公开(公告)号：US20100299105A1

公开(公告)日：2010-11-25

申请号：US12777826

申请日：2010-05-11

申请人： Jiri Vass ,  Petr Stluka ,  Bin Sai

发明人： Jiri Vass ,  Petr Stluka ,  Bin Sai

IPC分类号： G06F15/00

CPC分类号： G05B23/0283 ,  G05B19/41865 ,  G05B23/0254 ,  G05B2219/32234 ,  Y02P90/20

摘要： A method for online condition-based monitoring (CBM) of a tank farm including a plurality of storage tanks includes providing a tank model including a diagnostic and/or predictive tank model based on calculated tank metrics that is derived from historical data including tank operation data. The calculated tank metrics include tank operational metrics based on the tank operational data for the storage tanks and tank condition metrics based on tank inspection or maintenance data for the storage tanks. The tank model provides relationships between the tank condition metrics and the tank operational metrics. Results are generated using the tank model including at least one failure indicia for at least a first of the storage tanks using the calculated tank metrics and current measured data for the first tank as inputs to the tank model. The failure indicia is processed for scheduling at least one maintenance task for the first tank.

摘要翻译： 一种用于包括多个储罐的储罐场的基于状态的监测（CBM）的方法，包括基于从包括油箱操作数据的历史数据导出的计算油箱量度提供包括诊断和/或预测油箱模型的油箱模型 。 计算的油罐尺度包括基于储罐操作数据的坦克操作指标和基于坦克检查或储罐维护数据的油箱状况指标。 坦克模型提供了坦克条件指标和坦克操作指标之间的关系。 使用罐模型生成结果，该罐模型包括使用所计算的罐尺度和第一罐的当前测量数据作为罐模型的输入的至少第一储罐的至少一个故障标记。 对故障标记进行处理，以调度第一个储罐的至少一个维护任务。

公开(公告)号：US20130031086A1

公开(公告)日：2013-01-31

申请号：US13190134

申请日：2011-07-25

申请人： Martin STRELEC ,  Petr STLUKA

发明人： Martin STRELEC ,  Petr STLUKA

IPC分类号： G06F17/30

CPC分类号： G06F16/2465 ,  G06F16/26

摘要： An automated data processing method includes providing a plurality of incident data from a physical installation of a process automation system in a processing facility that collectively spans a period of time and building a stored relational database including database data from the incident data. The method includes automatically generating at least one processed data result from the database data, wherein the automatically generating generates at least one processed data result from the database data. The processed data result is in a displayable form that facilitates identification of a problem in the process automation system by an analyst in a remote support center.

摘要翻译： 自动数据处理方法包括从处理设施中的过程自动化系统的物理安装提供多个事件数据，该处理设备共同跨越一段时间并构建包括来自事件数据的数据库数据的存储的关系数据库。 该方法包括从数据库数据自动生成至少一个处理的数据结果，其中自动生成从数据库数据生成至少一个处理后的数据结果。 处理后的数据结果是可显示的形式，便于远程支持中心的分析人员识别过程自动化系统中的问题。

公开(公告)号：US08249829B2

公开(公告)日：2012-08-21

申请号：US12777826

申请日：2010-05-11

申请人： Jiri Vass ,  Petr Stluka ,  Bin Sai

发明人： Jiri Vass ,  Petr Stluka ,  Bin Sai

IPC分类号： G06F19/00

CPC分类号： G05B23/0283 ,  G05B19/41865 ,  G05B23/0254 ,  G05B2219/32234 ,  Y02P90/20

摘要： A method for online condition-based monitoring (CBM) of a tank farm including a plurality of storage tanks includes providing a tank model including a diagnostic and/or predictive tank model based on calculated tank metrics that is derived from historical data including tank operation data. The calculated tank metrics include tank operational metrics based on the tank operational data for the storage tanks and tank condition metrics based on tank inspection or maintenance data for the storage tanks. The tank model provides relationships between the tank condition metrics and the tank operational metrics. Results are generated using the tank model including at least one failure indicia for at least a first of the storage tanks using the calculated tank metrics and current measured data for the first tank as inputs to the tank model. The failure indicia is processed for scheduling at least one maintenance task for the first tank.

摘要翻译： 一种用于包括多个储罐的储罐场的基于状态的监测（CBM）的方法，包括基于从包括油箱操作数据的历史数据导出的计算油箱量度提供包括诊断和/或预测油箱模型的油箱模型 。 计算的油罐尺度包括基于储罐操作数据的坦克操作指标和基于坦克检查或储罐维护数据的油箱状况指标。 坦克模型提供了坦克条件指标和坦克操作指标之间的关系。 使用罐模型生成结果，该罐模型包括使用所计算的罐尺度和第一罐的当前测量数据作为罐模型的输入的至少第一储罐的至少一个故障标记。 对故障标记进行处理，以调度第一个储罐的至少一个维护任务。

公开(公告)号：US07908300B2

公开(公告)日：2011-03-15

申请号：US12061540

申请日：2008-04-02

申请人： Petr Stluka ,  Petr Cisar ,  Jiri Rojicek

发明人： Petr Stluka ,  Petr Cisar ,  Jiri Rojicek

IPC分类号： G06F7/00 ,  G06F17/30 ,  G06F13/14

CPC分类号： G06F17/30

摘要： Methods (100), algorithms, and implementing systems (200) for guided annotation of process deviations by an individual responsible for annotations (e.g. process operator) for a manufacturing process having an associated computing system (210) including a historical database (stored in memory 219). The historical database is provided (101) including data associated with a plurality of process performance measures including stored deviations for the plurality of process measures, stored quantitative deviation attributes thereof and at least one cause associated with each of the stored deviations process performance measures. The method includes the individual responsible for annotation selecting at least one present deviation (D) to be annotated responsive to a deviation in a target process performance measure (T) selected from the plurality of process measures (102). Automatically, the stored quantitative attributes associated with D, T, and a target value or target interval for T are retrieved (103). The database is searched to identify a plurality of similar deviations from the stored deviations determined to be similar to D and the plurality of similar deviations are statistically processed to generate a listing indicating a relative likelihood of the at least one cause associated with the plurality of similar deviations (104). The listing is presented to the individual to prompt selection of at least one present cause for D from the cause(s) associated with the plurality of similar deviations (105).

摘要翻译： 用于对具有包括历史数据库（存储在存储器中）的相关联的计算系统（210）的制造过程的负责注释的个体（例如过程操作员）进行指导注释过程偏差的方法（100），算法和实现系统（200） 219）。 提供历史数据库（101），包括与多个处理性能测量相关联的数据，包括多个处理测量的存储偏差，其存储的定量偏差属性以及与存储的偏差过程执行度量中的每一个相关联的至少一个原因。 该方法包括负责注释的个体，其响应于从多个处理措施（102）中选择的目标过程绩效测量（T）的偏差而选择要注释的至少一个当前偏差（D）。 自动地，检索与D，T相关联的存储的定量属性以及T的目标值或目标间隔（103）。 搜索数据库以识别与被确定为类似于D的所存储的偏差中的多个相似的偏差，并且对所述多个相似的偏差进行统计处理以产生表示与所述多个相似的相关联的所述至少一个原因的相对似然性的列表 偏差（104）。 该列表被呈现给个人以提示从与多个相似偏差（105）相关联的原因中选择D的至少一个当前原因。

公开(公告)号：US20090271150A1

公开(公告)日：2009-10-29

申请号：US12108076

申请日：2008-04-23

申请人： Petr Stluka ,  Jiri Rojicek

发明人： Petr Stluka ,  Jiri Rojicek

IPC分类号： G06F15/00

CPC分类号： G05B23/0232

摘要： A method for automatically monitoring the performance of equipment (100) includes the step (101) of compiling current operating conditions including at least one current sensor reading and at least one current performance measurement associated with the current conditions (PMc). A historical database including a plurality of stored operating conditions is searched (102), each stored operating condition including at least one stored sensor reading and at least one associated stored performance measure (PM*), wherein at least one similar operating condition is identified in the search using distances between the current operating conditions and the plurality of stored operating conditions. The performance measure associated with the similar operating condition is fit to generate a regression model (103). The regression model is applied to the current operating condition (104) to generate a model estimate for the performance measure for the current operating condition (PMe). A first difference between PMc and said PMe is computed (105). The first difference is compared to a predetermined threshold, wherein a warning is automatically generated if the first difference has a value greater than the predetermined threshold (106).

摘要翻译： 用于自动监测设备（100）的性能的方法包括编译当前操作条件的步骤（101），包括至少一个电流传感器读数和与当前条件（PMc）相关联的至少一个当前性能测量。 搜索包括多个存储的操作条件的历史数据库（102），每个存储的操作条件包括至少一个存储的传感器读数和至少一个相关联的存储性能测量（PM *），其中至少一个类似的操作条件在 使用当前操作条件和多个存储的操作条件之间的距离进行搜索。 与类似操作条件相关的性能测量适合于生成回归模型（103）。 将回归模型应用于当前操作条件（104）以产生用于当前操作条件（PMe）的性能测量的模型估计。 计算PMc和PMe之间的第一个差异（105）。 将第一差异与预定阈值进行比较，其中如果第一差异具有大于预定阈值（106）的值，则自动生成警告。

公开(公告)号：US08620634B2

公开(公告)日：2013-12-31

申请号：US12841345

申请日：2010-07-22

申请人： Wendy Foslien Graber ,  Zdenek Schindler ,  Petr Stluka ,  Girija Parthasarathy

发明人： Wendy Foslien Graber ,  Zdenek Schindler ,  Petr Stluka ,  Girija Parthasarathy

IPC分类号： G06F17/50

CPC分类号： G06Q10/06 ,  Y02A30/12 ,  Y02P90/86 ,  Y04S10/54 ,  Y04S10/60

摘要： A computer implemented method includes providing information related to wind speed and direction prediction to a wind turbine energy prediction module, providing wind energy production prediction information to a resource allocation engine, and combining the wind energy production prediction information with information about non-renewable energy resources to provide an output identifying energy production resources to use for optimal load servicing.

摘要翻译： 计算机实现方法包括向风力涡轮机能量预测模块提供与风速和方向预测有关的信息，向资源分配引擎提供风能生产预测信息，并将风能产生预测信息与不可再生能源资源相结合 以提供识别用于最佳负载维修的能量生产资源的输出。

公开(公告)号：US20110040550A1

公开(公告)日：2011-02-17

申请号：US12841345

申请日：2010-07-22

申请人： Wendey Foslien Graber ,  Zdenek Schindler ,  Petr Stluka ,  Girija Parthasarathy

发明人： Wendey Foslien Graber ,  Zdenek Schindler ,  Petr Stluka ,  Girija Parthasarathy

IPC分类号： G06G7/63

CPC分类号： G06Q10/06 ,  Y02A30/12 ,  Y02P90/86 ,  Y04S10/54 ,  Y04S10/60

摘要： A computer implemented method includes providing information related to wind speed and direction prediction to a wind turbine energy prediction module, providing wind energy production prediction information to a resource allocation engine, and combining the wind energy production prediction information with information about non-renewable energy resources to provide an output identifying energy production resources to use for optimal load servicing.

摘要翻译： 计算机实现方法包括向风力涡轮机能量预测模块提供与风速和方向预测有关的信息，向资源分配引擎提供风能生产预测信息，并将风能产生预测信息与不可再生能源资源相结合 以提供识别用于最佳负载维修的能量生产资源的输出。

公开(公告)号：US20100211192A1

公开(公告)日：2010-08-19

申请号：US12634425

申请日：2009-12-09

申请人： Petr Stluka ,  Petr Cisar ,  Eva Hostalkova

发明人： Petr Stluka ,  Petr Cisar ,  Eva Hostalkova

IPC分类号： G06F17/30 ,  G05B11/01

CPC分类号： G05B23/0272 ,  G06F16/2477

摘要： A method includes receiving at least one search parameter, where the at least one search parameter defines one or more restrictions on types of event patterns. The method also includes searching a collection of historical events associated with a process control system. The method further includes identifying one or more groups of alarms each having a pattern that satisfies the one or more restrictions. In addition, the method includes outputting information identifying the one or more groups of alarms. The method could also include receiving a selection of at least one of the one or more identified groups of alarms. The method could further include notifying one or more components in the process control system to begin dynamically suppressing alarms in the at least one selected group of alarms.

摘要翻译： 一种方法包括接收至少一个搜索参数，其中所述至少一个搜索参数定义对事件模式类型的一个或多个限制。 该方法还包括搜索与过程控制系统相关联的历史事件的集合。 该方法还包括识别一个或多个警报组，每个警报组具有满足一个或多个限制的模式。 此外，该方法包括输出识别一组或多组警报的信息。 该方法还可以包括接收对一个或多个所识别的警报组中的至少一个的选择。 该方法还可以包括通知过程控制系统中的一个或多个组件来开始动态地抑制所述至少一个所选择的报警组中的报警。

公开(公告)号：US20090254551A1

公开(公告)日：2009-10-08

申请号：US12061540

申请日：2008-04-02

申请人： Petr Stluka ,  Petr Cisar ,  Jiri Rojicek

发明人： Petr Stluka ,  Petr Cisar ,  Jiri Rojicek

IPC分类号： G06F7/06 ,  G06F17/30

CPC分类号： G06F17/30

摘要： Methods (100), algorithms, and implementing systems (200) for guided annotation of process deviations by an individual responsible for annotations (e.g. process operator) for a manufacturing process having an associated computing system (210) including a historical database (stored in memory 219). The historical database is provided (101) including data associated with a plurality of process performance measures including stored deviations for the plurality of process measures, stored quantitative deviation attributes thereof and at least one cause associated with each of the stored deviations process performance measures. The method includes the individual responsible for annotation selecting at least one present deviation (D) to be annotated responsive to a deviation in a target process performance measure (T) selected from the plurality of process measures (102). Automatically, the stored quantitative attributes associated with D, T, and a target value or target interval for T are retrieved (103). The database is searched to identify a plurality of similar deviations from the stored deviations determined to be similar to D and the plurality of similar deviations are statistically processed to generate a listing indicating a relative likelihood of the at least one cause associated with the plurality of similar deviations (104). The listing is presented to the individual to prompt selection of at least one present cause for D from the cause(s) associated with the plurality of similar deviations (105).

摘要翻译： 用于对具有包括历史数据库（存储在存储器中）的相关联的计算系统（210）的制造过程的负责注释的个体（例如过程操作员）进行指导注释过程偏差的方法（100），算法和实现系统（200） 219）。 提供历史数据库（101），包括与多个处理性能测量相关联的数据，包括多个处理测量的存储偏差，其存储的定量偏差属性以及与存储的偏差过程执行度量中的每一个相关联的至少一个原因。 该方法包括负责注释的个体，其响应于从多个处理措施（102）中选择的目标过程绩效测量（T）的偏差来选择要注释的至少一个当前偏差（D）。 自动地，检索与D，T相关联的存储的定量属性以及T的目标值或目标间隔（103）。 搜索数据库以识别与被确定为类似于D的所存储的偏差中的多个相似的偏差，并且对所述多个相似的偏差进行统计处理以产生表示与所述多个相似的相关联的所述至少一个原因的相对似然性的列表 偏差（104）。 该列表被呈现给个人以提示从与多个相似偏差（105）相关联的原因中选择D的至少一个当前原因。

公开(公告)号：US07460915B2

公开(公告)日：2008-12-02

申请号：US11471919

申请日：2006-06-21

申请人： Karel Marik ,  Roman Navratil ,  Petr Stluka

发明人： Karel Marik ,  Roman Navratil ,  Petr Stluka

IPC分类号： G05B13/04 ,  G05B21/00

CPC分类号： G05B13/04

摘要： In a process control system, the hidden properties of a catalyst are estimated by including those properties in hidden states within a state space model and solving the state space model based on measurable inputs and outputs of the process. The process may include defining a state space model for a process having a catalyst state comprising a hidden catalyst property; defining a set of empirically measurable input variables for the state space model, defining a set of output variables for the state space model, measuring a set of input values corresponding to the set of input variables; measuring a set of output values corresponding to the set of output variables; and estimating the hidden catalyst property based on the input values, the output values, and the state space model.

摘要翻译： 在过程控制系统中，通过将状态空间模型中的隐藏状态包括这些属性来估计催化剂的隐藏特性，并且基于过程的可测量输入和输出来求解状态空间模型。 该方法可以包括为具有包含隐藏的催化剂性质的催化剂状态的方法定义状态空间模型; 为状态空间模型定义一组经验可测量的输入变量，为状态空间模型定义一组输出变量，测量与该组输入变量相对应的一组输入值; 测量与该组输出变量相对应的一组输出值; 并基于输入值，输出值和状态空间模型估计隐藏的催化剂性质。