标题翻译：用于显示过程控制系统中统计参数变化的装置和方法
公开(公告)号：US08924877B2
公开(公告)日：2014-12-30
申请号：US11978349
申请日：2007-10-29
申请人： Wendy K. Foslien , Soumitri N. Kolavennu , Dinkar Mylaraswamy , Dal Vernon C. Reising
发明人： Wendy K. Foslien , Soumitri N. Kolavennu , Dinkar Mylaraswamy , Dal Vernon C. Reising
IPC分类号： G06F3/048 , G06F17/18 , G06F17/30 , G06T11/20 , G05B15/02 , G06F3/0481 , G05B23/02
CPC分类号： G06F3/04812 , G05B15/02 , G05B23/0267 , G06F17/18 , G06F17/30572 , G06F17/30958 , G06F17/30994 , G06T11/206
摘要： At least one statistical output associated with a process model and a rate of change associated with each statistical output is identified, such as by using historical data associated with the process model. The statistical outputs and the rates of change are used to generate a graphical display, such as a phase plane plot. Each point in the display is based on one of the statistical outputs and its associated rate of change. The graphical display could include multiple portions, such as quadrants, and one of the portions can be selected and highlighted. The different portions of the graphical display may represent whether the process model is a poor fit to current conditions and approaching a better fit, a poor fit and approaching a poorer fit, a good fit and approaching a poorer fit, and a good fit and approaching a better fit.
摘要翻译：识别与过程模型相关联的至少一个统计输出和与每个统计输出相关联的变化率，例如通过使用与过程模型相关联的历史数据。统计输出和变化率用于生成图形显示，如相平面图。显示屏中的每个点都是基于统计输出和相关联的变化率之一。图形显示可以包括多个部分，例如象限，并且可以选择和突出显示部分之一。图形显示的不同部分可以表示过程模型是否不适合当前条件并且接近更好的拟合，差的拟合和接近较差的拟合，良好的拟合和接近较差的拟合以及良好的拟合和接近更好的适合

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