Rockwell Automation Technologies Incorporated

Juan Asenjo

Francisco Paul Maturana

Jessica Lin Korpela

Rainer Hessmer

Joseph A Harkulich

Edward Alan Hill

John Strohmenger

Bradford Henry Hegrat

John Dyck

Jenifer Rydberg Wright

Stephen Nawalaniec

Sal Conti

Salvatore T Conti

Neethu Philip

Shweta Chatrola

Salvator Conti

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. The simulation generator component performs operation simulations using the simulation model or modified simulation model to facilitate determining whether making the modification is appropriate, determining or predicting performance of a modified industrial automation system, determining compatibility of a modification with an industrial automation system, or determining or predicting performance of the industrial automation system when processing a work order.

Using cloud-based data for industrial simulation

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. The service employs a performance enhancement component that analyzes the data to facilitate determining correlations between certain user interactions and favorable performance of an industrial automation system, determining user interactions that are less favorable or unsafe, determining alternative actions that a user can take to achieve a same or similar preferred operational result, generating recommendations relating to the alternative actions, determining or designing components or techniques that can automate a preferred user action, determining improved user assignments in connection with the industrial automation system, and/or generating training modules or presentations based on preferred user actions that can be used to train users to more efficiently interact with an industrial automation system to achieve improved system performance.

Using cloud-based data for industrial automation system training

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. The virtualization management component also customizes a user's view of the virtualized industrial automation system based on user's role, authorization, or location.

Using cloud-based data for virtualization of an industrial machine environment

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.

Secure command execution from a cloud monitoring system to a remote cloud agent

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.

Industrial data analytics in a cloud platform

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. The VMC enables a user to filter data of a virtualized view and share information, including virtual notes, with other users.

Using cloud-based data for virtualization of an industrial automation environment with information overlays

A cloud-based global alarm annunciation broker assist in locating and contacting suitable technical support personnel in response to detected alarm events at a plant facility. The system comprises a cloud-based framework that dynamically matches on-site alarm events to domain experts capable of addressing the alarm events. The framework uses an agent-based architecture to gather industrial data from data sources within the industrial environment, including time-series alarm data. The data is received at a cloud platform, where broker services perform a global search for suitable technical support resources in response to alarm events identified by the collected data. The brokering system can automatically generate service tickets and send notifications to both end users and system managers/supervisors, and can notify application-level experts about events and anomalies that emerge from the on-premise processes being monitored by the cloud-based broker system.

Cloud-based global alarm annunciation system for industrial systems

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.

Industrial monitoring using cloud computing

A cloud agent facilitates collection of industrial data from one or more data sources on the plant floor and migration of the collected data to a cloud platform for storage and processing. Collection services associated with the cloud agent perform on-premise data collection of historical, live, and/or alarm data directly from industrial devices networked to the agent or from intermediate data concentrators that gather the data from the devices. Queue processing services executed by the cloud agent package the data into a data packet comprising header information that identifies a customer associated with the industrial enterprise, processing priority information, and other information that informs data processing services on the cloud platform how to process and/or direct the incoming data. The cloud agent then establishes a communication channel to the cloud platform and sends the data via the channel.

On-premise data collection and ingestion using industrial cloud agents

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.

Cloud based drive monitoring solution

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.

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Timberlake, OH, United States of America

Juan Asenjo