Abstract: A system and method including receiving historical time series sensor data associated with operation of an industrial asset; generating visual representation images of scatter plots based on the historical time series sensor data based on a reference to a digitized knowledge domain associated with the industrial asset; assigning a root cause label to each image; generating a convolutional neural network (CNN) model trained and tested using subsets of the labeled images; and processing, by the CNN model, a real-time image to detect at least one anomaly in the real-time image and one or more root causes associated with the at least one anomaly.

Abstract: A method of correcting turbine underperformance includes calculating a power production curve using monitored data, detecting changes between the monitored data and a baseline power production curve, generating operability curves for paired operational variables from the monitored data, detecting changes between the operability curves and corresponding baseline operability curves, comparing the changes to a respective predetermined metric, and if the change exceeds the metric, providing feedback to a turbine control system identifying at least one of the paired operational variables for each paired variable in excess of the metric. A system and a non-transitory computer-readable medium are also disclosed.

Abstract: A method of correcting turbine underperformance includes calculating a power production curve using monitored data, detecting changes between the monitored data and a baseline power production curve, generating operability curves for paired operational variables from the monitored data, detecting changes between the operability curves and corresponding baseline operability curves, comparing the changes to a respective predetermined metric, and if the change exceeds the metric, providing feedback to a turbine control system identifying at least one of the paired operational variables for each paired variable in excess of the metric. A system and a non-transitory computer-readable medium are also disclosed.

Abstract: A system includes a physical analysis module, a cyber analysis module, and a determination module. The physical analysis module is configured to obtain physical diagnostic information, and to determine physical analysis information using the physical diagnostic information. The cyber analysis module is configured to obtain cyber security data of the functional system, and to determine cyber analysis information using the cyber security data. The determination module is configured to obtain the physical analysis information and the cyber analysis information, and to determine a state of the functional system using the physical analysis information and the cyber analysis information. The state determined corresponds to at least one of physical condition or cyber security threat. The determination module is also configured to identify if the state corresponds to one or more of a non-malicious condition or a malicious condition.

Abstract: A computer-implemented system includes an edge module and at least one input device coupled to the edge module. The at least one input device is configured to generate data input signals. The system also includes a cognitive module coupled to the edge module. The cognitive module includes a perception sub-module coupled to the edge module. The perception sub-module is configured to receive the data input signals. The cognitive module also includes a learning sub-module coupled to the perception sub-module. The learning sub-module is configured to adaptively learn at least in part utilizing the data input signals.

Abstract: A method for diagnosing machine faults, includes obtaining sensory data from a machine and obtaining a plurality of measured structural features based on the sensory data. The method also includes obtaining a plurality of reference cases corresponding to the sensory data, from a database. The plurality of reference cases include a plurality of reference structural features and a plurality of fault identifiers. The method further includes computing a statistical parameter based on the plurality of reference cases and obtaining a first subset of reference structural features from the plurality of reference structural features based on the computed statistical parameter. The method also includes computing a plurality of similarity values based on the obtained first subset of reference structural features and the plurality of measured structural features.

Abstract: A system and method for identifying characters using a processor and a sparse distributed memory (SDM) module. The system and method are configured to receive image data relating to an object having a surface with physical markings thereon. The physical markings include characters-of-interest. The system and method are also configured to analyze the image data to convert at least one of the characters-of-interest in the image data into a corresponding feature vector. The system and method are also configured to identify the characters-of-interest using the feature vector and the SDM module. A suggested identity for the characters-of-interest is provided.

Abstract: A system includes a physical analysis module, a cyber analysis module, and a determination module. The physical analysis module is configured to obtain physical diagnostic information, and to determine physical analysis information using the physical diagnostic information. The cyber analysis module is configured to obtain cyber security data of the functional system, and to determine cyber analysis information using the cyber security data. The determination module is configured to obtain the physical analysis information and the cyber analysis information, and to determine a state of the functional system using the physical analysis information and the cyber analysis information. The state determined corresponds to at least one of physical condition or cyber security threat. The determination module is also configured to identify if the state corresponds to one or more of a non-malicious condition or a malicious condition.

Abstract: A computer-implemented system includes an edge module and at least one input device coupled to the edge module. The at least one input device is configured to generate data input signals. The system also includes a cognitive module coupled to the edge module. The cognitive module includes a perception sub-module coupled to the edge module. The perception sub-module is configured to receive the data input signals. The cognitive module also includes a learning sub-module coupled to the perception sub-module. The learning sub-module is configured to adaptively learn at least in part utilizing the data input signals.

Abstract: A system and method for identifying characters using a processor and a sparse distributed memory (SDM) module. The system and method are configured to receive image data relating to an object having a surface with physical markings thereon. The physical markings include characters-of-interest. The system and method are also configured to analyze the image data to convert at least one of the characters-of-interest in the image data into a corresponding feature vector. The system and method are also configured to identify the characters-of-interest using the feature vector and the SDM module. A suggested identity for the characters-of-interest is provided.

Abstract: A method and system are provided for intelligent fuel monitoring and real-time dynamic route planning for a vehicle. The system includes one or more sensor devices monitoring a vehicle status; and a processing system coupled to the one or more sensor devices, the processing system having a memory and a processor, wherein the processor is programmed to: receive a plurality of data such as: vehicle status sensor data, points of interest data, environmental data, user data, route data. The processor then learns driver behavior from the plurality of data that influences selection of driving routes; processes the plurality of data for a current route; determines an optimal route based on the plurality of data processed for the current route and the learned driver behavior; and outputs the optimal route.

Abstract: A mileage estimation system comprising a data collection module, a route determination module, a learning module, and a mileage calculation module is provided. The data collection module receives data corresponding to a position and time of a moving asset from a remote location and the route determination module obtains information from a map database for determining a plurality of routes between at least two location of the moving asset. The learning module in the system determines a route travelled by the moving asset from the plurality of routes based on mileage estimation criterion, and the mileage calculation module estimates the distance travelled by the moving asset based on the route travelled by the moving asset.