Abstract: A cloud-based performance enhancement service captures and collects data relating to interactions of users with industrial automation systems of multiple industrial customers for storage and analysis on a cloud platform.

Abstract: A cloud-based performance enhancement service captures and collects data relating to interactions of users with industrial automation systems of multiple industrial customers for storage and analysis on a cloud platform.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sharing learned information among robots. In some implementations, a robot obtains sensor data indicating characteristics of an object. The robot determines a classification for the object and generates an embedding for the object using a machine learning model stored by the robot. The robot stores the generated embedding and data indicating the classification for the object. The robot sends the generated embedding and the data indicating the classification to a server system. The robot receives, from the server system, an embedding generated by a second robot and a corresponding classification. The robot stores the received embedding and the corresponding classification in the local cache of the robot. The robot may then use the information in the cache to identify objects.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sharing learned information among robots. In some implementations, a robot obtains sensor data indicating characteristics of an object. The robot determines a classification for the object and generates an embedding for the object using a machine learning model stored by the robot. The robot stores the generated embedding and data indicating the classification for the object. The robot sends the generated embedding and the data indicating the classification to a server system. The robot receives, from the server system, an embedding generated by a second robot and a corresponding classification. The robot stores the received embedding and the corresponding classification in the local cache of the robot. The robot may then use the information in the cache to identify objects.

Abstract: A cloud gateway for coupling an industrial system to a cloud platform is provided. The cloud gateway collects data from one or more industrial controllers, meters, sensors, or other devices comprising an industrial automation system. The cloud gateway optionally performs additional transformations on the data to add context, summarize, filter, reformat, and/or encrypt the data. The cloud gateway then sends data to a cloud platform for use by one or more cloud-based applications or services. The cloud gateway can facilitate cloud-based data collection from both fixed-location and mobile industrial systems. The cloud gateway can also support store-and-forward logic, allowing industrial data to be temporarily stored in local storage in the event that communication between the cloud gateway and the cloud platform is disrupted.

Abstract: A method includes executing an initial instance of a change log process for a distributed system, each instance of the change log process configured to store a transaction history of transactions executed on the distributed system. The method also includes receiving transaction requests for executing corresponding transactions on the distributed system and determining a change log load based on the received transaction requests. The method includes executing at least one subsequent instance of the change log process when the change log load satisfied a threshold load. When multiple instances of the change log process are executing, the method includes ceasing execution of the at least one subsequent instance of the change log process and merging the transaction history of the initial instance of the change log process and the transaction history of the at least one subsequent instance of the change log process.

Abstract: A method includes executing an initial instance of a change log process for a distributed system, each instance of the change log process configured to store, on memory hardware in communication with the data processing hardware, a transaction history of transactions executed on the distributed system. The method also includes receiving transaction requests for executing corresponding transactions on the distributed system and determining a change log load based on the received transaction requests. The method includes executing at least one subsequent instance of the change log process when the change log load satisfied a threshold load. When multiple instances of the change log process are executing, the method includes ceasing execution of the at least one subsequent instance of the change log process and merging the transaction history of the initial instance of the change log process and the transaction history of the at least one subsequent instance of the change log process.

Abstract: A cloud-based simulation generation service collects industrial data from multiple industrial customers for storage and analysis on a cloud platform. The service employs a simulation generator component that analyzes data to facilitate generating a simulation model that simulates an industrial automation system, including simulating or emulating industrial devices, industrial processes, other industrial assets, or network-related assets or devices, and their respective interrelationships with each other. The simulation generator component also analyzes modification data to facilitate generating a modified simulation model that simulates the industrial automation system based on the modification.

Abstract: A cloud-based simulation generation service collects industrial data from multiple industrial customers for storage and analysis on a cloud platform. The service employs a simulation generator component that analyzes data to facilitate generating a simulation model that simulates an industrial automation system, including simulating or emulating industrial devices, industrial processes, other industrial assets, or network-related assets or devices, and their respective interrelationships with each other. The simulation generator component also analyzes modification data to facilitate generating a modified simulation model that simulates the industrial automation system based on the modification.

Abstract: A cloud-based performance enhancement service captures and collects data relating to interactions of users with industrial automation systems of multiple industrial customers for storage and analysis on a cloud platform.

Abstract: A cloud-based performance enhancement service captures and collects data relating to interactions of users with industrial automation systems of multiple industrial customers for storage and analysis on a cloud platform.

Abstract: A cloud-based operator interface system is provided that runs as a cloud service on a cloud platform. The cloud-based operator interface system collects industrial data from one or more industrial systems via respective cloud gateway devices. A set of predefined operator interface screens are stored on cloud storage associated with the operator interface system, and delivered to authorized Internet-capable client devices upon request. The industrial data received from the cloud gateways can be delivered to the client devices from the cloud platform via the operator interface screens. Additional cloud-side services can correlate and analyzes the industrial data on the cloud platform to facilitate additional reporting, alarming, and notification features.

Abstract: A cloud-based virtualization generation service collects industrial-automation-system-related data from multiple industrial automation systems of multiple industrial customers for storage and analysis on a cloud platform. A virtualization management component analyzes the data and generates a virtualized industrial automation system of the industrial automation system based on the analysis results. The virtualization management component updates the virtualized industrial automation system based on operation of, and user interactions with, the industrial automation system. The virtualization management component receives user interactions with the virtualized industrial automation system to facilitate remotely interacting with and/or controlling the industrial automation system and/or generates corresponding control signals that it sends to the industrial automation system to facilitate controlling operation of the industrial automation system.

Abstract: A cloud gateway for coupling an industrial system to a cloud platform is provided. The cloud gateway collects data from one or more industrial controllers, meters, sensors, or other devices comprising an industrial automation system. The cloud gateway optionally performs additional transformations on the data to add context, summarize, filter, reformat, and/or encrypt the data. The cloud gateway then sends data to a cloud platform for use by one or more cloud-based applications or services. The cloud gateway can facilitate cloud-based data collection from both fixed-location and mobile industrial systems. The cloud gateway can also support store-and-forward logic, allowing industrial data to be temporarily stored in local storage in the event that communication between the cloud gateway and the cloud platform is disrupted.

Abstract: A cloud gateway for coupling an industrial system to a cloud platform is provided. The cloud gateway collects data from one or more industrial controllers, meters, sensors, or other devices comprising an industrial automation system. The cloud gateway optionally performs additional transformations on the data to add context, summarize, filter, reformat, and/or encrypt the data. The cloud gateway then sends data to a cloud platform for use by one or more cloud-based applications or services. The cloud gateway can facilitate cloud-based data collection from both fixed-location and mobile industrial systems. The cloud gateway can also support store-and-forward logic, allowing industrial data to be temporarily stored in local storage in the event that communication between the cloud gateway and the cloud platform is disrupted.

Abstract: Cloud-aware industrial devices feed robust sets of data to a cloud-based data analyzer that executes as a service in a cloud platform. In addition to industrial data generated or collected by the industrial devices, the devices can provide device profile information to the cloud-based analyzer that identifies the device and relevant configuration information. The industrial devices can also provide customer data identifying an owner of the industrial devices, contact information for the owner, active service contracts, etc. The cloud-based data analyzer leverages this information to perform a variety of custom analytics on the data and generate reports or notifications catered to the particular industrial assets' optimal performance and business goals of the owner's industrial enterprise, as well as perform real-time decision making and control.

Abstract: A method includes executing an initial instance of a change log process for a distributed system, each instance of the change log process configured to store, on memory hardware in communication with the data processing hardware, a transaction history of transactions executed on the distributed system. The method also includes receiving transaction requests for executing corresponding transactions on the distributed system and determining a change log load based on the received transaction requests. The method includes executing at least one subsequent instance of the change log process when the change log load satisfied a threshold load. When multiple instances of the change log process are executing, the method includes ceasing execution of the at least one subsequent instance of the change log process and merging the transaction history of the initial instance of the change log process and the transaction history of the at least one subsequent instance of the change log process.

Abstract: A cloud-based virtualization generation service collects industrial data from multiple industrial automation systems of multiple industrial customers for storage and analysis on a cloud platform. A virtualization management component (VMC) generates a virtualized industrial automation system of an industrial automation system based on data analysis results. The VMC facilitates remotely controlling the industrial automation system based on user interactions with the virtualized industrial automation system, and updates the virtualized industrial automation system based on collected data relating to the industrial automation system. The VMC customizes a user's view of the virtualized industrial automation system based on a user's role, authorization, location, or preferences, wherein different views of the virtualized industrial automation system with different data overlays are presented on different communication devices of different users.

Abstract: A method includes executing an initial instance of a change log process for a distributed system, each instance of the change log process configured to store, on memory hardware in communication with the data processing hardware, a transaction history of transactions executed on the distributed system. The method also includes receiving transaction requests for executing corresponding transactions on the distributed system and determining a change log load based on the received transaction requests. The method includes executing at least one subsequent instance of the change log process when the change log load satisfied a threshold load. When multiple instances of the change log process are executing, the method includes ceasing execution of the at least one subsequent instance of the change log process and merging the transaction history of the initial instance of the change log process and the transaction history of the at least one subsequent instance of the change log process.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sharing learned information among robots. In some implementations, a robot obtains sensor data indicating characteristics of an object. The robot determines a classification for the object and generates an embedding for the object using a machine learning model stored by the robot. The robot stores the generated embedding and data indicating the classification for the object. The robot sends the generated embedding and the data indicating the classification to a server system. The robot receives, from the server system, an embedding generated by a second robot and a corresponding classification. The robot stores the received embedding and the corresponding classification in the local cache of the robot. The robot may then use the information in the cache to identify objects.