Abstract: Generating fault indications for an additive manufacturing machine based on a comparison of the outputs of multiple process models to measured sensor data. The method receiving sensor data from the additive manufacturing machine during manufacture of at least one part. Models are selected from a model database, each model generating expected sensor values for a defined condition. Difference values are computed between the received sensor data and an output of each of the models. A probability density function is computed, which defines, for each of the models, a likelihood that a given difference value corresponds to each respective model. A probabilistic rule is applied to determine, for each of the models, a probability that the corresponding model output matches the received sensor data. An indicator is output of a defined condition corresponding to a model having the highest match probability.

Abstract: Generating fault indications for an additive manufacturing machine based on a comparison of the outputs of multiple process models to measured sensor data. The method receiving sensor data from the additive manufacturing machine during manufacture of at least one part. Models are selected from a model database, each model generating expected sensor values for a defined condition. Difference values are computed between the received sensor data and an output of each of the models. A probability density function is computed, which defines, for each of the models, a likelihood that a given difference value corresponds to each respective model. A probabilistic rule is applied to determine, for each of the models, a probability that the corresponding model output matches the received sensor data. An indicator is output of a defined condition corresponding to a model having the highest match probability.

Abstract: Generating fault indications for an additive manufacturing machine based on a comparison of the outputs of multiple process models to measured sensor data. The method receiving sensor data from the additive manufacturing machine during manufacture of at least one part. Models are selected from a model database, each model generating expected sensor values for a defined condition. Difference values are computed between the received sensor data and an output of each of the models. A probability density function is computed, which defines, for each of the models, a likelihood that a given difference value corresponds to each respective model. A probabilistic rule is applied to determine, for each of the models, a probability that the corresponding model output matches the received sensor data. An indicator is output of a defined condition corresponding to a model having the highest match probability.

Abstract: According to some embodiments, a plurality of heterogeneous data source nodes may each generate a series of current data source node values over time that represent a current operation of an electric power grid. A real-time threat detection computer, coupled to the plurality of heterogeneous data source nodes, may receive the series of current data source node values and generate a set of current feature vectors. The threat detection computer may then access an abnormal state detection model having at least one decision boundary created offline using at least one of normal and abnormal feature vectors. The abnormal state detection model may be executed, and a threat alert signal may be transmitted if appropriate based on the set of current feature vectors and the at least one decision boundary.

Abstract: According to some embodiments, a plurality of heterogeneous data source nodes may each generate a series of current data source node values over time that represent a current operation of an electric power grid. A real-time threat detection computer, coupled to the plurality of heterogeneous data source nodes, may receive the series of current data source node values and generate a set of current feature vectors. The threat detection computer may then access an abnormal state detection model having at least one decision boundary created offline using at least one of normal and abnormal feature vectors. The abnormal state detection model may be executed, and a threat alert signal may be transmitted if appropriate based on the set of current feature vectors and the at least one decision boundary.

Abstract: A system for strategic operation of a variable generation power plant includes a computing device in communication with a data store including environmental data independent system operator rules, operator risk metrics, power storage systems, a maintenance schedule, and a capacity record. The computing device including a statistical modeling unit to generate a risk-to-revenue strategic bid estimate for successive time periods based on one or more factors accessible in the data store, a display device to display a graphical representation of the risk-to-revenue strategic bid estimate; and a control processor that analyzes the risk-to-revenue strategic bid estimate to schedule the daily operation of a power storage system and to identify one or more time periods of the successive time periods in which to perform a scheduled maintenance. A method and a non-transitory computer readable medium are also disclosed.

Abstract: Embodiments herein generally provide techniques for upgrading a virtual router (VR) comprising first and second physical routers used to route data between network devices. Before upgrade the first physical router, one or more ports of the router are disabled which causes data paths previously flowing through the first router to flow through the second router. After performing the upgrade, the first router is rebooted and uses a unique router ID to discover the network topology of an external network coupled to the VR. Once the network topology is identified, the first router activates a routing application (e.g., a BGP routing application) which enables the router to process control plane traffic received from the second router. Once routing information is gathered, the first router is able to assume the responsibility of forwarding data packets in the network, thereby freeing the second router to be upgraded.

Abstract: Embodiments herein generally provide techniques for upgrading a virtual router (VR) comprising first and second physical routers used to route data between network devices. Before upgrade the first physical router, one or more ports of the router are disabled which causes data paths previously flowing through the first router to flow through the second router. After performing the upgrade, the first router is rebooted and uses a unique router ID to discover the network topology of an external network coupled to the VR. Once the network topology is identified, the first router activates a routing application (e.g., a BGP routing application) which enables the router to process control plane traffic received from the second router. Once routing information is gathered, the first router is able to assume the responsibility of forwarding data packets in the network, thereby freeing the second router to be upgraded.

Abstract: According to one embodiment, processing data packets includes inserting marker packets into a data stream at time intervals. Each marker packet includes a respective first time stamp indicative of the time the marker packet is inserted. An estimate of the age of a data packet is calculated based on a current time system and the respective first time stamp of the last marker packet that was inserted into the data stream before the data packet.

Abstract: A network node for a scalable packet based network comprising ports for reception and transmission of data packets of network segments connected to the respective ports of the network node, a forwarding table which stores for each port of the network node addresses of nodes reachable through the respective port of the network node, a registering table which stores a list of node addresses registered with the network node wherein the network node forms a registrar node for these addresses, and a registrar table which stores information data on all network nodes which are registrar nodes of the network.

Abstract: According to one embodiment, processing data packets includes inserting marker packets into a data stream at time intervals. Each marker packet includes a respective first time stamp indicative of the time the marker packet is inserted. An estimate of the age of a data packet is calculated based on a current time system and the respective first time stamp of the last marker packet that was inserted into the data stream before the data packet.