Abstract: In an example embodiment, a method of calculating end-of-life (EOL) predictions for a physical asset is provided. A state-space model for the physical asset is obtained, the state-space model being a physics-based model describing a state of the physical asset at a particular time given measurements or observations for the physical asset. Then a current state of the physical asset is inferred. Then a long-term prediction is derived for the physical asset based on the inferred current state of the physical asset and the state-space model for the physical asset. Then an EOL probability distribution function is generated for the physical asset based on the long-term prediction, the EOL probability distribution function describing a range of estimates of EOL for the physical asset and their corresponding confidence intervals.

Abstract: A method for determining fleet conditions and operational management thereof, performed by a central system includes receiving fleet data from at least one distributed data repository. The fleet data is substantially representative of information associated with a fleet of physical assets. The method also includes processing the received fleet data for the fleet using at least one process of a plurality of processes. The plurality of processes assess the received fleet data into processed fleet data. The method additionally includes determining a fleet condition status using the processed fleet data and the at least one process of the plurality of processes. The method further includes generating a fleet response. The fleet response is substantially representative of a next operational step for the fleet of physical assets. The method also includes transmitting the fleet response to at least one of a plurality of fleet response recipients.

Abstract: A method for determining fleet conditions and operational management thereof, performed by a central system includes receiving fleet data from at least one distributed data repository. The fleet data is substantially representative of information associated with a fleet of physical assets. The method also includes processing the received fleet data for the fleet using at least one process of a plurality of processes. The plurality of processes assess the received fleet data into processed fleet data. The method additionally includes determining a fleet condition status using the processed fleet data and the at least one process of the plurality of processes. The method further includes generating a fleet response. The fleet response is substantially representative of a next operational step for the fleet of physical assets. The method also includes transmitting the fleet response to at least one of a plurality of fleet response recipients.

Abstract: In an example embodiment, a method of calculating end-of-life (EOL) predictions for a physical asset is provided. A state-space model for the physical asset is obtained, the state-space model being a physics-based model describing a state of the physical asset at a particular time given measurements or observations for the physical asset. Then a current state of the physical asset is inferred. Then a long-term prediction is derived for the physical asset based on the inferred current state of the physical asset and the state-space model for the physical asset. Then an EOL probability distribution function is generated for the physical asset based on the long-term prediction, the EOL probability distribution function describing a range of estimates of EOL for the physical asset and their corresponding confidence intervals.

Abstract: A system and method for extracting a resource from a reservoir repeatedly alternates between injecting a fluid and injecting a gas into the reservoir. A rate and/or an amount of each of the fluid and the gas that is injected into the reservoir is defined by a first fluid-and-gas ratio function that designates different ratios as a function of time. The ratios designate the rate and/or the amount of the fluid that is injected into the reservoir to the rate and/or the amount of the gas that is injected into the reservoir. The rate and/or the amount at which the fluid and/or the gas is injected into the reservoir is changed according to the ratios designated by the first fluid-and-gas ratio function as time progresses.

Abstract: A system and method for leveraging industrial asset computing capacity into a distributed computing system is disclosed. The method including receiving from a requesting entity a request for available computing capacity, the request including at least one computational task, determining the available computing capacity across a network of interconnected industrial assets, each industrial asset including at least one central controller, allocating at least one of the interconnected industrial assets to perform at least a portion of the computational request, providing the allocated industrial assets with computer executable instructions and data related to performing the computational request, performing at least a portion of the computational request at the allocated industrial assets, obtaining the results of the performing step from the allocated industrial assets; and returning the obtained results to the requesting entity. A system to implement the method and a computer-readable medium are disclosed.

Abstract: A method, system, and non-transitory computer-readable medium, the method including determining automatically, by a processor, whether behavior for a model representing a plurality of entities and relationships therebetween deviates from a reference behavior for the model; determining, in response to the determination that the model does deviate from the reference behavior, at least one basis for the deviation; automatically forecasting an estimate of a remaining useful life for the model; and modifying the model to compensate for the deviation by at least one of modifying the model to accommodate the deviation and updating the model based on at least one new requirement.

Abstract: Starter control valve failure prediction machines, systems, computer readable media, program products, and computer implemented methods to predict and trend starter control valve failures in gas turbine engines using a starter control valve health prognostic and to make predictions of starter control valve failures, are provided.

Abstract: A system includes a library of algorithms, and a request module configured to receive an execution request. The system also includes a job scheduler/optimizer module configured to select algorithms from the library and to create at least one execution job based on the algorithms and the execution request. The system further includes a resource module configured to determine execution computing resources from multiple computing sources, including internal computing resources and external computing resources. The system also includes an executor module configured to transmit an execution job to the computing resources.

Abstract: A computer-implemented system for identifying a precursor to a failure of a particular type of component in a physical system is provided. The physical system includes sensors coupled to the physical system. The computer-implemented system includes a computing device, a database, a processor, and a memory device. The memory device includes historical data including sensor measurements. When instructions are executed by the processor, the processor receives the historical data from the memory device. The processor generates a predictive model. The predictive model uses, as inputs, sensor measurements in the historical data. The predictive model is able to differentiate between sensor measurements taken before the repair event and those taken after the repair event without a time of the repair event being an input to the predictive model. The processor designates at least one sensor measurements used as inputs to the predictive model as precursors to the failure of the component.

Abstract: A method for determining fleet conditions and operational management thereof, performed by a central system includes receiving fleet data from at least one distributed data repository. The fleet data is substantially representative of information associated with a fleet of physical assets. The method also includes processing the received fleet data for the fleet using at least one process of a plurality of processes. The plurality of processes assess the received fleet data into processed fleet data. The method additionally includes determining a fleet condition status using the processed fleet data and the at least one process of the plurality of processes. The method further includes generating a fleet response. The fleet response is substantially representative of a next operational step for the fleet of physical assets. The method also includes transmitting the fleet response to at least one of a plurality of fleet response recipients.

Abstract: A computer-implemented system for creating customized model ensembles on demand is provided. An input module is configured to receive a query. A selection module is configured to create a model ensemble by selecting a subset of models from a plurality of models, wherein selecting includes evaluating an aspect of applicability of the models with respect to answering the query. An application module is configured to apply the model ensemble to the query, thereby generating a set of individual results. A combination module is configured to combine the set of individual results into a combined result and output the combined result, wherein combining the set of individual results includes evaluating performance characteristics of the model ensemble relative to the query.

Abstract: Starter control valve failure prediction machines, systems, program products, and computer implemented methods to predict and trend starter control valve failures in gas turbine engines using a starter control valve health prognostic and to make predictions of starter control valve failures, are provided. A computer implemented method according to an embodiment of the present invention can include the steps of generating a continuous starter control valve deterioration trend function responsive to a plurality of health indices derived from gas turbine engine startup data downloaded from gas turbine engine sensors for a plurality of startups and analyzing the continuous starter control valve deterioration trend function to identify potential starter control valve failure points where the points on the starter control valve deterioration trend function correlate to a starter control valve health prognostic responsive to historic gas turbine engine startup data downloaded from gas turbine engine sensors.

Abstract: The systems and methods of the invention are directed to portfolio optimization and related techniques. For example, the invention provides a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the method comprising: generating an initial population of solutions of portfolio allocations; committing the initial population of solutions to an initial population archive; performing a multi-objective process, based on the initial population archive and on multiple competing objectives, to generate an efficient frontier, the multi-objective process including a evolutionary algorithm process, the evolutionary algorithm process utilizing a dominance filter, the efficient frontier being used in investment decisioning.

Abstract: A method and system for fusing a collection of classifiers used for an automated insurance underwriting system and/or its quality assurance is described. Specifically, the outputs of a collection of classifiers are fused. The fusion of the data will typically result in some amount of consensus and some amount of conflict among the classifiers. The consensus will be measured and used to estimate a degree of confidence in the fused decisions. Based on the decision and degree of confidence of the fusion and the decision and degree of confidence of the production decision engine, a comparison module may then be used to identify cases for audit, cases for augmenting the training/test sets for re-tuning production decision engine, cases for review, or may simply trigger a record of its occurrence for tracking purposes. The fusion can compensate for the potential correlation among the classifiers.

Abstract: The system and method disclosed herein provides an integrated and automated workflow, sensor, and reasoning system that automatically detects breaches in protocols, appropriately alarms and records these breaches, facilitates staff adoption of protocol adherence, and ultimately enables the study of protocols for care comparative effectiveness. The system provides real-time alerts to medical personnel in the actual processes of care, thereby reducing the number of negative patient events and ultimately improving staff behavior with respect to protocol adherence.

Abstract: The systems and methods of the invention are directed to portfolio optimization and related techniques. For example, the invention provides a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the method comprising: generating an initial population of solutions of portfolio allocations, the generating the initial population of solutions of portfolio allocations including systematically generating the initial population of solutions to substantially cover the space defined by the competing objectives and the plurality of constraints; and generating an efficient frontier in the space based on the initial population, the efficient frontier for use in investment decisioning.

Abstract: A system for collecting and storing performance data for an engine is provided. The system includes one or more sensors configured to generate sensor data signals representative of one or more engine data performance parameters. The system further includes a data sampling component, a data quantizing component, a data storage sampling rate component, a data encoding component and a data storage component. The data sampling component is configured to sample the sensor data signals at a data sampling rate. The data quantizing component is configured to generate quantized data samples corresponding to the sampled sensor data signals. The data storage sampling rate component is configured to determine a data storage sampling rate for the quantized data samples, based on an analysis of at least a subset of the quantized data samples.

Abstract: Monitoring dynamic units that operate in complex, dynamic environments, is provided in order to classify and track unit behavior over time. When domain knowledge is available, feature-based models may be used to capture the essential state information of the units. When domain knowledge is not available, raw data is relied upon to perform this task. By analyzing logs of event messages (without having access to their data dictionary), embodiments allow the identification of anomalies (novelties). Specifically, a Normalized Compression Distance (such as one based on Kolmogorov Complexity) may be applied to logs of event messages. By analyzing the similarity and differences of the event message logs, units are identified that did not experience any abnormality (and locate regions of normal operations) and units that departed from such regions.

Abstract: Starter control valve failure prediction machines, systems, program products, and computer implemented methods to predict and trend starter control valve failures in gas turbine engines using a starter control valve health prognostic and to make predictions of starter control valve failures, are provided. A computer implemented method according to an embodiment of the present invention can include the steps of generating a continuous starter control valve deterioration trend function responsive to a plurality of health indices derived from gas turbine engine startup data downloaded from gas turbine engine sensors for a plurality of startups and analyzing the continuous starter control valve deterioration trend function to identify potential starter control valve failure points where the points on the starter control valve deterioration trend function correlate to a starter control valve health prognostic responsive to historic gas turbine engine startup data downloaded from gas turbine engine sensors.