Abstract: According to some embodiments, a system and method are provided to create a template associated with an industrial problem. The method comprises receiving one or more kernels from a machine learning library. The one or more kernels are then aggregated, via a processor, into a template. The template is integrated into a user interface where is may be executed.

Abstract: According to some embodiments, system and methods for building a model are provided, comprising a display; a memory storing processor-executable process steps; and a processor to execute the processor-executable process steps to cause the system to: present a user interface on a display, the user interface including one or more user-entry fields to build a model, user-entry fields is associated with a selection of big data or small data for use with the model; receive at least one data source in a user-entry field associated with the model; determine if data in the data source includes big data or small data; and in response to the determination of big data or small data in the data source, execute the model with data from the data source in a big data or small data execution environment. Numerous other aspects are provided.

Abstract: According to some embodiments, a system and method are provided to model a sparse data asset. The system comprises a processor and a non-transitory computer-readable medium comprising instructions that when executed by the processor perform a method to model a sparse data asset. Relevant data and operational data associated with the newly operational are received. A transfer model based on the relevant data and the received operational data. An input into the transfer model is received and a predication based on data associated with the received operational data and the relevant data is output.

Abstract: According to some embodiments, system and methods are provided, comprising receiving, at a machine health module, non-parametric data associated with operation of an installed product; generating, via the machine health module, a health status for at least one of a failure type and a remaining useful life of the installed product, based on the received non-parametric data; and generating an operating response of the installed product based on the generated health status. Numerous other aspects are provided.

Abstract: A system and method for associating equipment sensor tags with tags associated with analytic programs including embodiments that receive a list of equipment tag names and corresponding descriptions as well as a list of analytic tag names and descriptions. The equipment tag descriptions are normalized to create normalized equipment tag descriptions, and the analytic tag descriptions are normalized to create normalized analytic tag descriptions. A first matrix of vectors that associates content in a dictionary with aspects of the equipment tag names is created using corresponding normalized tag descriptions. A second matrix of vectors that associates the content in a dictionary with aspects of the normalized analytic tag description is created.

Abstract: According to some embodiments, a system and method are provided to model a sparse data asset. The system comprises a processor and a non-transitory computer-readable medium comprising instructions that when executed by the processor perform a method to model a sparse data asset. Relevant data and operational data associated with the newly operational are received. A transfer model based on the relevant data and the received operational data. An input into the transfer model is received and a predication based on data associated with the received operational data and the relevant data is output.

Abstract: According to some embodiments, system and methods are provided, comprising receiving, at a machine health module, non-parametric data associated with operation of an installed product; generating, via the machine health module, a health status for at least one of a failure type and a remaining useful life of the installed product, based on the received non-parametric data; and generating an operating response of the installed product based on the generated health status. Numerous other aspects are provided.

Abstract: The example embodiments are directed to a system and method for integrating domain knowledge into a feature discovery process. In an example, the method includes receiving data associated with an operation of an asset, receiving domain knowledge associated with a subject matter of the asset, performing a feature discovery process based on the received data using the domain knowledge to generate a feature set associated with the operation of the asset, wherein the feature discovery processes reduces a search space of possible features in the received data based on the domain knowledge when generating the feature set, and performing an analytic associated with the operation of the asset based on the domain-knowledge-integrated feature set and outputting information concerning results of analytic for display to a display device. By injecting domain knowledge into the feature discovery process, more accurate features can be identified more efficiently.

Abstract: According to some embodiments, a system and method are provided to create a template associated with an industrial problem. The method comprises receiving one or more kernels from a machine learning library. The one or more kernels are then aggregated, via a processor, into a template. The template is integrated into a user interface where is may be executed.

Abstract: Systems and methods for predictive modeling of an industrial asset. In some embodiments, a database stores an electronic file containing a machine learning library and predictive modeling tools associated with the industrial asset. A computer processor accesses the machine learning library and predictive modeling tools, provides a model building framework user interface and receives a selection of a feature engineering (FE) technique, including one of evolutionary feature selection, evolutionary feature synthesis, and symbolic regression. Next, an input selection interface is provided, industrial asset input data and parameter data received, and at least one of an evolutionary feature selection process, an evolutionary feature synthesis process, and a symbolic regression process is executed.

Abstract: According to some embodiments, system and methods for building a model are provided, comprising a display; a memory storing processor-executable process steps; and a processor to execute the processor-executable process steps to cause the system to: present a user interface on a display, the user interface including one or more user-entry fields to build a model, user-entry fields is associated with a selection of big data or small data for use with the model; receive at least one data source in a user-entry field associated with the model; determine if data in the data source includes big data or small data; and in response to the determination of big data or small data in the data source, execute the model with data from the data source in a big data or small data execution environment. Numerous other aspects are provided.

Abstract: A system and method for associating equipment sensor tags with tags associated with analytic programs including embodiments that receive a list of equipment tag names and corresponding descriptions as well as a list of analytic tag names and descriptions. The equipment tag descriptions are normalized to create normalized equipment tag descriptions, and the analytic tag descriptions are normalized to create normalized analytic tag descriptions. A first matrix of vectors that associates content in a dictionary with aspects of the equipment tag names is created using corresponding normalized tag descriptions. A second matrix of vectors that associates the content in a dictionary with aspects of the normalized analytic tag description is created.