Abstract: A cyber-physical system may have a plurality of system nodes including a plurality of monitoring nodes each generating a series of current monitoring node values over time that represent current operation of the cyber-physical system. According to some embodiments, a watermarking computer platform may randomly inject a watermarking signal into an injection subset of the system nodes. The watermarking computer platform may then receive current monitoring node values over time and generate a current watermarking feature vector based on the current monitoring node values. The watermarking computer platform might comprise a dedicated watermarking abnormality detection platform or a unified abnormality detection platform (e.g., that also uses data-drive feature vectors). The injection subset may be associated with a randomly selected subset of the system nodes and/or magnitudes of watermarking signals that are randomly selected.

Abstract: A verification platform may include a data connection to receive a stream of industrial asset cyber-attack detection algorithm data, including a subset of the industrial asset cyber-attack detection algorithm data. The verification platform may store the subset into a data store (the subset of industrial asset cyber-attack detection algorithm data being marked as invalid) and record a hash value associated with a compressed representation of the subset of industrial asset cyber-attack detection algorithm data combined with metadata in a secure, distributed ledger.

Abstract: A cyber-physical system may have a plurality of system nodes including a plurality of monitoring nodes each generating a series of current monitoring node values over time that represent current operation of the cyber-physical system. According to some embodiments, a watermarking computer platform may randomly inject a watermarking signal into an injection subset of the system nodes. The watermarking computer platform may then receive current monitoring node values over time and generate a current watermarking feature vector based on the current monitoring node values. The watermarking computer platform might comprise a dedicated watermarking abnormality detection platform or a unified abnormality detection platform (e.g., that also uses data-drive feature vectors). The injection subset may be associated with a randomly selected subset of the system nodes and/or magnitudes of watermarking signals that are randomly selected.

Abstract: A cyber-physical system may have a plurality of system nodes including a plurality of monitoring nodes each generating a series of current monitoring node values over time that represent current operation of the cyber-physical system. According to some embodiments, a watermarking computer platform may randomly inject a watermarking signal into an injection subset of the system nodes. The watermarking computer platform may then receive current monitoring node values over time and generate a current watermarking feature vector based on the current monitoring node values. The watermarking computer platform might comprise a dedicated watermarking abnormality detection platform or a unified abnormality detection platform (e.g., that also uses data-drive feature vectors). The injection subset may be associated with a randomly selected subset of the system nodes and/or magnitudes of watermarking signals that are randomly selected.

Abstract: A verification platform may include a data connection to receive a stream of industrial asset cyber-attack detection algorithm data, including a subset of the industrial asset cyber-attack detection algorithm data. The verification platform may store the subset into a data store (the subset of industrial asset cyber-attack detection algorithm data being marked as invalid) and record a hash value associated with a compressed representation of the subset of industrial asset cyber-attack detection algorithm data combined with metadata in a secure, distributed ledger.

Abstract: A cyber-physical system may have a plurality of system nodes including a plurality of monitoring nodes each generating a series of current monitoring node values over time that represent current operation of the cyber-physical system. According to some embodiments, a watermarking computer platform may randomly inject a watermarking signal into an injection subset of the system nodes. The watermarking computer platform may then receive current monitoring node values over time and generate a current watermarking feature vector based on the current monitoring node values. The watermarking computer platform might comprise a dedicated watermarking abnormality detection platform or a unified abnormality detection platform (e.g., that also uses data-drive feature vectors). The injection subset may be associated with a randomly selected subset of the system nodes and/or magnitudes of watermarking signals that are randomly selected.

Abstract: A dispatch optimization system leverages ambient and market forecast data as well as asset performance and parts-life models to generate recommended operating schedules for gas turbines or other power-generating plant assets that substantially maximize profit while satisfying parts-life constraints. The system generates operating profiles that balance optimal peak fire opportunities with optimal cold part-load opportunities within a maintenance interval or other operating horizon. During real-time operation of the assets, the optimization system can update the operating schedule based on actual market, ambient, and operating data. The system provides information that can assist operators in determining suitable conditions in which to cold part-load or peak-fire the assets in an optimally profitable manner without violating target life constraints.

Abstract: A dispatch optimization system leverages ambient and market forecast data as well as asset performance and parts-life models to generate recommended operating schedules for gas turbines or other power-generating plant assets that substantially maximize profit while satisfying parts-life constraints. The system generates operating profiles that balance optimal peak fire opportunities with optimal cold part-load opportunities within a maintenance interval or other operating horizon. During real-time operation of the assets, the optimization system can update the operating schedule based on actual market, ambient, and operating data. The system provides information that can assist operators in determining suitable conditions in which to cold part-load or peak-fire the assets in an optimally profitable manner without violating target life constraints.

Abstract: A bearing assembly for a first part that rotates with respect to a second part may include a slewing bearing having an axis of rotation and inner and outer races concentric with the axis of rotation. The inner race may be fixed to the first part and the outer race may be fixed to the second part. An inner, annular catch ring may be fixed to and adjacent to a lower surface of the inner race. An outer, annular catch ring may be fixed to and interposed between a lower surface of the outer race and the second part. A portion of the outer catch ring may be disposed below and adjacent to the lower surface of the inner race.