公开(公告)号：US20100063611A1

公开(公告)日：2010-03-11

申请号：US12208686

申请日：2008-09-11

申请人： Shailesh Rajnikant Patel ,  Ramprasad Yelchuru ,  Srikanth Ryali ,  Pradeep K. Shetty ,  Gudi Ravindra

发明人： Shailesh Rajnikant Patel ,  Ramprasad Yelchuru ,  Srikanth Ryali ,  Pradeep K. Shetty ,  Gudi Ravindra

IPC分类号： G06F19/00

CPC分类号： G05B23/024

摘要： Systems and methods (300) for offline/online performance monitoring of batch processes (BPs) involving obtaining archived data (AD) obtained during runs of BP and including information defining a batch quality attribute for each run. The method also involves forming clusters by classifying AD for the runs into classes based on the batch quality attribute(s) and building a first multivariate statistical model (MSM) using AD. The method can further involve building a wavelet analysis based feature matrix (FM) using AD, forming a first projection (1200) by projecting FM onto a first MSM, building a second MSM (1300) using information obtained from the first projection, and computing centroids (C902, . . . , C918) and boundary profiles for the clusters (902, . . . , 918). The method can involve performing an online/offline performance monitoring (700/800) using an integrated version of the first and second MSM, a classification algorithm, centroids, and boundary profiles.

摘要翻译： 用于批处理过程（BP）的离线/在线性能监视的系统和方法（300），涉及获取BP运行期间获得的归档数据（AD），并包括为每个运行定义批次质量属性的信息。 该方法还涉及通过基于批次质量属性将AD分类为类，并使用AD构建第一多元统计模型（MSM）来形成集群。 该方法还可以包括使用AD构建基于小波分析的特征矩阵（FM），通过将FM投影到第一MSM上形成第一投影（1200），使用从第一投影获得的信息构建第二MSM（1300） 重心（C902，... C918）和群集的边界轮廓（902，... 918）。 该方法可以包括使用第一和第二MSM的集成版本，分类算法，质心和边界轮廓来执行在线/离线性能监视（700/800）。

公开(公告)号：US08090676B2

公开(公告)日：2012-01-03

申请号：US12208686

申请日：2008-09-11

申请人： Shailesh Rajnikant Patel ,  Ramprasad Yelchuru ,  Srikanth Ryali ,  Pradeep K. Shetty ,  Gudi Ravindra

发明人： Shailesh Rajnikant Patel ,  Ramprasad Yelchuru ,  Srikanth Ryali ,  Pradeep K. Shetty ,  Gudi Ravindra

IPC分类号： G06F15/00 ,  G06F15/18 ,  G06F19/00

CPC分类号： G05B23/024

摘要： Systems and methods (300) for offline/online performance monitoring of batch processes (BPs) involving obtaining archived data (AD) obtained during runs of BP and including information defining a batch quality attribute for each run. The method also involves forming clusters by classifying AD for the runs into classes based on the batch quality attribute(s) and building a first multivariate statistical model (MSM) using AD. The method can further involve building a wavelet analysis based feature matrix (FM) using AD, forming a first projection (1200) by projecting FM onto a first MSM, building a second MSM (1300) using information obtained from the first projection, and computing centroids (C902, . . . , C918) and boundary profiles for the clusters (902, . . . , 918). The method can involve performing an online/offline performance monitoring (700/800) using an integrated version of the first and second MSM, a classification algorithm, centroids, and boundary profiles.

摘要翻译： 用于批处理过程（BP）的离线/在线性能监视的系统和方法（300），涉及获取BP运行期间获得的归档数据（AD），并包括为每个运行定义批次质量属性的信息。 该方法还涉及通过基于批次质量属性将AD分类为类，并使用AD构建第一多元统计模型（MSM）来形成集群。 该方法还可以包括使用AD构建基于小波分析的特征矩阵（FM），通过将FM投影到第一MSM上形成第一投影（1200），使用从第一投影获得的信息构建第二MSM（1300） 重心（C902，... C918）和群集的边界轮廓（902，... 918）。 该方法可以包括使用第一和第二MSM的集成版本，分类算法，质心和边界轮廓来执行在线/离线性能监视（700/800）。

公开(公告)号：US08078434B2

公开(公告)日：2011-12-13

申请号：US12174955

申请日：2008-07-17

申请人： Ramprasad Yelchuru ,  Srikanth Ryali ,  Shailesh Rajnikant Patel ,  Gudi Ravindra

发明人： Ramprasad Yelchuru ,  Srikanth Ryali ,  Shailesh Rajnikant Patel ,  Gudi Ravindra

IPC分类号： G06F7/60 ,  G06F17/10

CPC分类号： G05B19/41885 ,  G05B19/41875 ,  G05B2219/31444 ,  G05B2219/32201

摘要： A method (300, 400, 500, 1200) for offline/online monitoring of batch processes. The method involves (312) decomposing a time domain of a batch process run (BPR) into several blocks and (334) building multivariate statistical models (MSMs) for each of them using archived data for a batch process (ABPD). ABPD comprises stored data obtained during BPRs. The method also involves (506, 1204) retrieving recently stored data (RSD) for a recent fully performed BPR run (FPRNEW) or current BPR run. The method further involves (520, 1210) building a feature vector matrix (FVM) using RSD. FVM contains feature vectors representing statistical measures of wavelet coefficients determined for variables (v0, . . . , vJ). A projection (1100, 1150, 1190) is formed by projecting feature vectors onto at least one MSM or a combined multivariate statistical model (CMSM). CMSM is a weighted average of at least two MSMs. Subsequently, at least one estimate is generated for FPRNEW or current BPR run using information provided by the projection (528, 1220).

摘要翻译： 一种用于批处理过程的脱机/在线监测的方法（300,400,500,100）。 该方法涉及（312）将批处理运行（BPR）的时域分解为若干块，并且（334）使用用于批处理（ABPD）的归档数据为其中的每一个构建多变量统计模型（MSM）。 ABPD包括在BPR期间获得的存储数据。 该方法还涉及（506,1204）检索用于最近完全执行的BPR运行（FPRNEW）或当前BPR运行的最近存储的数据（RSD）。 该方法还涉及（520,1012）使用RSD构建特征向量矩阵（FVM）。 FVM包含表示为变量（v0，...，vJ）确定的小波系数的统计度量的特征向量。 通过将特征向量投影到至少一个MSM或组合多变量统计模型（CMSM）上来形成投影（1100,1150,1190）。 CMSM是至少两个MSM的加权平均数。 随后，使用由投影（528,1220）提供的信息，为FPRNEW或当前BPR运行生成至少一个估计。

公开(公告)号：US11221157B2

公开(公告)日：2022-01-11

申请号：US16966703

申请日：2018-02-01

申请人： Ademco Inc. ,  Aaron J. Klein ,  Ramprasad Yelchuru ,  Huanmin Bao ,  Cameron K. Vreeland

发明人： Aaron J. Klein ,  Ramprasad Yelchuru ,  Huanmin Bao ,  Cameron K. Vreeland

IPC分类号： F24F11/523 ,  F24F11/56 ,  F24F110/20 ,  F24F110/10 ,  G05B15/02

摘要： A wireless controller to set a programmable setpoint of a remote HVAC unit that thermostatically controls a temperature in a space using the temperature sensed and a programmable setpoint. The wireless controller may include a transmitter, a memory, a temperature sensor, and a controller. The wireless controller may transmit commands to set the programmable setpoint of the remote HVAC unit, wait until the temperature sensed by the temperature sensor of the wireless controller stabilizes, determine a difference between the desired setpoint temperature and the stabilized temperature, and if the offset temperature is greater than or equal to a threshold offset, determine an updated control setpoint temperature and transmit commands to set the programmable setpoint of the remote HVAC unit to an updated control setpoint temperature.

公开(公告)号：US20100017008A1

公开(公告)日：2010-01-21

申请号：US12174955

申请日：2008-07-17

申请人： Ramprasad Yelchuru ,  Srikanth Ryali ,  Shailesh Rajnikant Patel ,  Gudi Ravindra

发明人： Ramprasad Yelchuru ,  Srikanth Ryali ,  Shailesh Rajnikant Patel ,  Gudi Ravindra

IPC分类号： G06F19/00

CPC分类号： G05B19/41885 ,  G05B19/41875 ,  G05B2219/31444 ,  G05B2219/32201

摘要： A method (300, 400, 500, 1200) for offline/online monitoring of batch processes. The method involves (312) decomposing a time domain of a batch process run (BPR) into several blocks and (334) building multivariate statistical models (MSMs) for each of them using archived data for a batch process (ABPD). ABPD comprises stored data obtained during BPRs. The method also involves (506, 1204) retrieving recently stored data (RSD) for a recent fully performed BPR run (FPRNEW) or current BPR run. The method further involves (520, 1210) building a feature vector matrix (FVM) using RSD. FVM contains feature vectors representing statistical measures of wavelet coefficients determined for variables (v0, . . . , vJ). A projection (1100, 1150, 1190) is formed by projecting feature vectors onto at least one MSM or a combined multivariate statistical model (CMSM). CMSM is a weighted average of at least two MSMs. Subsequently, at least one estimate is generated for FPRNEW or current BPR run using information provided by the projection (528, 1220).

摘要翻译： 一种用于批处理过程的脱机/在线监测的方法（300,400,500,100）。 该方法涉及（312）将批处理运行（BPR）的时域分解为若干块，并且（334）使用用于批处理（ABPD）的归档数据为其中的每一个构建多变量统计模型（MSM）。 ABPD包括在BPR期间获得的存储数据。 该方法还涉及（506,1204）检索用于最近完全执行的BPR运行（FPRNEW）或当前BPR运行的最近存储的数据（RSD）。 该方法还涉及（520,1012）使用RSD构建特征向量矩阵（FVM）。 FVM包含表示为变量（v0，...，vJ）确定的小波系数的统计度量的特征向量。 通过将特征向量投影到至少一个MSM或组合多变量统计模型（CMSM）上来形成投影（1100,1150,1190）。 CMSM是至少两个MSM的加权平均数。 随后，使用由投影（528,1220）提供的信息，为FPRNEW或当前BPR运行生成至少一个估计。

公开(公告)号：US20080243289A1

公开(公告)日：2008-10-02

申请号：US11729058

申请日：2007-03-28

申请人： Ramprasad Yelchuru ,  Harigopal Raghavan ,  Ravindra D. Gudi ,  Jagadeesh Brahmajosyula ,  Srinivasa Prabhu Edamadaka

发明人： Ramprasad Yelchuru ,  Harigopal Raghavan ,  Ravindra D. Gudi ,  Jagadeesh Brahmajosyula ,  Srinivasa Prabhu Edamadaka

IPC分类号： G06F19/00

CPC分类号： G05B17/02

摘要： A system and method modifies a dynamic model of a process in a plant for an advanced process control controller wherein the model includes sub models. Performance of the controller is monitored and performance degradation is quantified as the process changes. It is then determined whether a selected number of sub models need updating or the entire model dynamics need updating as a function of the quantified controller performance degradation If a selected number of sub models need updating, an excitation signal is initiated for such sub models to identify new sub models. If the entire model dynamics need updating, a complete perturbation signal is initiated and triggers exhaustive closed-loop identification of entire model. The newly identified model or sub models is incorporated in the controller.

摘要翻译： 系统和方法修改用于高级过程控制控制器的工厂中的过程的动态模型，其中模型包括子模型。 监控控制器的性能，并且随着过程的变化，量化性能下降。 然后确定所选数量的子模型是否需要更新，或者整个模型动力学需要根据量化的控制器性能退化的函数进行更新。如果所选择的子模型数量需要更新，则启动激励信号以使这些子模型识别 新的子模型。 如果整个模型动力学需要更新，则会启动一个完整的扰动信号，并触发整个模型的彻底的闭环识别。 新确定的模型或子模型被并入控制器。