Abstract: Various embodiments of the present technology provide an integrated platform that provides optimization tools that can be used across multiple lifecycle phases of an industrial automation system. In accordance with various embodiments, the integrated platform can take specified designs in a presale phase and easily open up that information in a corresponding tool to address needs in the post-sale phase to allow for all device commissioning, programming, and configuration without any importing, exporting, or recreation of presale files. Various embodiments use a common, cross-platform data file that links activity within each lifecycle phase. As such, any layouts or designs can be easily opened up within the platform by users with differing roles. Moreover, if in the post-sale phase, a control engineer decides that a specific aspect of control system was left out, the control engineer can summon the capability of the presale phase to expand the system.

Abstract: A cloud-based modeler component that generates interactive models of an industrial automation system(s) (IAS(s)) is presented. An interactive model facilitates remote viewing of, interaction with, troubleshooting of problems with, or optimization of industrial assets of an IAS. The modeler component polls industrial assets via cloud gateways to obtain information relating to the industrial assets to identify industrial assets of the IAS and relationships with other industrial assets or can receive information from a communication device that obtains information relating to legacy industrial assets to identify those legacy assets and their relationships. The modeler component generates an interactive model of the industrial assets of the IAS based on the information.

Abstract: A cloud-based analytics engine that analyzes data relating to an industrial automation system(s) to facilitate enhancing operation of the industrial automation system(s) is presented. The analytics engine can interface with the industrial automation system(s) via a cloud gateway(s) and can analyze industrial-related data obtained from the industrial automation system(s). The analytics engine can determine correlations between respective portions or aspects of the system(s), between a portion(s) or aspect(s) of the system(s) and extrinsic events or conditions, or between an employee(s) and the system(s). The analytics engine can determine and provide recommendations or instructions in connection with the industrial automation system(s) to enhance system performance based on the determined correlations.

Abstract: The technology disclosed herein enables automation of component sourcing when designing a process system in an industrial environment. In a particular embodiment, a method includes receiving design specifications of a process system in an industrial environment via a system design application. The method further includes determining components to implement the process system from the design specifications and identifying a first subset of the components for sourcing from an external provider. The method also includes populating the first subset into a component sourcing application.

Abstract: The technology disclosed herein enables automation of component sourcing when designing a process system in an industrial environment. In a particular embodiment, a method includes receiving design specifications of a process system in an industrial environment via a system design application. The method further includes determining components to implement the process system from the design specifications and identifying a first subset of the components for sourcing from an external provider. The method also includes populating the first subset into a component sourcing application.

Abstract: In one embodiment, a multi-purpose sensor may couple to a machine operating in an industrial environment and include numerous sensors disposed within the multi-purpose sensor to acquire sets of data associated with the machine or an environment surrounding the machine. A first portion of the sets of data may include historical sensor measurements over time for each of the sensors, and a second portion of the sets of data may include sensor measurements subsequent to when the first portion is acquired for each of the sensors. A processor of the multi-purpose sensor may determine a baseline collective signature based on the first portion, determine a subsequent collective signature based on the second portion, determine whether the collective signatures vary, and generate signals when a variance exists. The signals may cause a computing device, a cloud-based computing system, and/or a control/monitoring device to perform various actions.

Abstract: A cloud-based modeler component that generates interactive models of an industrial automation system(s) (IAS(s)) is presented. An interactive model facilitates remote viewing of, interaction with, troubleshooting of problems with, or optimization of industrial assets of an IAS. The modeler component polls industrial assets via cloud gateways to obtain information relating to the industrial assets to identify industrial assets of the IAS and relationships with other industrial assets or can receive information from a communication device that obtains information relating to legacy industrial assets to identify those legacy assets and their relationships. The modeler component generates an interactive model of the industrial assets of the IAS based on the information.

Abstract: Systems, methods, and software described herein manage tickets in an industrial automation environment. In one implementation, a ticket management service is configured to obtain a plurality of tickets from a plurality of data sources and extract attributes from each ticket in the plurality of tickets. Once extracted, the ticket management service may, for each ticket in the plurality of tickets, generate at least one ticket in a unified format and add the at least one ticket to a ticket database. The ticket management service may then use the ticket database to present ticket summaries to users associated with the industrial automation environment.

Abstract: A cloud-based industrial controller that controls devices, processes, and other assets of an industrial automation system via control algorithms that execute on a cloud platform is presented. A cloud-based collection component collects information from the industrial automation system via cloud gateways associated with the industrial automation system or extrinsic data sources. The cloud-based industrial controller can monitor and analyze the information, generate control instructions based on the analysis results, and communicate the control instructions to the devices, processes, and/or other assets of the industrial automation system to control operation of the industrial automation system.

Abstract: A cloud-based modeler component that generates interactive models of an industrial automation system(s) (IAS(s)) is presented. An interactive model facilitates remote viewing of, interaction with, troubleshooting of problems with, or optimization of industrial assets of an IAS. The modeler component polls industrial assets via cloud gateways to obtain information relating to the industrial assets to identify industrial assets of the IAS and relationships with other industrial assets or can receive information from a communication device that obtains information relating to legacy industrial assets to identify those legacy assets and their relationships. The modeler component generates an interactive model of the industrial assets of the IAS based on the information.

Abstract: The technology disclosed herein enables automation of component sourcing when designing a process system in an industrial environment. In a particular embodiment, a method includes receiving design specifications of a process system in an industrial environment via a system design application. The method further includes determining components to implement the process system from the design specifications and identifying a first subset of the components for sourcing from an external provider. The method also includes populating the first subset into a component sourcing application.

Abstract: Various embodiments of the present technology provide an integrated platform that provides personalized experiences for users of an integrated platform during various phases of an industrial automation project lifecycle. In accordance with various embodiments, the integrated platform can create personalized experiences for different users based on one or more assigned roles. The roles can include company roles, industry roles, job roles, location-based roles, personalized roles, and the like. For example, some company that only purchases certain brands of components for an industrial automation project can indicate that preference. During interactions with the integrated platform, any user associated with that company (e.g., via the company role) will only be presented with those preferences. As another example, generic requests received via one interaction channel can be analyzed and personalized, specific responses can be generated based on activity within other interaction channels.

Abstract: A cloud-based analytics engine that analyzes data relating to an industrial automation system(s) to facilitate enhancing operation of the industrial automation system(s) is presented. The analytics engine can interface with the industrial automation system(s) via a cloud gateway(s) and can analyze industrial-related data obtained from the industrial automation system(s). The analytics engine can determine correlations between respective portions or aspects of the system(s), between a portion(s) or aspect(s) of the system(s) and extrinsic events or conditions, or between an employee(s) and the system(s). The analytics engine can determine and provide recommendations or instructions in connection with the industrial automation system(s) to enhance system performance based on the determined correlations.

Abstract: The technology disclosed herein enables automation of component sourcing when designing a process system in an industrial environment. In a particular embodiment, a method includes receiving design specifications of a process system in an industrial environment via a system design application. The method further includes determining components to implement the process system from the design specifications and identifying a first subset of the components for sourcing from an external provider. The method also includes populating the first subset into a component sourcing application.

Abstract: Various embodiments of the present technology provide an integrated platform that provides optimization tools that can be used across multiple lifecycle phases of an industrial automation system. In accordance with various embodiments, the integrated platform can take specified designs in a presale phase and easily open up that information in a corresponding tool to address needs in the post-sale phase to allow for all device commissioning, programming, and configuration without any importing, exporting, or recreation of presale files. Various embodiments use a common, cross-platform data file that links activity within each lifecycle phase. As such, any layouts or designs can be easily opened up within the platform by users with differing roles. Moreover, if in the post-sale phase, a control engineer decides that a specific aspect of control system was left out, the control engineer can summon the capability of the presale phase to expand the system.

Abstract: A cloud-based analytics engine that analyzes data relating to an industrial automation system(s) to facilitate enhancing operation of the industrial automation system(s) is presented. The analytics engine can interface with the industrial automation system(s) via a cloud gateway(s) and can analyze industrial-related data obtained from the industrial automation system(s). The analytics engine can determine correlations between respective portions or aspects of the system(s), between a portion(s) or aspect(s) of the system(s) and extrinsic events or conditions, or between an employee(s) and the system(s). The analytics engine can determine and provide recommendations or instructions in connection with the industrial automation system(s) to enhance system performance based on the determined correlations.

Abstract: Various embodiments of the present technology provide an integrated platform that provides personalized experiences for users of an integrated platform during various phases of an industrial automation project lifecycle. In accordance with various embodiments, the integrated platform can create personalized experiences for different users based on one or more assigned roles. The roles can include company roles, industry roles, job roles, location-based roles, personalized roles, and the like. For example, some company that only purchases certain brands of components for an industrial automation project can indicate that preference. During interactions with the integrated platform, any user associated with that company (e.g., via the company role) will only be presented with those preferences. As another example, generic requests received via one interaction channel can be analyzed and personalized, specific responses can be generated based on activity within other interaction channels.

Abstract: Various embodiments of the present technology provide an integrated platform that provides optimization tools that can be used across multiple lifecycle phases of an industrial automation system. In accordance with various embodiments, the integrated platform can take specified designs in a presale phase and easily open up that information in a corresponding tool to address needs in the post-sale phase to allow for all device commissioning, programming, and configuration without any importing, exporting, or recreation of presale files. Various embodiments use a common, cross-platform data file that links activity within each lifecycle phase. As such, any layouts or designs can be easily opened up within the platform by users with differing roles. Moreover, if in the post-sale phase, a control engineer decides that a specific aspect of control system was left out, the control engineer can summon the capability of the presale phase to expand the system.

Abstract: Cloud-based backup provides a back up of an industrial plant comprising an industrial automation system(s) (IAS(s)). A cloud-based backup component comprising a modeler component can generate a model of industrial assets of the IAS(s) and relationships between industrial assets based on information obtained from the industrial assets via cloud gateways, a communication device associated with the IAS(s), or another source. The cloud-based backup component can store the model in a data store to be employed as a backup of the IAS(s) or to be used to configure a new IAS that is the same as or similar to the IAS(s). The model can be stored in the data store in a standardized or an agnostic format, wherein the backup component can translate the model to a format suitable to an IAS for which it is to be implemented based on characteristics associated with the IAS.

Abstract: In one embodiment, a multi-purpose sensor may couple to a machine operating in an industrial environment and include numerous sensors disposed within the multi-purpose sensor to acquire sets of data associated with the machine or an environment surrounding the machine. A first portion of the sets of data may include historical sensor measurements over time for each of the sensors, and a second portion of the sets of data may include sensor measurements subsequent to when the first portion is acquired for each of the sensors. A processor of the multi-purpose sensor may determine a baseline collective signature based on the first portion, determine a subsequent collective signature based on the second portion, determine whether the collective signatures vary, and generate signals when a variance exists. The signals may cause a computing device, a cloud-based computing system, and/or a control/monitoring device to perform various actions.