Lockheed Martin Corporation

Eric W Worden

Gregory Anthony Harrison

Sreerupa Das

Michael Allen Bodkin

Richard M Hall

Stefan Herzog, Jr

David A Smith

Jonathan Charles Brant

Michelle L Harris

Keith Hollister

Thomas Wonneberger

Jason Tr Smith

David S Maynard

Gregory Anhony Harrison

A system, method, and computer-readable instructions for a distributed machine learning system are provided. A plurality of distributed learning environments are in communication over a network, wherein each environment has a computing device having a memory and a processor coupled to the memory, the processor adapted implement a learning environment via one or more agents in a rules-based system, wherein the agents learn to perform tasks in their respective learning environment; and a persistent storage in which knowledge comprising a plurality of rules developed by the agents for performing the tasks are stored, wherein the knowledge is tagged and shared with other agents throughout the plurality of distributed learning environments.

Distributed machine learning intelligence development systems

Quantification of a condition of a selected item using a neural network is disclosed. A device defines a good hypertube in a neural state space based on good item state points obtained from one or more items that exhibit desired operating characteristics, and a bad hypertube in the neural state space based on bad item state points obtained from one or more items that exhibit undesirable operating characteristics. A current item state hyperpoint is determined in the neural state space based on a current item state point of the selected item. A condition of the selected item is quantified as a function of a location of the current item state hyperpoint with respect to at least a portion of the good hypertube and with respect to at least a portion of the bad hypertube.

Quantifying a condition with a neural network

Estimating a remaining useful life (RUL) of an apparatus is disclosed. A computer device may obtain a priori RUL data of an apparatus. The a priori RUL data identifies a priori RULs values of the apparatus as a function of time. Buckets are then defined in the a priori RUL data, wherein each of the buckets corresponds to a different set of the a priori RUL values in the a priori RUL data. An operational event indicator may then be obtained for the apparatus that indicates a current operational event of the apparatus. The RUL of an apparatus is estimated by determining probability values throughout a time period. Probability values are then determined based on the operational event indicator where each probability value quantifies a probability that a current RUL value of the first apparatus is within one of the buckets.

Adaptive learning network for fusion of remaining useful life with machinery condition

A system, method, and computer-readable instructions for real-time characters with learning capabilities. A plurality of rules are defined in a rules-based system, each of the rules defining a condition that determines a behavior of a virtual agent when the rule is triggered by the condition being satisfied so that upon triggering of multiple rules at the same time, each of the behaviors of the multiple rules whose conditions were satisfied are combined into a resultant behavior for the virtual agent. This resultant behavior is compared with a desired behavior to providing feedback in the form of rewards or punishments to each of the multiple rules based on their corresponding contribution to the resultant behavior as compared to the desired behavior.

Real-time intelligent virtual characters with learning capabilities

Real-time condition-based analysis is performed on a machine for providing diagnostic and prognostic outputs indicative of machine status includes a signal processor for receiving signals from sensors adapted for measuring machine performance parameters. The signal processor conditions and shapes at least some of the received signals into an input form for a neural network. A fuzzy adaptive resonance theory neural network receives at least some of the conditioned and shaped signals, and detects and classifies a state of the machine based upon the received conditioned and shaped signals, and upon a predetermined ontology of machine states, diagnostics, and prognostics. The neural network can also determine from the machine state a health status thereof, which can comprise an anomaly, and output a signal representative of the determined health status. A Bayesian intelligence network receives the machine state from the neural network and determines a fault probability at a future time.

Condition-based monitoring system for machinery and associated methods

A system and a method enabling a software agent to learn about its environment, and learn how to perform jobs and tasks. Novel information is assimilated and used in an adaptive manner by the genetically evolving rule set of the agent. The agent learning system communicates with the external environment in an artificial economy through on-line auctions. Internal information also passes through message auctions, that provide chaining of rule execution. The agent continually attempts to automatically improve its performance with respect to fitness for a job.

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Orlando, FL, United States of America

Eric W Worden