Abstract: A control system for active stability management of a compressor element of a turbine engine is provided. In one example aspect, the control system includes one or more computing devices configured to receive data indicative of an operating characteristic associated with the compressor element. For instance, the data can be received from a high frequency sensor operable to sense pressure at the compressor element. The computing devices are also configured to determine, by a machine-learned model, a stall margin remaining of the compressor element based at least in part on the received data. The machine-learned model is trained to recognize certain characteristics of the received data and associate the characteristics with a stall margin remaining of the compressor element. The computing devices are also configured to cause adjustment of one or more engine systems based at least in part on the determined stall margin remaining.

Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising a Hypothesis Generation Engine (HGE) to receive one or more property target values for a material; a memory for storing program instructions; an HGE processor, coupled to the memory, and in communication with the HGE, and operative to execute program instructions to: receive the one or more property target values for the material; analyze the one or more property target values as compared to one or more known values in a knowledge base; generate, based on the analysis, an initial set of hypothetical structures, wherein each hypothetical structure includes at least one property target value; execute a likelihood model for each candidate material to generate a likelihood probability for each hypothetical structure, wherein the likelihood probability is a measure of the likelihood that the hypothetical structure will have the target property value; convert each hypothetical structure into a natural

Abstract: A control system for active stability management of a compressor element of a turbine engine is provided. In one example aspect, the control system includes one or more computing devices configured to receive data indicative of an operating characteristic associated with the compressor element. For instance, the data can be received from a high frequency sensor operable to sense pressure at the compressor element. The computing devices are also configured to determine, by a machine-learned model, a stall margin remaining of the compressor element based at least in part on the received data. The machine-learned model is trained to recognize certain characteristics of the received data and associate the characteristics with a stall margin remaining of the compressor element. The computing devices are also configured to cause adjustment of one or more engine systems based at least in part on the determined stall margin remaining.

Abstract: According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising a Hypothesis Generation Engine (HGE) to receive one or more property target values for a material; a memory for storing program instructions; an HGE processor, coupled to the memory, and in communication with the HGE, and operative to execute program instructions to: receive the one or more property target values for the material; analyze the one or more property target values as compared to one or more known values in a knowledge base; generate, based on the analysis, an initial set of hypothetical structures, wherein each hypothetical structure includes at least one property target value; execute a likelihood model for each candidate material to generate a likelihood probability for each hypothetical structure, wherein the likelihood probability is a measure of the likelihood that the hypothetical structure will have the target property value; convert each hypothetical structure into a natural

Abstract: A controller includes a processor and memory. The memory stores instructions that, when executed, are configured to cause the processor to receive measurements pertaining to a measured operation parameter of at least a portion of a turbine system. The instructions are also configured to cause the processor to generate a customized model for the at least the portion of the turbine system. Moreover, the instructions are configured to cause the processor to estimate an estimated value using the received measurements. The estimated value pertains to a parameter of the turbine system. Furthermore, the instructions are configured to cause the processor to using the customized model, reduce or remove at least some environmental conditions from a corrected estimated value derived from the estimated value.

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: A method for imputing multivariate-time series data in a predictive model includes performing historical training of the predictive model by accessing data element information obtained from a real world physical asset, the data element information representing operational characteristics or measurements of the real world physical asset, examining configuration details of the real world physical asset, evaluating an expressiveness of the predictive model by comparing the predicative model to the configuration details, developing the model to express the configuration details, training the developed model by running scenarios based on the data element information, comparing error metrics between a model prediction and a corresponding one of the data element information, deploying the model if the error metrics are within predetermined parameters, and retraining the model if the error metrics are outside the predetermined parameters.

Abstract: An automated prognostics system includes a sensor system configured to obtain measurement data by monitoring one or more parameters at one or more locations on each of one or more movable components of an object when the object is subjected to a first mode of operation. The system also includes a computing device having an input interface and a processor. The input interface receives input information such as spatial coordinates information associated with the one or more locations, the measurement data obtained by the sensor system, operational data associated with the first mode of operation, and structural data associated with the object. The processor processes the input information and generates a prognostics report on at least a first component of the one or more movable components, the prognostics report including at least one of a failure prognostic or a likelihood-of-failure prognostic of the first component.

Abstract: A controller includes a processor and memory. The memory stores instructions that, when executed, are configured to cause the processor to receive measurements pertaining to a measured operation parameter of at least a portion of a turbine system. The instructions are also configured to cause the processor to generate a customized model for the at least the portion of the turbine system. Moreover, the instructions are configured to cause the processor to estimate an estimated value using the received measurements. The estimated value pertains to a parameter of the turbine system. Furthermore, the instructions are configured to cause the processor to using the customized model, reduce or remove at least some environmental conditions from a corrected estimated value derived from the estimated value.

Abstract: An automated analytics system can include a sensor system that obtains measurement data by monitoring one or more parameters at each of a number of locations on each of a number of replicated components of an object. A computing device receives the measurement data from the sensor system and uses the measurement data to automatically generate a computerized representation of each of the plurality of replicated components. Thereafter, upon receipt of an input query, the computing device generates a synthesized representation of the object that is specifically directed to a parameter of interest indicated in the query. The synthesized representation may be displayed in a visual format that is interpretable by a human to derive information associated with the parameter of interest.

Abstract: An automated analytics system can include a sensor system that obtains measurement data by monitoring one or more parameters at each of a number of locations on each of a number of replicated components of an object. A computing device receives the measurement data from the sensor system and uses the measurement data to automatically generate a computerized representation of each of the plurality of replicated components. Thereafter, upon receipt of an input query, the computing device generates a synthesized representation of the object that is specifically directed to a parameter of interest indicated in the query. The synthesized representation may be displayed in a visual format that is interpretable by a human to derive information associated with the parameter of interest.

Abstract: An automated prognostics system includes a sensor system configured to obtain measurement data by monitoring one or more parameters at one or more locations on each of one or more movable components of an object when the object is subjected to a first mode of operation. The system also includes a computing device having an input interface and a processor. The input interface receives input information such as spatial coordinates information associated with the one or more locations, the measurement data obtained by the sensor system, operational data associated with the first mode of operation, and structural data associated with the object. The processor processes the input information and generates a prognostics report on at least a first component of the one or more movable components, the prognostics report including at least one of a failure prognostic or a likelihood-of-failure prognostic of the first component.

Abstract: According to some embodiments, a model building platform may receive a set of historic industrial plant parameters associated with operation of a plurality of industrial plants over a period of time. The model building platform may automatically create a generative model based on relationships detected within the set of historic industrial plant parameters. A model execution platform may then receive incomplete industrial plant information associated with a particular industrial plant, and automatically generate supplemented industrial plant data based on the received incomplete industrial plant information and the generative model. An indication of the supplemented industrial plant data may then be output.

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: A system, method and computer-readable medium for monitoring a life of a gas turbine component is disclosed. A numerical model of the gas turbine component is created and parameter measurements are obtained in real-time for at least a portion of the gas turbine component. The parameter measurements are fused with a subset of the numerical model corresponding to the portion of the gas turbine component to obtain a subset of a fused parameter model corresponding to the portion of the gas turbine component. The subset of the fused parameter model is expanded to obtain the fused parameter model that corresponds to at least a location outside of the portion of the gas turbine component. The life of the gas turbine component is monitored using the fused temperature model.