Abstract: Systems, methods, and software to facilitate simulating machines used in industrial automation are disclosed herein. In at least one implementation, an API is utilized to establish at least a communication link between a simulation model created in a simulation application and an industrial controller system outside of the simulation model, wherein the simulation model comprises definitions for a virtual representation of at least a portion of a machine used in an industrial automation environment. Data is then exchanged between the industrial controller system and the simulation model over the communication link.

Abstract: A method for optimizing data storage includes obtaining a data object for storage at memory hardware in communication with data processing hardware. The memory hardware includes a plurality of storage devices, each storage device of the plurality of storage devices including storage parameters different from each other storage device of the plurality of storage devices. The method also includes determining one or more data object parameters associated with the data object and predicting, using a model and the data object parameters and the storage parameters, an object temperature representative of a frequency of access for the data object and an object lifetime representative of an amount of time the data object is to be stored. The method further includes selecting, using the predicted object temperature and object lifetime, one of the storage devices, and storing the data object at the selected one of the storage devices.

Abstract: For machine learning data reduction and model optimization,a method randomly assigns each data feature of a training data set to a plurality of solution groups. Each solution group has no more than a solution group number k of data features and each data feature is assigned to a plurality of solution groups. The method identifies each solution group as a high-quality solution group or a low-quality solution group. The method further calculates data feature scores for each data feature comprising a high bin number and a low bin number. The method determines level data for each data feature from the data feature scores using a fuzzy inference system. The method identifies an optimized data feature set based on the level data. The method further trains a production model using only the optimized data feature set. The method predicts a result using the production model.

Abstract: Data flows and data processing modules are provided to fulfill the implementation of: contextualized data collection, scalable analytics, and machine learning operations quality assurance. These modules may be implemented as standalone components, and/or bundled as a group of coordinated microservices. The conversion of raw domain expertise and knowledge into data processors, analytics, and visualization modules for industry oriented Industrial Internet of Things (IIoT) solutions is streamlined.

Abstract: Systems, methods, and software to facilitate simulating machines used in industrial automation are disclosed herein. In at least one implementation, motion of a solid model of a machine is analyzed to generate a motion profile of the solid model. An industrial controller system is then configured based on the motion profile of the solid model. A simulation of the machine controlled by the industrial controller system is executed, and motion of the simulation of the machine as controlled by the industrial controller system is analyzed. The motion of the simulation of the machine is then compared to the motion profile of the solid model to determine how closely the motion of the simulation of the machine as controlled by the industrial controller system matches the motion profile of the solid model.

Abstract: For machine learning data reduction and model optimization a method randomly assigns each data feature of a training data set to a plurality of solution groups. Each solution group has no more than a solution group number k of data features and each data feature is assigned to a plurality of solution groups. The method identifies each solution group as a high-quality solution group or a low-quality solution group. The method further calculates data feature scores for each data feature comprising a high bin number and a low bin number. The method determines level data for each data feature from the data feature scores using a fuzzy inference system. The method identifies an optimized data feature set based on the level data. The method further trains a production model using only the optimized data feature set. The method predicts a result using the production model.

Abstract: An industrial control system that includes a cloud platform facilitates secure execution of command data for an industrial device in communication with the cloud platform. The system includes an interface component, a validation component and an execution component. The interface component transmits industrial data associated with an industrial device to the cloud platform that analyzes the industrial data. The interface component also receives, from the cloud platform, command data for the industrial device that is generated based on the industrial data. The validation component validates the command data received from the cloud platform based on execution data indicative of a set of conditions for the command data. The validation component also establishes a secure communication link with the industrial device in response to a determination that the command data is approved for execution on the industrial device. The execution component initiates execution of the command data via the industrial device.

Abstract: Systems, methods, and software to facilitate simulating machines used in industrial automation are disclosed herein. In at least one implementation, an API is utilized to establish at least a communication link between a simulation model created in a simulation application and an industrial controller system outside of the simulation model, wherein the simulation model comprises definitions for a virtual representation of at least a portion of a machine used in an industrial automation environment. Data is then exchanged between the industrial controller system and the simulation model over the communication link.

Abstract: Methods and systems for creating and running and industrial control system simulation are described herein. The simulation may include animation of a complex machine linked with the industrial control device controlling the complex machine. The simulation may also include links to the physical I/O and other modules of the industrial controller to enhance the functionality of the simulation. This may increase the likelihood that the timing and functionality of the simulation may be more like real-time operation of the industrial control system. This may enhance the system design and save time of system design and start-up and troubleshooting of the operational industrial control system.

Abstract: Systems, methods, and software to facilitate cloud-based data processing and analysis in an industrial automation environment are disclosed herein. In at least one implementation, operational data generated by at least one industrial machine is stored in a local cache. The operational data is transferred for storage in an unstructured data storage system in a cloud-based architecture. In the cloud-based architecture, the operational data is processed to convert the operational data to a structured format and the operational data in the structured format is then stored in a structured data storage system. In response to receiving a request for analytics, at least a portion of the operational data is extracted from the structured data storage system and the analytics are executed on the at least the portion of the operational data.

Abstract: A cloud-based remote monitoring system and method monitor one or more industrial devices of an industrial facility, including a local data historian located to monitor one or more parameters from the industrial devices, and store parameters in a local storage associated with the data historian, as well as a cloud agent located at the industrial facility to collect data indicative of a past and/or a present state of the industrial devices from the data historian local storage according a manifest specific to the industrial facility. The cloud agent sends the collected data to a cloud platform according to the manifest, and dynamically reconfigures the cloud agent without interrupting the collecting and the sending.

Abstract: A boiler control system executing on a cloud platform facilitates remote control system analysis and generation of suitable set point parameters for a given boiler application. The system includes a system interface component, a modeling component and a correlation analytics component. The system interface component collects industrial data associated with a set of boilers and demand data associated with a set of loads. The system interface component also stores the industrial data and the demand data on a cloud platform. The modeling component generates a boiler behavioral model for storage on the cloud platform based on analysis of the industrial data and the demand data. The boiler behavioral model defines at least one correlation between efficiency and capacity associated with the set of boilers. The correlation analytics component determines at least one set point parameter for the set of boilers based on analysis of the boiler behavioral model.

Abstract: A cloud-based remote monitoring system and method monitor one or more industrial devices of an industrial facility, including a local data historian located to monitor one or more parameters from the industrial devices, and store parameters in a local storage associated with the data historian, as well as a cloud agent located at the industrial facility to collect data indicative of a past and/or a present state of the industrial devices from the data historian local storage according a manifest specific to the industrial facility. The cloud agent sends the collected data to a cloud platform according to the manifest, and dynamically reconfigures the cloud agent without interrupting the collecting and the sending.

Abstract: A cloud-based remote monitoring system and method monitor one or more industrial devices of an industrial facility, including a local data historian located to monitor one or more parameters from the industrial devices, and store parameters in a local storage associated with the data historian, as well as a cloud agent located at the industrial facility to collect data indicative of a past and/or a present state of the industrial devices from the data historian local storage according a manifest specific to the industrial facility. The cloud agent sends the collected data to a remote cloud platform according to the manifest, and dynamically reconfigures the cloud agent without interrupting the collecting and the sending.

Abstract: An industrial control system that includes a cloud platform facilitates secure execution of command data for an industrial device in communication with the cloud platform. The system includes an interface component, a validation component and an execution component. The interface component transmits industrial data associated with an industrial device to the cloud platform that analyzes the industrial data. The interface component also receives, from the cloud platform, command data for the industrial device that is generated based on the industrial data. The validation component validates the command data received from the cloud platform based on execution data indicative of a set of conditions for the command data. The validation component also establishes a secure communication link with the industrial device in response to a determination that the command data is approved for execution on the industrial device. The execution component initiates execution of the command data via the industrial device.

Abstract: Present embodiments include an automation control system including one or more processors and memories with an application stored on the one or more memories and implemented by the one or more processors. The application includes an interface configured to communicate with automation devices via a communication subsystem. Further, the application includes an operation environment, a programming environment, and an emulation environment. The programming environment is configured to generate device elements corresponding to the automation devices within the operation environment in which the device elements are configured to functionally interact with the automation devices. The emulation environment is configured to automatically host an emulation model of the automation devices based on the device elements generated within the operation environment.

Abstract: Authentication of cloud agents that collect and/or process industrial data facilitates secure communications with a cloud platform. An authentication component receives an authentication request from a cloud agent device residing at an industrial facility. The authentication component also authenticate the cloud agent device in response to the authentication request for a defined period of time based on an access key that uniquely identifies the cloud agent device residing at the industrial facility. A cloud data processing component receives, at a cloud platform, one or more data packets from the cloud agent device during the defined period of time and processes industrial data contained in the one or more data packets according processing instructions associated with the cloud platform.

Abstract: A cloud-based infrastructure facilitates gathering, transmitting, and remote storage of control and automation data using an agent-based communication channel. The infrastructure collects the industrial data from an industrial enterprise and intelligently sorts and organizes the acquired data based on selected criteria. Message queues can be configured on the cloud platform to segregate the industrial data according to priority, data type, or other criteria. Behavior assemblies stored in customer-specific manifests on the cloud platform define customer-specific preferences for processing data stored in the respective message queues. Web-based tools can deliver automated notifications to an end user device based on analysis of the stored data, and allow the user to remotely view the stored data.

Abstract: A cloud-based remote monitoring system and method monitor one or more industrial devices of an industrial facility, including a local data historian located to monitor one or more parameters from the industrial devices, and store parameters in a local storage associated with the data historian, as well as a cloud agent located at the industrial facility to collect data indicative of a past and/or a present state of the industrial devices from the data historian local storage according a manifest specific to the industrial facility. The cloud agent sends the collected data to a remote cloud platform according to the manifest, and dynamically reconfigures the cloud agent without interrupting the collecting and the sending.

Abstract: A cloud-based remote monitoring system and method monitor an industrial facility. The industrial facility includes one or more industrial devices. A cloud agent located at the industrial facility is configured to, by at least one processor, collect data indicative of a past and/or a present state of the industrial devices according a manifest specific to the industrial facility. The cloud agent is further configured to send the collected data to a remote cloud platform according to the manifest dynamically reconfigure the cloud agent without interrupting the collecting and the sending. The cloud platform processes the sent data to facilitate remote monitoring of the industrial devices.