Abstract: Methods and systems for conveying or operationalizing data from a transport climate control system (TCCS) that provides climate control within an internal space moved by a vehicle are disclosed. A method includes determining, by a controller of the TCCS, a set of TCCS operational measurements; obtaining, by the controller, a set of TCCS operational settings; determining, by the controller, a set of cargo measurements; creating a shipment performance log based on the set of TCCS operational measurements, the set of TCCS operational settings, and the set of cargo measurements; publishing the shipment performance log to a data repository; and retrieving the shipment performance log to facilitate disposition of goods in the internal space.

Abstract: A method includes processing data associated with operation of equipment in an industrial process to repeatedly (i) identify one or more models that mathematically represent the operation of the equipment using training data and (ii) generate first indicators potentially identifying at least one specified condition of the equipment using evaluation data and the one or more models. The method also includes classifying the first indicators into multiple classes. The multiple classes include true positive indicators and false positive indicators. The true positive indicators identify that the equipment is suffering from the at least one specified condition. The false positive indicators identify that the equipment is not suffering from the at least one specified condition. The method further includes generating a notification indicating that the equipment is suffering from the at least one specified condition in response to one or more first indicators being classified into the class of true positive indicators.

Abstract: Methods and systems for conveying or operationalizing data from a transport climate control system (TCCS) that provides climate control within an internal space moved by a vehicle are disclosed. A method includes determining, by a controller of the TCCS, a set of TCCS operational measurements; obtaining, by the controller, a set of TCCS operational settings; determining, by the controller, a set of cargo measurements; creating a shipment performance log based on the set of TCCS operational measurements, the set of TCCS operational settings, and the set of cargo measurements; publishing the shipment performance log to a data repository; and retrieving the shipment performance log to facilitate disposition of goods in the internal space.

Abstract: A method includes obtaining data associated with operation of equipment in an industrial process and identifying training data and evaluation data in the obtained data. The method also includes, during each of multiple training periods, identifying one or more models and one or more first statistical values using at least some of the training data and determining a threshold value using the one or more first statistical values. The one or more models represent the operation of the equipment. The method further includes, during each of multiple evaluation periods, determining one or more second statistical values using at least some of the evaluation data and the one or more models, comparing the one or more second statistical values to the threshold value determined in a preceding one of the training periods, and determining whether the equipment is suffering from at least one specified condition based on the comparison.

Abstract: A method includes identifying a first steady-state gain associated with a relationship between a characteristic of a furnace and a setpoint used by a controller that is configured to control the characteristic of the furnace, The first steady-state gain is identified using data collected when the furnace is not suffering from flooding. The method also includes identifying a second steady-state gain associated with the relationship during operation of the furnace. The method further includes comparing the first and second steady-state gains and identifying actual or potential flooding of the furnace based on the comparison.

Abstract: A method includes processing data associated with operation of equipment in an industrial process to repeatedly (i) identify one or more models that mathematically represent the operation of the equipment using training data and (ii) generate first indicators potentially identifying at least one specified condition of the equipment using evaluation data and the one or more models. The method also includes classifying the first indicators into multiple classes. The multiple classes include true positive indicators and false positive indicators. The true positive indicators identify that the equipment is suffering from the at least one specified condition. The false positive indicators identify that the equipment is not suffering from the at least one specified condition. The method further includes generating a notification indicating that the equipment is suffering from the at least one specified condition in response to one or more first indicators being classified into the class of true positive indicators.

Abstract: A method includes obtaining data associated with operation of equipment in an industrial process and identifying training data and evaluation data in the obtained data. The method also includes, during each of multiple training periods, identifying one or more models and one or more first statistical values using at least some of the training data and determining a threshold value using the one or more first statistical values. The one or more models represent the operation of the equipment. The method further includes, during each of multiple evaluation periods, determining one or more second statistical values using at least some of the evaluation data and the one or more models, comparing the one or more second statistical values to the threshold value determined in a preceding one of the training periods, and determining whether the equipment is suffering from at least one specified condition based on the comparison.

Abstract: A method includes identifying a first steady-state gain associated with a relationship between a characteristic of a furnace and a setpoint used by a controller that is configured to control the characteristic of the furnace, The first steady-state gain is identified using data collected when the furnace is not suffering from flooding. The method also includes identifying a second steady-state gain associated with the relationship during operation of the furnace. The method further includes comparing the first and second steady-state gains and identifying actual or potential flooding of the furnace based on the comparison.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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 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.

Abstract: 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.