Abstract: A scatter plot showing the relationship of one variable as a function of a second variable on an x-y graph is enhanced by displaying information about a third variable by means of the shade or color of the data points comprising the scatter plot in correlation with the value of that third variable corresponding to the particular data point.

Abstract: 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.

Abstract: Templates for use in searching for data segments of interest in stores of data are defined and/or refined by analyzing related matches, extracting common or key elements, and/or generalizing or modifying the templates. This process can involve calculating the similarity between matches, clustering matches, and identifying key elements for defining and/or refining templates and/or search parameters. A user may interact with a software tool for refining templates.

Abstract: Templates for use in searching for data segments of interest in stores of data are defined and/or refined by analyzing related matches, extracting common or key elements, and/or generalizing or modifying the templates. This process can involve calculating the similarity between matches, clustering matches, and identifying key elements for defining and/or refining templates and/or search parameters. A user may interact with a software tool for refining templates.

Abstract: A system having a combination including a predictive controller and a correlation model analysis which may include a principal component analysis (PCA) calculator. The system may encompass a discrete sampler of trajectories of variables from the predictive controller for a correlation model analysis calculation. There may be a compensator for providing initialization and estimation values of other system variables for the correlation model analysis. An uncertainty calculator connected to the predictive controller may output an anomaly impact prediction zone based on upper and lower bounds from the predictive controller.

Abstract: 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.

Abstract: A sensor placement algorithm uses process data to determine the optimal distribution of sensors in a distributed parameter manufacturing system. An automatic classification procedure maps any problems in the process to a predetermined set of process disturbances. A control procedure uses process data to determine the best control action that will ensure good system response. Methods for sensor placement, automatic decision tree classification, corrective action control and the apparatus to effectuate these respective methods are integrated into a design methodology.

Abstract: 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.

Abstract: Principal 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.

Abstract: A sensor placement algorithm uses process data to determine the optimal distribution of sensors in a distributed parameter manufacturing system. An automatic classification procedure maps any problems in the process to a predetermined set of process disturbances. A control procedure uses process data to determine the best control action that will ensure good system response. Methods for sensor placement, automatic decision tree classification, corrective action control and the apparatus to effectuate these respective methods are integrated into a design methodology.

Abstract: A sensor placement algorithm uses process data to determine the optimal distribution of sensors in a distributed parameter manufacturing system. An automatic classification procedure maps any problems in the process to a predetermined set of process disturbances. A control procedure uses process data to determine the best control action that will ensure good system response. Methods for sensor placement, automatic decision tree classification, corrective action control and the apparatus to effectuate these respective methods are integrated into a design methodology.

Abstract: A sensor placement algorithm uses process data to determine the optimal distribution of sensors in a distributed parameter manufacturing system. An automatic classification procedure maps any problems in the process to a predetermined set of process disturbances. A control procedure uses process data to determine the best control action that will ensure good system response. Methods for sensor placement, automatic decision tree classification, corrective action control and the apparatus to effectuate these respective methods are integrated into a design methodology.