Abstract: An industrial asset item definition data store may contain at least one electronic record defining the industrial asset item. An automated support structure creation platform may include a support structure optimization computer processor. The automated support structure optimization computer processor may be adapted to automatically create support structure geometry data associated with an additive printing process for the industrial asset item. The creation may be performed via an iterative loop between a build process simulation engine and a topology optimization engine.

Abstract: A method includes receiving a first time-dependent data characterizing measurement by a first sensor operatively coupled to an oil and gas industrial machine; determining a first anomaly score associated with a first portion of the first time-dependent data over a time period, the determination is based on a first value of an operating characteristic over the time period and a second value of the operating characteristic over the time period, wherein the first value of the operating characteristic is calculated from the first time-dependent data and the second value of the operating characteristic is detected at the oil and gas industrial machine; and rendering, in a graphical user interface display space, a visual representation indicative of the first anomaly score. Related apparatus, systems, articles, and techniques are also described.

Abstract: According to some embodiments, a system may include a design experience data store containing electronic records associated with prior industrial asset item designs. A deep learning model platform, coupled to the design experience data store, may include a communication port to receive constraint and load information from a designer device. The deep learning platform may further include a computer processor adapted to automatically and generatively create boundaries and geometries, using a deep learning model associated with an additive manufacturing process, for an industrial asset item based on the prior industrial asset item designs and the received constraint and load information. According to some embodiments, the deep learning model computer processor is further to receive design adjustments from the designer device. The received design adjustments might be for example, used to execute an optimization process and/or be fed back to continually re-train the deep learning model.

Abstract: Data characterizing a load on a rod of a down-hole pump at different positions of a stroke of the rod can be received. An image characterizing the load of the rod at the different positions of the stroke can be determined. A defect in operating conditions of the down-hole pump can be determined. The determining can include comparing the image to a set of predetermined images with associated defects. The determined defect can be provided.

Abstract: Sensor data is received characterizing operational data associated with a machine. A data analytic specification is received from a user. The received operational data can be processed using the data analytic. A visual representation of the processed operational data can be rendered, within a graphical user interface display space. A digital model characterizing the machine can be generated based on user input received in the graphical user interface. Additional sensor data can be received. The digital model can be updated based on the received additional sensor data. A recommendation for modifying an input value and/or an operational parameter of the machine can be determined using the updated model. The recommendation can be rendered within the graphical user interface space. Related apparatus, systems, techniques and articles are also described.

Abstract: Systems, methods, and a computer readable medium are provided for generating natural language recommendations based on an industrial language model. Input data, including a plurality of natural language input units is received from a first computing device and transmitted to a second computing device. The second computing device can determine an output including a plurality of natural language outputs using a predictive model trained to generate the output corresponding to an energy exploration lexicon. The second computing device can provide the output to the first computing device for display.

Abstract: A unit cell structure is provided. The unit cell structure includes a first section and a second section. The first section defines a first load path and includes a first plurality of first unit cells joined together. The second section defines a second load path separate from the first load path and includes a second plurality of second unit cells joined together, each second unit cell of the second plurality of second unit cells nested within and spaced apart from each first unit cell of the first plurality of first unit cells of the first section.

Abstract: In one implementation, a method includes generating a manifold predictive model configured to calculate an initial virtual measurement associated with an oil field comprising a plurality of oil wells. The manifold predictive model can be based on one or more predictive models associated with one or more components of the oil field. The method also includes receiving data characterizing one or more pressure measurements and flow measurements obtained in the oil field. The method further includes determining a prospective sensor location of a first prospective sensor in the oil field. The first prospective sensor can be configured to detect an oil field parameter. The manifold predictive model can be configured to receive data characterizing the detected oil field parameter and generate an updated virtual measurement. The method also includes providing the prospective sensor location and the identity of the first prospective sensor.

Abstract: According to some embodiments, system and methods are provided comprising: receiving data; providing a simulation model for the data; generating one or more simulations via a Bayesian module based on the data, wherein the simulation includes one or more nodes in a chain; executing the Bayesian module to determine the acceptability of the nodes in the simulation based on a Bayesian rule, wherein execution of the Bayesian module further comprises: generating a binary decision tree representing the chain in the simulation, wherein the chain includes one or more nodes; prioritizing which nodes in the tree to simulate; generating one or more simulations; executing the simulation model with data associated with the prioritized nodes in the tree in parallel to determine a posterior probability for each prioritized node; and determining whether each prioritized node is accepted or rejected based on the posterior probabilities. Numerous other aspects are provided.

Abstract: A testing system computer device for dynamically updating a test plan of an apparatus includes at least one processor in communication with at least one memory device. The testing system computer device is configured to store a plurality of historical data and generate a simulation model of the apparatus based in part on the historical data. The simulation model includes a plurality of inputs and a plurality of outputs of the apparatus. The testing system computer device is also configured to determine a plurality of tests to perform on the apparatus based on the simulation model and the plurality of historical data. The testing system computer device is further configured to receive a plurality of desirability ratings from a user, rank the plurality of tests to perform based on the plurality of desirability ratings, and present the ranked plurality of tests to the user.

Abstract: A monitoring system can include a dashboard including a graphical user interface display space. The monitoring system can also include a digital twin validation system including a processor configured to perform operations including rendering, in a graphical user interface display space, an interactive graphical object characterizing a first input value of a first digital model of a plurality of oil and gas industrial machines. The operations can also include receiving data characterizing user interaction with the interactive graphical object. The data characterizing the user interaction can be indicative of the first input value of the first digital model. The operations can further include updating the first digital model to generate updated output values representative of an operational parameter of the plurality of oil and gas industrial machines. The operations can also include rendering, in the graphical user interface display space, a visual representation of the updated output values.

Abstract: A component is provided. The component includes a structure including a plurality of unit cells joined together, each unit cell of the plurality of unit cells having a mass density substantially similar to the mass density of every other unit cell of the plurality of unit cells. The plurality of unit cells includes a first portion of unit cells having a characteristic dimension and a first portion average stiffness, the characteristic dimension of the first portion of unit cells having a first value. The plurality of unit cells also includes a second portion of unit cells having the characteristic dimension and a second portion average stiffness, the characteristic dimension of the second portion of unit cells having a second value different from the first value, wherein the second portion average stiffness differs from the first portion average stiffness.

Abstract: A controller for use in an additive manufacturing system including a consolidation device configured to consolidate material is provided. The controller is configured to receive a build file for a component including a plurality of build layers, wherein each build layer includes a component outer perimeter, at least one build layer generating function, at least one generating function variable, and at least one generating function constant. The controller is configured to generate at least one control signal to control a power output throughout at least one scan path of the consolidation device across the material for each build layer of the plurality of build layers, the at least one scan path generated based at least partially on the component outer perimeter, the at least one generating function, the at least one generating function variable, and the at least one generating function constant for each build layer.

Abstract: According to some embodiments, system, apparatus and methods are provided comprising an analytic model for an installed product; an execution platform configured to execute the analytic model; an application programming interface (API) wrapper associated with each of the analytic model and the execution platform, the API wrapper including input information, output information and a technique; and a storage in communication with the analytic model and the execution platform and storing program instructions to perform the functions as follows: transmitting information between the analytic API wrapper and the execution platform API wrapper; and deploying the analytic model to the execution platform based on the transmitted information. Numerous other aspects are provided.

Abstract: A component has a first structural configuration and a second structural configuration. The component includes a sensor assembly including a plurality of interconnected structural members defining a plurality of load paths. A first structural member and a second structural member define a first load path when the component is in the first structural configuration. The first structural member and a third structural member define a second load path when the component is in the second structural configuration. The second load path is configured to bypass the second structural member. The sensor assembly is configured to detect a characteristic of the component that changes when the component switches between the first structural configuration and the second structural configuration.

Abstract: According to some embodiments, system, apparatus and methods are provided comprising one or more component models of an analytic model for an installed product; an application programming interface (API) wrapper associated with each of the one or more component models, the API wrapper including information about one or more inputs to the component model; and wherein the component model and the API wrapper form a self-aware component. Numerous other aspects are provided.

Abstract: According to some embodiments, system, apparatus and methods are provided comprising a platform hosting one or more elements; an application programming interface (API) wrapper associated with each of the one or more elements, the API wrapper including input information to the one or more elements, output information to the one or more elements, and at least one instruction defining a function of the element; and wherein the one or more elements and the API wrapper form a self-aware element. Numerous other aspects are provided.

Abstract: A unit cell structure is provided. The unit cell structure includes a first section and a second section. The first section defines a first load path and includes a first plurality of first unit cells joined together. The second section defines a second load path separate from the first load path and includes a second plurality of second unit cells joined together, each second unit cell of the second plurality of second unit cells nested within and spaced apart from each first unit cell of the first plurality of first unit cells of the first section.

Abstract: A system for estimating data in large datasets for an equipment system is provided. The system includes a data estimation and forecasting (DEF) computing device. The DEF computing device arranges a dataset in a primary matrix and parses rows of the primary matrix and generates a sample matrix by selecting primary matrix rows having non-null values for each variable. The DEF computing device adds to the sample matrix rows that include non-null values for each variable except one. The DEF computing device generates normalized values for this augmented matrix, applies several techniques including probabilistic principal component analysis (PPCA) and Markov processes, and scales the augmented matrix to normalized values. The DEF computing device generates non-null values for the variable, scales the augmented matrix back to the sample matrix, and generates a forecast for the equipment system, directing a user to update logistics processes for the equipment system.

Abstract: Systems, methods, and a computer readable medium are provided for generating natural language recommendations based on an industrial language model. Operational data associated with an operating condition of a pump is received and provided as inputs to a predictive model used to determine diagnostic and prognostic data associated with the pump. The diagnostic and prognostic data can include a plurality of metrics associated with the pump that are predicted in relation to the operational data. The predicted diagnostic and prognostic data can be transmitted by and/or to one or more computing systems.