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: A system and method for applying a first filter and a second filter, such as a recommendation and a constraint filter, to a plurality of items, including determining a cost of applying the first filter and the second filter to the plurality of items, and determining an order of applying the first and second filters based on the cost of applying the first and second filters.

Abstract: Methods, systems, and articles of manufacture consistent with the present invention provide a recommendation server that receives a recommendation request from a user of a client computer. The recommendation server contains software to provide recommendations to the user. To provide the recommendations, the recommendation server applies a constraint filter and a recommendation filter on a set of items.

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 for obtaining diagnostic information, such as evidence about a mechanism, within an algorithmic framework, including filtering and aggregating the information through, for instance, a stochastic process. The output may be an overall belief value relative to a presence of an item such as, for example, a fault in the mechanism.

Abstract: A system and method for combining conclusions from multiple fault detection techniques to isolate likely faults in a turbine engine is provided. The system and method provide the ability to effectively deal with multiple concurrent faults in the engine. Additionally, the embodiments of the invention provide the ability to correctly characterize multiple conclusions generated from evidence having different levels of interdependence. In one embodiment, the conclusions based on device data with high dependency are aggregated using a high dependency aggregation rule, and the resulting high-dependency sets are then further aggregated using a weak dependency rule. Finally, any conclusions based on independent evidence can be aggregated using an independent combination rule. The resulting aggregation determines which fault(s) are most likely indicated by the plurality of conclusions, taken into account the dependency of the device data used to generate the conclusions.

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: 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 and method for combining conclusions from multiple fault detection techniques to isolate likely faults in a turbine engine is provided. The system and method provide the ability to effectively deal with multiple concurrent faults in the engine. Additionally, the embodiments of the invention provide the ability to correctly characterize multiple conclusions generated from evidence having different levels of interdependence. In one embodiment, the conclusions based on device data with high dependency are aggregated using a high dependency aggregation rule, and the resulting high-dependency sets are then further aggregated using a weak dependency rule. Finally, any conclusions based on independent evidence can be aggregated using an independent combination rule. The resulting aggregation determines which fault(s) are most likely indicated by the plurality of conclusions, taken into account the dependency of the device data used to generate the conclusions.

Abstract: Systems and methods operable on a network to coordinate resource utilization amongst agents in a multi-agent systems in a decentralized manner. In one embodiment, this includes interconnected agents that circulate coordination keys amongst coordination group members. The coordination key includes information defining the coordination group, resources coordinated by group members, and information about scheduled resource utilization. In some embodiments, each agent in a coordination group determines its local schedule of resource utilization based on information in the coordination key to achieve its local goals while coordinating with other agents to achieve coordination group goals.

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: Time series data is modeled to understand typical behavior in the time series data. Data that is notably different from typical behavior, as identified by the model, is used to identify candidate patterns corresponding to events that might be interesting. The model may be revised by removing model biasing events so that it better reflects normal or typical behavior. Interesting patterns are then reidentified based on the revised model. The set of interesting patterns is iteratively pruned to result in a set of candidate features to be applied in a time series search algorithm.

Abstract: A system for coordinating the activity of a plurality of humans in teams with a central automated controller having reasoning capability based on a predetermined set of criteria by sending messages to and from each of the humans. The controller processes input from each of the humans in accordance with programmed decision making capability to accomplish predetermined objectives and provide output to at least some of the humans to assess a situation, direct steps in response thereto and coordinate decisions based on a predetermined model and task assessment reasoning to determine the best way to accomplish the predetermined objectives. The coordinator assesses changes to the situation, and makes decisions about the various tasks to be performed and when they are to be begun. Outputs to the humans may be instructions, questions, information and combinations thereof.

Abstract: Anomaly detection technology is used to detect attempts at remote tampering of communications used to control components of critical infrastructure. Intrusions in a control network are detected by monitoring operational traffic on the control network. Activity outside a normal region is identified, and alerts are provided as a function of identified activity outside the normal region. A stide algorithm may be used to identify such activity.

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: A system for coordinating the activity of a plurality of humans in teams with a central automated controller having reasoning capability based on a predetermined set of criteria by sending messages to and from each of the humans. The controller processes input from each of the humans in accordance with programmed decision making capability to accomplish predetermined objectives and provide output to at least some of the humans to assess a situation, direct steps in response thereto and coordinate decisions based on a predetermined model and task assessment reasoning to determine the best way to accomplish the predetermined objectives. The coordinator assesses changes to the situation, and makes decisions about the various tasks to be performed and when they are to be begun.

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 of operating a monitoring and response system for an actor in a daily living environment that relies upon learned models of behavior for adapting system operation. The learned model of behavior preferably includes sequential patterns organized pursuant to assigned partition values that in turn are generated based upon an evaluation of accumulated data. Based upon reference to the learned model of behavior, the system can generate more appropriate response plans based upon expected or unexpected activities, more readily recognize intended activities, recognize abandoned tasks, formulate probabilities of method choice, build probabilities of action success, anticipate and respond to actor movement within the environment, optimize response plan effectiveness, and share learned models across two or more separate system installations.