Apparatus and method for deploying analytics

A location where to deploy an analytic is determined. The location is at the cloud or at the site of the industrial machine. The analytic is configured to process data from an industrial machine. The analytic is obtained and is configured to communicate with one or more connectors. A first selected one of the one or more connectors is configured to communicate with a database. The analytic is configured so as to be interchangeable and operable at the cloud or at the site of an industrial machine. The analytic is then deployed at one or more of the cloud or the site of the industrial machine.

BACKGROUND OF THE INVENTION

Field of the Invention

The subject matter disclosed herein generally relates to analytics and, more specifically, to the ability to deploy analytics at various locations.

Brief Description of the Related Art

Some types of industrial machines are used to perform various manufacturing operations and tasks. For instance, some machines are used to create and finish parts associated with wind turbines. Other machines are used to create mechanical parts or components utilized by vehicles. Still other machines are used to produce electrical parts (e.g., resistors, capacitors, and inductors to mention a few examples). Other industrial machines (e.g., windmills or other generators) produce electrical power. Typically, industrial machines are controlled at least in part by computer code (or a computer program) that is executed by a processor that is located at the machine.

Industrial machines have sensors or other types of measurement devices that gather data, for example, concerning the operation of the machine. Analytics are computer programs that in some aspects analyze the data produced by the sensors at the industrial machines. In these regards, analytics can perform various types of analysis on the data, and can present the results of the analysis to a user or operator.

Analytics are deployed in various locations. At these locations, the analytics need to communicate with different databases and typically process different data in different formats. Often, analytics need to be custom-written to fit the nuances of a particular operating environment. Because of these factors, the cost of producing and utilizing analytics is typically high.

This has caused some dissatisfaction with current approaches.

BRIEF DESCRIPTION OF THE INVENTION

The present invention is directed to analytics that can be deployed interchangeably at a wide variety of locations. For example, the same version or copy of an analytic may be deployed at one or both of the cloud or a local site (e.g., a factory or plant). The analytics may be optionally implemented and executed using a computerized industrial Internet of Things analytics platform that may be deployed at the location of the manufacturing process, at the manufacturing facility premise, or in the cloud. Since a custom-written version of the analytic need not be made for each separate location, costs are reduced and the user experience is enhanced.

In many of these embodiments, a location where to deploy an analytic is determined. The location is at the cloud or at the site of the industrial machine. The analytic is configured to process data from an industrial machine.

The analytic is obtained and is configured to communicate with one or more connectors. A first selected one of the one or more connectors is configured to communicate with a database. The analytic is configured so as to be interchangeable and operable at the cloud, or at the site of an industrial machine, by using connectors that connected to a local industrial system or database or cloud equivalents. The analytic is then deployed at one or more of the cloud, or the site of the industrial machine.

In aspects, the data is time series data. In other examples, the database is a historian. In other examples, the analytic utilizes one or more of an asset model and a financial model obtained from the database.

In others of these embodiments, a second selected one of the one or more connectors is coupled to a sensor at the industrial machine. In still other examples, the analytic is packaged in a file.

In others of these embodiments, a system includes a first processor, a first analytic, a first cloud connector, and a second cloud connector. The first processor is deployed at the cloud. The first analytic is deployed at the first processor. The first cloud connector is coupled to a first database and the first analytic, and the second cloud connector is coupled to a second database and the first analytic. The first analytic is configured to communicate with the first cloud connector and the second cloud connector. The first analytic is configured so as to be interchangeable and operable at the cloud, or at the site of an industrial machine. The first analytic receives the first data stored in the first database, and second data via the second cloud connector.

In aspects, the first data comprises an asset model or a financial model. In other examples, the second data comprises time series data from an industrial machine. In yet other examples, the second data comprises a local result provided by a local analytic at the industrial machine.

In other examples, a second processor is deployed at the site of the industrial machine. A second analytic is deployed at the second processor. A first local connector is coupled to a second database and the second analytic, and a second local connector is coupled to a sensor at the industrial machine and the second analytic. The second analytic is configured to communicate with the first local connector and the second local connector. The second analytic is configured so as to be interchangeable and operable at the cloud, or at the site of the industrial machine. The second analytic receives third data stored in the second database via the first local connector, and fourth data from the sensor via the second local connector.

In aspects, the third data comprises an asset model or a financial model. In other examples, the fourth data is time series data. In yet other aspects, the first database or the second database are historians.

A system includes a processor, an analytic, a first local connector, and a second local connector. The processor is deployed at a site of an industrial machine. The analytic is deployed at the processor. The first local connector is coupled to a database and the analytic. The second local connector is coupled to a sensor at the industrial machine and the analytic. The analytic is configured to communicate with the first local connector and the second local connector. The analytic is configured so as to be interchangeable and operable at the cloud or at the site of the industrial machine. The second analytic receives first data stored in the database via the first local connector, and second data from the sensor via the second local connector.

In aspects, the first data comprises an asset model or a financial model. In other examples, the second data is time series data. In yet other aspects, the database is a historian.

Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity. It will further be appreciated that certain actions and/or steps may be described or depicted in a particular order of occurrence while those skilled in the art will understand that such specificity with respect to sequence is not actually required. It will also be understood that the terms and expressions used herein have the ordinary meaning as is accorded to such terms and expressions with respect to their corresponding respective areas of inquiry and study except where specific meanings have otherwise been set forth herein.

DETAILED DESCRIPTION OF THE INVENTION

The present approaches advantageously provide that the same version or copy of an analytic may be deployed at one or both of the cloud or a local site (e.g., a factory or plant). The analytics may be optionally implemented and executed using a computerized industrial Internet of Things (IOT) analytics platform that may be deployed at the location of the manufacturing process, at the manufacturing facility premise or in the cloud. Since a custom-written version of the analytic need not be made for each separate location, costs are reduced and the user experience is enhanced.

As used herein, “computer code” or “software” means the physical representation of a computer program on physical media (e.g., different bit patterns stored at a storage media such as a disc drive or electronic memory). The computer code may be (or represent) various computer instructions, data structures, variables, or any other software element used in any type of computer program.

As used herein, an “analytic” is computer code or software that analyzes data or information supplied by a machine (or machines). Various types of analysis may be performed. For example, the data can be analyzed to determine whether the machine is operating properly. In another example, the data can be analyzed to predict future performance of the machine. The output of the analytic may be a control signal (or some other control mechanism) that causes various actions to occur. For example, the control signal (or other control mechanism) may cause an alert message to be formed and sent to a human operator or a central control office. In another example, the output of the analytic may control the operation of the machine. Other examples are possible. As described below, the analytic may be executed or implemented at an analytic platform either at the cloud or a local site.

Referring now toFIG. 1, one example of a system100that includes universally deployable analytics is described. The cloud102includes an analytic104, a first cloud connector106, a second cloud connector108, and a database110.

The cloud102is a computer network and may include routers, gateways, processors, and other devices. The analytic104is computer software or a computer program that receives information from industrial machines and analyzes this information. The analytic104may present the results of the analysis to analysis to a human operator, or may perform other actions. The database110is any type of memory storage device that stores information.

The first cloud connector106and the second cloud connector108are implemented as any combination of hardware or software. The first cloud connector106and the second cloud connector108are physically and logically separate from analytics, but connect to analytics. The first cloud connector106and the second cloud connector108perform translation functions. For example, the first cloud connector106translates information between a format used by the machines and a format used by the analytic. The second cloud connector108translates information between a format used by the database110and a format used by the analytic104.

A first location120(e.g., factory or grouping of machines such as a wind farm) includes a processor or control circuit122that operates an analytic124, a first local connector126, a second local connector128, a third local connector129, a first historian130, other databases132, a grouping134of machines136and138.

A second location140(e.g., factory or grouping of machines such as a wind farm) includes a processor or control circuit142that operates an analytic144, a first local connector146, a second local connector148, a third local connector149, a first historian150, other databases152, a grouping154of machines156and158.

The first location120and the second location140may be, for example, within the vicinity of a factory or grouping of machines such as a wind farm. Other examples are possible. The processor or control circuits122and142may be implemented as any hardware or software.

The analytics124and analytic144are computer software or computer programs that receive information from industrial machines and analyze this information. The analytics124and144may present the results of the analysis to analysis to a human operator, or may perform other actions. In some examples, the analytics104,124, and144are exactly the same analytic both physically and logically. In other examples, the analytics104,124, and144are physically and logically different. In yet other examples, two of the analytics104,124, and144are physically and logically the same, while one is different.

In other examples, the analytics104,124, and144are packaged as files. For example, they are packaged as zip files. Other examples are possible.

The first local connector126, second local connector128, third local connector129, fourth local connector146, fifth local connector148, and the sixth local connector149perform translation functions. For example, the first local connector126and the fourth local connector146translate information between formats used by the other local data stores132,152and formats of the analytics124,144. The second local connector128and the fifth local connector148translate information between formats used by the machines138,158and formats used by the analytics124,144. The third local connector129and the sixth local connector149translate information between formats used by the historians129,149and formats used by the analytics124,144.

The first historian130and the second historian150are any type of memory devices that store historical information (e.g., archived time series data). The other databases132and152are any type of memory storage device (or devices) that store non-historical information such as information concerning the characteristics of assets. For example, the number of assets, their connectivity, and their operating characteristics (e.g., speed, voltages, and power) may be stored (e.g., as a model). In one example, the asset information (or model) indicates that the machine is a windmill, there are 10 windmills in a windfarm, and each windmill has 10 blades that are 30 feet long.

Financial information (which may be structured as a model) may also be stored in the other database152. The financial information may include costs associated with the machine, the market for products or services created by the machine, and the values of products or services produced by the machine. In one example, the financial information (or model) indicates the value of the power output of a windmill for a day is stored.

The groupings134and154may be a factory, business, building, wind farm, or area within these areas. Other types of groupings are possible. The machines136,138,156, and158are any type of industrial machine such as grinders, cappers, milling machines, or windmills. Other examples are possible.

In one example of the operation of the system ofFIG. 1, the analytics124and144calculate a result related to their assigned machines (machines136and138for analytic124, and machines156and158for analytic144). In one example, the results are efficiencies. The results are then sent to analytic104, which calculates or determines an overall result. In one example, an overall efficiency for all machines is calculated. For instance, if the machines136,138,156, and158were windmills and groupings134and154were windfarms, analytic124may calculate an efficiency for windfarm134, analytic144may calculate an efficiency for windfarm154, and analytic104may calculate an efficiency for all windfarms. It will be appreciated that in this example no time series data is uploaded to the cloud102.

Users may copy or download the analytic104from the cloud to the processors122and142. In some aspects, analytics104,124and144are identical. In other words, the analytic104is universal and is capable of being run anywhere without having to be internally modified. In aspects, the analytics104,124and144are written in a common computer language, and depend upon the same asset and financial models.

Referring now toFIG. 2, another example of a system200with universally deployable analytics is described. The cloud202includes an analytic203(at or being executed by a processor214), a first cloud connector204, a second cloud connector206, a third cloud connector208, a historian210, and other cloud data stores212.

The cloud202is a computer network and may include routers, gateways, processors, and other devices. The analytic203is computer software or a computer program that receives information from industrial machines and analyzes this information. The analytic203may present the results of the analysis to analysis to a human operator, or may perform other actions.

The historian210is any type of memory storage device that stores historical information. The other cloud data stores212are any type of memory storage device that store non-historical data such as asset and financial data as has been described above.

The first cloud connector204, second cloud connector206, and third cloud connector208are implemented as any combination of hardware or software. The first cloud connector204, second cloud connector206, and third cloud connector208are physically and logically separate from analytics, but connect to analytics. The first cloud connector204, second cloud connector206, and third cloud connector208perform translation functions. For example, the first cloud connector204translates information between a format used by the historian210and a format used by the analytic203. The second cloud connector206translates information (e.g., time series data) between a format used by the machines224,226,234,236,244,246, and a format used by the analytic203. The third cloud connector208translates information between formats used by the other cloud data stores212(e.g., asset information or financial information) and a format used by the analytic203.

A first location242, second location252, and third location262may be any type of location such as a factory, a plant, an office, a building, or wind farm. Other examples are possible.

The first machine244, second machine246, third machine254, the fourth machine256, the fifth machine264, and the sixth machine266are any type of industrial machine such as grinders, cappers, milling machines, or windmills. Other examples are possible.

Time series data262,264, and266is produced at the machines244,246, and248. The data is produced by sensors or other types of measurement devices at these machines. For example, the time series data may be pressure, temperature, or speed data. Other examples are possible.

The analytic204is configured with an interface that communicates the local connectors204,206, and208. The analytic is configured so as to be interchangeable and operable at the cloud or at the site of an industrial machine. The analytic receives data stored in the database and received via the local connector via the interface.

In one example of the operation of the system ofFIG. 2, machines224,226,234,236,244,246produce time series data262,264,266, which is sent to the analytic203via the cloud connector206. The connection between the machines and the clouds may be wired, wireless, and through any combination of networks and network elements. Time series data262,264,266, information from the historian210, and/or information from the other cloud data stores212may be utilized by the analytic203to calculate a result. The results may be specific to machines in the groupings222,232,242, relate to all machines in a grouping222,232,242, or be an overall result for all machines in all groupings222,232,242. For instance, if the machines224,226,234,236,242,246were windmills, and groupings222,232,242were windfarms, analytic203may calculate an efficiency for individual machines, individual windfarms, or all windfarms. In aspects, the analytic203is capable of being operated at any of the machines224,226,234,236,244, and246, without modifying the analytic.

Referring now toFIG. 3, yet another example of a system300that includes universally deployable analytics is described. Analytics may be developed at the cloud302. The analytics may be downloaded from the cloud302to local sites. For example, an analytic304may be developed at the cloud and downloaded.

The cloud302is a computer network and may include routers, gateways, processors, and other devices. The analytic304is computer software or a computer program that receives information from industrial machines and analyzes this information. The analytic304may present the results of the analysis to analysis to a human operator, or may perform other actions.

A first location320(e.g., factory or grouping of machines such as a wind farm) includes a processor or control circuit322that operates an analytic324, a first local connector326, a second local connector328, a third local connector329, a first historian330, other databases332, and a grouping334of machines336and338.

A second location340(e.g., factory or grouping of machines such as a wind farm) includes a processor or control circuit342that operates an analytic344, a first local connector346, a second local connector348, a third local connector349, a first historian350, other databases352, and a grouping354of machines356and358.

The first location320and the second location340may be, for example, within the vicinity of a factory or grouping of machines such as a wind farm. Other examples are possible. The processor or control circuits322and342may be implemented as any hardware or software.

The analytics324and analytic344are computer software or computer programs that receive information from industrial machines and analyze this information. The analytics324and344may present the results of the analysis to analysis to a human operator, or may perform other actions. In some examples, the analytics304,324, and344are exactly the same analytic both physically and logically. In other examples, the analytics304,324, and344are physically and logically different. In yet other examples, two of the analytics304,324, and344are physically and logically the same, while one is different. In aspects, the analytics304,324, and344are exactly the same, are interchangeable, and can be executed at any location.

In other examples, the analytics304,324, and344are packaged as files. For example, they are packaged as zip files. Other examples are possible.

The first local connector326, second local connector328, third local connector329, fourth local connector346, fifth local connector348, and the sixth local connector349perform translation functions. For example, the first local connector326and the fourth local connector346translate information between formats used by the other local data stores332,352and formats used by the analytics324,344. The second local connector328and the fifth local connector348translate information between formats used by the machines338,358and formats used by the analytics324,344. The third local connector329and the sixth local connector349translate information between formats used by the historians329,349and formats used by the analytics324,344.

The first historian330and the second historian350are any type of memory devices that store historical information (e.g., archived time series data). The other databases332and352are any type of memory storage device (or devices) that store non-historical information such as information concerning the characteristics of assets. For example, the number of assets, their connectivity, and their operating characteristics (e.g., speed, voltages, and power) may be stored, for example in an asset model. In one example, the asset information indicates that the machine is a windmill, that there are 10 windmills in a windfarm, and that each windmill has 10 blades that are 30 feet long.

Financial information may also be stored in the other database352, for example, as a financial model. The financial information may include costs associated with the machine, the market for products or services created by the machine, and the values of products or services produced by the machine. In one example, the value of the power output of a windmill for a day is stored.

The groupings334and354may be a factory, business, building, wind farm, or area within these areas. Other types of groupings are possible. The machines336,338,356, and358are any type of industrial machine such as grinders, cappers, milling machines, or windmills. Other examples are possible.

In one example of the operation of the system ofFIG. 3, the analytics324and344calculate a result related to data received from their assigned machines (machines336and338for analytic324, and machines356and358for analytic344). In one example, the results are efficiencies. For instance, if the machines336,338,356, and358were windmills, and groupings334and354were windfarms, analytic324may calculate an efficiency for windfarm334, analytic344may calculate an efficiency for windfarm354. It will be appreciated that in this example no time series data is uploaded to the cloud302. Users may copy or download the analytic304from the cloud to processors322and342. In some aspects, analytics304,324and344are identical.

Referring now toFIG. 4, an example of an approach that deploys analytics at different locations is described. At step402, an analytic that processes information from an industrial machine is created. The analytic is configured with an interface that communicates with one or more connectors. The analytic is configured so as to be interchangeable and operable at the cloud, or at the site of an industrial machine.

At step404, a location to deploy the analytic is determined. The location may be at the cloud, at another network, at a central location, home office, or a remote location such as a factory, plant, office, building, or wind farm. Other examples are possible.

At step406, the analytic is deployed at one or more of the cloud, or the site of the industrial machine. The analytic may be downloaded and sent in any file structure (e.g., as a zip file).

Referring now toFIG. 5, one example of an analytic500is described. The analytic has an executable portion502, and connectors504,506, and508. The connectors504,506, and508couple to external connectors524,526, and528. The external connectors524,526, and528themselves couple to external data sources534,536, and538. The analytic500is disposed physically in a package550.

The executable portion502is executable computer code or computer instructions that performs a function (or functions). The code or instructions may be in any computer language. The executable portion502may be based upon or rely upon various asset and financial models. An asset model may specify asset information such as the number of assets, their connectivity, and their operating characteristics (e.g., speed, voltages, and power). In one example, the asset model may specify that the machine is a windmill, that there are 10 windmills in a windfarm, and that each windmill has 10 blades that are 30 feet long.

A financial model may specify financial information such as the costs associated with the machine, the market for products or services created by the machine, and the values of products or services produced by the machine. In one example, the value of the power output of a windmill for a day is stored in the financial model. The asset and financial models are stored in a database.

The connectors504,506, and508may be software elements that connect the executable portion to the local connectors524,526, and528. Local connectors524,526, and528may be software elements that connect the connectors504,506, and508to the external data sources534,536, and538. The connectors and local connectors may perform various translation functions between sources and destinations.

The external data sources534,536, and538may be data stores (e.g., data historians), or machines (e.g., that transmit time series data), or other source that transmits information.

The package550is a data structure that houses the executable portion502, and the connectors. The analytic500(as encapsulated by the package550) can be executed at the cloud, or at a processor located at a remote location, or at a processor located at the machine itself. Copies of analytics can be downloaded from the cloud. The downloading can be based on a pay-as-you-go basis, or users at remote locations can subscribe to certain analytics.

As mentioned, the analytics described herein may optionally be implemented using a computerized industrial internet of things analytics platform that may be deployed at the location of the manufacturing process, at the manufacturing facility premise, or in the cloud.

While progress with industrial equipment automation has been made over the last several decades, and assets have become “smarter,” the intelligence of any individual asset pales in comparison to intelligence that can be gained when multiple smart devices are connected together. Aggregating data collected from or about multiple assets can enable users to improve business processes, for example by improving effectiveness of asset maintenance or improving operational performance if appropriate industrial-specific data collection and modeling technology is developed and applied.

In an example, an industrial asset can be outfitted with one or more sensors configured to monitor respective ones of an asset's operations or conditions. Data from the one or more sensors can be recorded or transmitted to a cloud-based or other remote computing environment. By bringing such data into a cloud-based computing environment, new software applications informed by industrial process, tools and know-how can be constructed, and new physics-based analytics specific to an industrial environment can be created. Insights gained through analysis of such data can lead to enhanced asset designs, or to enhanced software algorithms for operating the same or similar asset at its edge, that is, at the extremes of its expected or available operating conditions.

The systems and methods for managing industrial machines (also referred to assets herein) can include or can be a portion of an Industrial Internet of Things (IIoT). In an example, an IIoT connects industrial assets, such as turbines, jet engines, and locomotives, to the Internet or cloud, or to each other in some meaningful way. The systems and methods described herein can include using a “cloud” or remote or distributed computing resource or service. The cloud can be used to receive, relay, transmit, store, analyze, or otherwise process information for or about one or more industrial assets. In an example, a cloud computing system includes at least one processor circuit, at least one database, and a plurality of users or assets that are in data communication with the cloud computing system. The cloud computing system can further include or can be coupled with one or more other processor circuits or modules configured to perform a specific task, such as to perform tasks related to asset maintenance, analytics, data storage, security, or some other function.

However, the integration of industrial assets with the remote computing resources to enable the IIoT often presents technical challenges separate and distinct from the specific industry and from computer networks, generally. A given industrial asset may need to be configured with novel interfaces and communication protocols to send and receive data to and from distributed computing resources. Given industrial assets may have strict requirements for cost, weight, security, performance, signal interference, and the like such that enabling such an interface is rarely as simple as combining the industrial asset with a general purpose computing device.

To address these problems and other problems resulting from the intersection of certain industrial fields and the IIoT, embodiments may enable improved interfaces, techniques, protocols, and algorithms for facilitating communication with and configuration of industrial assets via remote computing platforms and frameworks. Improvements in this regard may relate to both improvements that address particular challenges related to particular industrial assets (e.g., improved aircraft engines, wind turbines, locomotives, medical imaging equipment) that address particular problems related to use of these industrial assets with these remote computing platforms and frameworks, and also improvements that address challenges related to operation of the platform itself to provide improved mechanisms for configuration, analytics, and remote management of industrial assets.

The Predix™ platform available from GE is a novel embodiment of such Asset Management Platform (AMP) technology enabled by state of the art cutting edge tools and cloud computing techniques that enable incorporation of a manufacturer's asset knowledge with a set of development tools and best practices that enables asset users to bridge gaps between software and operations to enhance capabilities, foster innovation, and ultimately provide economic value. Through the use of such a system, a manufacturer of industrial assets can be uniquely situated to leverage its understanding of industrial assets themselves, models of such assets, and industrial operations or applications of such assets, to create new value for industrial customers through asset insights.

FIG. 6illustrates generally an example of portions of a first AMP800. As further described herein, one or more portions of an AMP can reside in an asset cloud computing system820, in a local or sandboxed environment, or can be distributed across multiple locations or devices. An AMP can be configured to perform any one or more of data acquisition, data analysis, or data exchange with local or remote assets, or with other task-specific processing devices.

The first AMP600includes a first asset community602that is communicatively coupled with the asset cloud computing system620. In an example, a machine module610receives information from, or senses information about, at least one asset member of the first asset community602, and configures the received information for exchange with the asset cloud computing system620. In an example, the machine module610is coupled to the asset cloud computing system620or to an enterprise computing system630via a communication gateway605.

In an example, the communication gateway605includes or uses a wired or wireless communication channel that extends at least from the machine module610to the asset cloud computing system620. The asset cloud computing system620includes several layers. In an example, the asset cloud computing system620includes at least a data infrastructure layer, a cloud foundry layer, and modules for providing various functions. In the example ofFIG. 6, the asset cloud computing system620includes an asset module621, an analytics module622, a data acquisition module623, a data security module624, and an operations module625. Each of the modules621-625includes or uses a dedicated circuit, or instructions for operating a general purpose processor circuit, to perform the respective functions. In an example, the modules621-625are communicatively coupled in the asset cloud computing system620such that information from one module can be shared with another. In an example, the modules621-625are co-located at a designated datacenter or other facility, or the modules621-625can be distributed across multiple different locations.

An interface device640can be configured for data communication with one or more of the machine module610, the gateway605, or the asset cloud computing system620. The interface device640can be used to monitor or control one or more assets. In an example, information about the first asset community602is presented to an operator at the interface device640. The information about the first asset community602can include information from the machine module610, or the information can include information from the asset cloud computing system620. In an example, the information from the asset cloud computing system620includes information about the first asset community602in the context of multiple other similar or dissimilar assets, and the interface device640can include options for optimizing one or more members of the first asset community602based on analytics performed at the asset cloud computing system620.

In an example, an operator selects a parameter update for the first wind turbine601using the interface device640, and the parameter update is pushed to the first wind turbine via one or more of the asset cloud computing system620, the gateway605, and the machine module610. In an example, the interface device640is in data communication with the enterprise computing system630and the interface device640provides an operation with enterprise-wide data about the first asset community602in the context of other business or process data. For example, choices with respect to asset optimization can be presented to an operator in the context of available or forecasted raw material supplies or fuel costs. In an example, choices with respect to asset optimization can be presented to an operator in the context of a process flow to identify how efficiency gains or losses at one asset can impact other assets. In an example, one or more choices described herein as being presented to a user or operator can alternatively be made automatically by a processor circuit according to earlier-specified or programmed operational parameters. In an example, the processor circuit can be located at one or more of the interface device640, the asset cloud computing system620, the enterprise computing system630, or elsewhere.

Returning again to the example ofFIG. 6some capabilities of the first AMP600are illustrated. The example ofFIG. 6includes the first asset community602with multiple wind turbine assets, including the first wind turbine601. Wind turbines are used in some examples herein as non-limiting examples of a type of industrial asset that can be a part of, or in data communication with, the first AMP600.

In an example, the multiple turbine members of the asset community602include assets from different manufacturers or vintages. The multiple turbine members of the asset community602can belong to one or more different asset communities, and the asset communities can be located locally or remotely from one another. For example, the members of the asset community602can be co-located on a single wind farm, or the members can be geographically distributed across multiple different farms. In an example, the multiple turbine members of the asset community602can be in use (or non-use) under similar or dissimilar environmental conditions, or can have one or more other common or distinguishing characteristics.

FIG. 6further includes the device gateway605configured to couple the first asset community602to the asset cloud computing system620. The device gateway605can further couple the asset cloud computing system620to one or more other assets or asset communities, to the enterprise computing system630, or to one or more other devices. The first AMP600thus represents a scalable industrial solution that extends from a physical or virtual asset (e.g., the first wind turbine601) to a remote asset cloud computing system620. The asset cloud computing system620optionally includes a local system, enterprise, or global computing infrastructure that can be optimized for industrial data workloads, secure data communication, and compliance with regulatory requirements.

In an example, information from an asset, about the asset, or sensed by an asset itself is communicated from the asset to the data acquisition module624in the asset cloud computing system620. In an example, an external sensor can be used to sense information about a function of an asset, or to sense information about an environment condition at or near an asset. The external sensor can be configured for data communication with the device gateway605and the data acquisition module624, and the asset cloud computing system620can be configured to use the sensor information in its analysis of one or more assets, such as using the analytics module622.

In an example, the first AMP600can use the asset cloud computing system620to retrieve an operational model for the first wind turbine601, such as using the asset module621. The model can be stored locally in the asset cloud computing system620, or the model can be stored at the enterprise computing system630, or the model can be stored elsewhere. The asset cloud computing system620can use the analytics module622to apply information received about the first wind turbine601or its operating conditions (e.g., received via the device gateway605) to or with the retrieved operational model. Using a result from the analytics module622, the operational model can optionally be updated, such as for subsequent use in optimizing the first wind turbine601or one or more other assets, such as one or more assets in the same or different asset community. For example, information about the first wind turbine601can be analyzed at the asset cloud computing system620to inform selection of an operating parameter for a remotely located second wind turbine that belongs to a different second asset community.

The first AMP600includes a machine module610. The machine module610includes a software layer configured for communication with one or more industrial assets and the asset cloud computing system620. In an example, the machine module610can be configured to run an application locally at an asset, such as at the first wind turbine601. The machine module610can be configured for use with or installed on gateways, industrial controllers, sensors, and other components. In an example, the machine module610includes a hardware circuit with a processor that is configured to execute software instructions to receive information about an asset, optionally process or apply the received information, and then selectively transmit the same or different information to the asset cloud computing system620.

In an example, the asset cloud computing system620can include the operations module625. The operations module625can include services that developers can use to build or test Industrial Internet applications, or the operations module625can include services to implement Industrial Internet applications, such as in coordination with one or more other AMP modules. In an example, the operations module625includes a microservices marketplace where developers can publish their services and/or retrieve services from third parties. The operations module625can include a development framework for communicating with various available services or modules. The development framework can offer developers a consistent look and feel and a contextual user experience in web or mobile applications.

In an example, an AMP can further include a connectivity module. The connectivity module can optionally be used where a direct connection to the cloud is unavailable. For example, a connectivity module can be used to enable data communication between one or more assets and the cloud using a virtual network of wired (e.g., fixed-line electrical, optical, or other) or wireless (e.g., cellular, satellite, or other) communication channels. In an example, a connectivity module forms at least a portion of the gateway605between the machine module610and the asset cloud computing system620.

In an example, an AMP can be configured to aid in optimizing operations or preparing or executing predictive maintenance for industrial assets. An AMP can leverage multiple platform components to predict problem conditions and conduct preventative maintenance, thereby reducing unplanned downtimes. In an example, the machine module610is configured to receive or monitor data collected from one or more asset sensors and, using physics-based analytics (e.g., finite element analysis or some other technique selected in accordance with the asset being analyzed), detect error conditions based on a model of the corresponding asset. In an example, a processor circuit applies analytics or algorithms at the machine module610or at the asset cloud computing system620.

In response to the detected error conditions, the AMP can issue various mitigating commands to the asset, such as via the machine module610, for manual or automatic implementation at the asset. In an example, the AMP can provide a shut-down command to the asset in response to a detected error condition. Shutting down an asset before an error condition becomes fatal can help to mitigate potential losses or to reduce damage to the asset or its surroundings. In addition to such an edge-level application, the machine module610can communicate asset information to the asset cloud computing system620.

In an example, the asset cloud computing system620can store or retrieve operational data for multiple similar assets. Over time, data scientists or machine learning can identify patterns and, based on the patterns, can create improved physics-based analytical models for identifying or mitigating issues at a particular asset or asset type. The improved analytics can be pushed back to all or a subset of the assets, such as via multiple respective machine modules610, to effectively and efficiently improve performance of designated (e.g., similarly-situated) assets.

In an example, the asset cloud computing system620includes a Software-Defined Infrastructure (SDI) that serves as an abstraction layer above any specified hardware, such as to enable a data center to evolve over time with minimal disruption to overlying applications. The SDI enables a shared infrastructure with policy-based provisioning to facilitate dynamic automation, and enables SLA mappings to underlying infrastructure. This configuration can be useful when an application requires an underlying hardware configuration. The provisioning management and pooling of resources can be done at a granular level, thus allowing optimal resource allocation.

In a further example, the asset cloud computing system620is based on Cloud Foundry (CF), an open source PaaS that supports multiple developer frameworks and an ecosystem of application services. Cloud Foundry can make it faster and easier for application developers to build, test, deploy, and scale applications. Developers thus gain access to the vibrant CF ecosystem and an ever-growing library of CF services. Additionally, because it is open source, CF can be customized for IIoT workloads.

The asset cloud computing system620can include a data services module that can facilitate application development. For example, the data services module can enable developers to bring data into the asset cloud computing system620and to make such data available for various applications, such as applications that execute at the cloud, at a machine module, or at an asset or other location. In an example, the data services module can be configured to cleanse, merge, or map data before ultimately storing it in an appropriate data store, for example, at the asset cloud computing system620. A special emphasis has been placed on time series data, as it is the data format that most sensors use.

Security can be a concern for data services that deal in data exchange between the asset cloud computing system620and one or more assets or other components. Some options for securing data transmissions include using Virtual Private Networks (VPN) or an SSL/TLS model. In an example, the first AMP600can support two-way TLS, such as between a machine module and the security module624. In an example, two-way TLS may not be supported, and the security module624can treat client devices as OAuth users. For example, the security module624can allow enrollment of an asset (or other device) as an OAuth client and transparently use OAuth access tokens to send data to protected endpoints.

In the example ofFIG. 6, it will be understood that the approaches described herein with respect toFIGS. 1-5may be implemented using the AMP600, which may be deployed at the first asset community602, at the wind turbine601, or in the cloud620. In aspects, the analytics described herein may be deployed at any of these locations using the structures ofFIG. 6.

It will be appreciated by those skilled in the art that modifications to the foregoing embodiments may be made in various aspects. Other variations clearly would also work, and are within the scope and spirit of the invention. It is deemed that the spirit and scope of that invention encompasses such modifications and alterations to the embodiments herein as would be apparent to one of ordinary skill in the art and familiar with the teachings of the present application.