Abstract: A system has a processor that may receive an order of an industrial device assembly having multiple industrial automation devices. The processor may also generate a first data file based on the order of the industrial device assembly. The first data file may have specification data associated with each industrial automation device and hierarchical information associated with each industrial automation device. Additionally, the processor may generate a second data file based on the first data file. The second data file has one or more control blocks of code associated with each industrial automation device. Further, the processor may transmit the second data file to a control system associated with the industrial device assembly. The control system is automatically programmed based on the second data file to control one or more operations of each industrial automation device in the industrial device assembly.

Abstract: A compressor controller for operating a compressor within an industrial automation environment is provided. The compressor controller includes a control module, configured to control the compressor via control settings, and a machine learning module, coupled with the control module. The machine learning module is configured to receive a set of supervised data related to the compressor, and to train with the supervised data to produce a Newtonian physics model representing the inputs and outputs of the compressor within the industrial automation environment. The machine learning module is also configured to receive performance data related to the compressor, receive environment data related to the compressor, and to process the performance data and environment data to produce predicted future performance data for the compressor, and to produce control settings for the compressor.

Abstract: Techniques to facilitate automatic discovery of industrial assets and associated data in an industrial automation environment are disclosed herein. In at least one implementation, a computing system iteratively scans a communication network to automatically detect at least one industrial device on the communication network. The computing system determines a unique identifier of a device type associated with the at least one industrial device detected on the communication network. The computing system determines an identity object for the industrial device and maps the identity object to the unique identifier of the device type associated with the industrial device. Based on the identity object for the industrial device, the computing system establishes communication with the industrial device on the communication network and receives operational data from the industrial device.

Abstract: A system has a processor that may receive an order of an industrial device assembly having multiple industrial automation devices. The processor may also generate a first data file based on the order of the industrial device assembly. The first data file may have specification data associated with each industrial automation device and hierarchical information associated with each industrial automation device. Additionally, the processor may generate a second data file based on the first data file. The second data file has one or more control blocks of code associated with each industrial automation device. Further, the processor may transmit the second data file to a control system associated with the industrial device assembly. The control system is automatically programmed based on the second data file to control one or more operations of each industrial automation device in the industrial device assembly.

Abstract: A compressor controller for operating a compressor within an industrial automation environment is provided. The compressor controller includes a control module, configured to control the compressor via control settings, and a machine learning module, coupled with the control module. The machine learning module is configured to receive a set of supervised data related to the compressor, and to train with the supervised data to produce a Newtonian physics model representing the inputs and outputs of the compressor within the industrial automation environment. The machine learning module is also configured to receive performance data related to the compressor, receive environment data related to the compressor, and to process the performance data and environment data to produce predicted future performance data for the compressor, and to produce control settings for the compressor.

Abstract: Techniques to facilitate automatic discovery of industrial assets and associated data in an industrial automation environment are disclosed herein. In at least one implementation, a computing system iteratively scans a communication network to automatically detect at least one industrial device on the communication network. The computing system determines a unique identifier of a device type associated with the at least one industrial device detected on the communication network. The computing system determines an identity object for the industrial device and maps the identity object to the unique identifier of the device type associated with the industrial device. Based on the identity object for the industrial device, the computing system establishes communication with the industrial device on the communication network and receives operational data from the industrial device.