Patent Publication Number: US-10317887-B2

Title: Apparatus and method for maintaining a machine

Description:
BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The subject matter disclosed herein generally relates to industrial machines and, more specifically, to maintaining these machines. 
     Brief Description of the Related Art 
     Various types of industrial and other machines are used to perform various manufacturing and industrial 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). In industrial settings, industrial machines may be used for power generation, water treatment, fossil fuel extraction and other endeavors requiring the interoperation of multiple machines in a coordinated and complex manner. 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. 
     As machines are operated over time, the machines need to be maintained. Typically, the machines enter a maintenance facility to have this type of work performed. In previous approaches, the entire machine was taken apart to make certain that all parts were new and working. Unfortunately, these previous approaches were costly and time-consuming to undertake since the machine always had to be disassembled, and since machines were often complex and are constructed of many parts. Additionally, when parts were automatically discarded, many good parts were often thrown away as a result of, e.g., standardized maintenance procedures, thereby wasting additional resources. 
     All of these problems have led to general user dissatisfaction with previous approaches. 
     BRIEF DESCRIPTION OF THE INVENTION 
     The present invention is directed to achieve the efficient and cost-effective maintenance of industrial machines that are typically constructed of multiple components. In some embodiments, the invention described herein examines the historic genealogy of components of a machine, and the layout of a machine to identify components of the machine that need to be replaced (and possibly when) based on component characteristics, overall machine characteristics, service history of both the machine and the components, service requirements of both the machine and the components, or operating life of the machine and components, to mention a few factors. 
     By taking into account machine- and component-specific data, operating and service history, manufacturing data and other related information, the maintenance schedules determined by embodiments of the present invention are optimized. As a result and advantageously, the entire machine need not necessarily be torn apart when maintenance is required, thereby saving time to complete machine maintenance and reducing maintenance costs. Embodiments described herein are made possible through the use of machine and/or component data available through, e.g., sensing devices that may be optionally analyzed using a computerized industrial internet of things analytics platform that serves as a repository of historical component, machine, maintenance and/or manufacturing information that may be deployed at the location of the manufacturing process, at the manufacturing facility premise or in the cloud. 
     In some of these embodiments, a machine part number of an industrial machine is scanned (or otherwise obtained) when the machine enters a facility for maintenance. The scanned (or obtained) number is associated with components that comprise the machine and with characteristics of the components or machine. Based upon the characteristics of the machine or components, a customized maintenance route is produced for the machine through the facility. The customized maintenance route specifies a sequence of maintenance actions and, in aspects, the timing of these actions for the machine that are adjusted in real-time as the characteristics change. 
     In aspects, each of the characteristics is weighted. In other examples, the route is graphically displayed to a user. 
     In examples, the analysis and creation of a maintenance schedule occurs at the cloud. In other examples, the analysis and creation of the maintenance schedule occurs at a service center that is not located at the cloud. 
     In other aspects, the characteristics may be machine layout, a service history of the component, a service history of the machine, service requirements of the machine, service requirements of the component, operating life of the machine, or operating characteristic of the component. Other examples are possible. 
     In other examples, the analysis determines when components of the machine need to be replaced and this is included in the schedule. In aspects, the whole machine need not be completely disassembled during maintenance of the machine. Advantageously, this reduces maintenance time and costs associated with the machine. 
     In others of these embodiments, an apparatus includes an interface and a control circuit. The interface includes an input and an output. The input is configured to receive a scanned (or otherwise obtained) machine part number of an industrial machine when the machine enters a facility for maintenance. The control circuit is coupled to the interface. The control circuit is configured to associate the scanned number with components that comprise the machine, and with characteristics of the components or machine. The control circuit is further configured to, based upon the characteristics of the machine or components, produce a customized maintenance route for the machine through the facility. The customized maintenance route specifies a sequence of maintenance actions and timing of these actions for the machine that are adjusted in real-time as the characteristics change. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a more complete understanding of the disclosure, reference should be made to the following detailed description and accompanying drawings wherein: 
         FIG. 1  comprises a block diagram of a system and apparatus for maintaining a machine according to various embodiments of the present invention; 
         FIG. 2  comprises a flowchart of one approach for maintaining a machine according to various embodiments of the present invention; 
         FIG. 3  comprises a flowchart of an approach for producing a schedule that maintains a machine according to various embodiments of the present invention; 
         FIG. 4  comprises a block diagram showing a maintenance facility and schedules for a machine passing through this facility according to various embodiments of the present invention; 
         FIG. 5  comprises a block diagram showing an implementation for a system for maintaining a machine according to various embodiments of the present invention. 
     
    
    
     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 examine the genealogy or maintenance history of components of a machine, and the layout of a machine to see what components of the machine need to be replaced, and in some aspects the timing of the replacement based upon component characteristics, overall machine characteristics, service history of both machine and component characteristics, service requirements of both machine and component characteristics, or operating life of the machine or components, to mention a few factors. Advantageously, the entire machine need not be torn apart when maintenance is required, thereby saving time required to do the maintenance and reducing the costs for maintaining the machine. 
     In one example, the machine part number is scanned when the machine enters a facility for maintenance. In other examples, the machine part number may be obtained in other ways such as by a human operator manually entering the part number. The number is associated with components that comprise the machine, along with a maintenance history of these components. 
     In aspects, a customized route describing the path of the machine through a maintenance facility is produced. The route specifies a sequence of maintenance actions and is adjusted in real-time based on the history of machine components. For example, if a component was replaced one month ago, any steps involving replacing that component may be skipped (since it can be assumed that the component is new and does not need to be replaced). In other aspects, the route may be graphically displayed on screens to users. In still other aspects, the analysis may occur at the cloud or may be performed at the same facility as the machine. 
     It will be appreciated that it is typically time consuming and costly to take machines and all their components apart when performing maintenance. Good parts are sometimes thrown away, and this is costly and wasteful of resources. The present approaches eliminate or alleviate these and other problems associated with previous approaches. 
     Referring now to  FIG. 1 , a system  100  includes an apparatus  102 , a scanner  112 , and a machine  114 . The apparatus  102  includes an interface  104 , a control circuit  106 , and a database  108 . The interface  104  includes an input  120  and an output  122 . The control circuit is coupled to the interface  104  and the database  108 . 
     The scanner  112  is any device that scans, senses, or identifies the part number of the machine  114 . In aspects, the scanner  112  may scan labels on the machine  114  (or the components of the machine  114 ). For example, the scanner  112  may be a bar code scanner (and the machine or parts have associated labels with barcodes), or the scanner  112  may be an RFID tag reader (and the machine  114  or the components of the machine have RFID tags). The scanner  112  may be hand-held, or may be fixed in location. The scanner  112  may wirelessly transmit the scanned information to the apparatus  102 , or the scanner  112  may transmit the information over a wired link. 
     In other aspects, the part number of the machine  114  can be entered into the system  100  by an alternative mechanism. For example, a human operator may enter the part number of the machine  114  manually via a graphical user interface (such as those associated with a personal computer, smart phone, or tablet). Other mechanisms and approaches for obtaining the part number may also be used. 
     The machine  114  may be any type of industrial machine (or any other type of machine). In examples, the machine  114  may be a grinder, a milling machine, a saw, or a capper that may be used in a production or assembly line. In other examples, the machine may be a wind turbine or other type of machine that is used in a grouping (e.g., wind farm) of machines. The machine  114  may have sensors that gather and transmit data. For example, the machine  114  may have sensors that measure and transit time series data to the apparatus  102  (or some other processing or analysis device) for analysis. (e.g., predicting the operation of the machine  114 ). 
     The interface  104  performs conversion functions between the control circuit  106  and the output of the apparatus  102 . For example, data may be converted between analog and digital formats. The interface  104  is also configured to transmit and receive information, and may be structured with buffers, transmitter circuitry, and receiver circuitry. The interface  104  may be implemented as any combination of computer hardware and software. 
     The control circuit  106  is also any combination of computer hardware and/or computer software. In one example, the control circuit  106  is a microprocessor that executes computer instructions stored in a memory. 
     The database  108  may be any type of memory storage device. In aspects, the database  108  may be combinations of permanent or temporary memory. 
     In one example of the operation of the system of  FIG. 1 , the input  120  is configured to receive a scanned machine part number of an industrial machine  106  when the machine enters a facility for maintenance. The control circuit  106  is configured to associate the scanned number with components that comprise the machine  114  and with characteristics  124  (stored in the database  108 ) of the components or machine  114 . The control circuit  106  is also configured to, based upon the characteristics of the machine  114  or components, produce a customized maintenance route  116  for the machine through the facility. 
     The customized maintenance route  116  specifies a sequence of maintenance actions and in some aspects the timing of these actions for the machine  114 . The sequence and/or the timing are adjusted in real-time as the characteristics change. In one example, a customized route  116  describes the path of the machine through a maintenance facility. The route specifies a sequence of maintenance actions and is adjusted in real-time based on the history of machine components. For example, if a component was replaced one month ago, any steps involving replacing that component may be skipped (since it can be assumed that the component is new and does not need to be replaced). The layout of the machine may also be considered in determining the schedule, for example, by scheduling the maintenance to minimize machine disruption. 
     At step  118 , the sequence of actions in a maintenance sequence is rendered to a user. The rendering may be a graphical rendering on a computer display screen. The screen may be associated with any type of device and may also be made on multiple devices and on multiple formats. For example, the rendering may occur on a portable electronic device (e.g., a smart phone or tablet) of a user or may be made on a device with a fixed location (e.g., a personal computer). The schedule can be sent to these devices where these devices are located at the location of the machine  114 . In other examples, the schedule can be sent to the cloud, or through another network to a home office or other central monitoring location. 
     Referring now to  FIG. 2 , one example of an approach for maintaining a machine is described. At step  202 , a machine part number of an industrial machine is scanned when the machine enters a facility for maintenance. In other examples, the part number may be manually entered via a user interface. 
     At step  204 , component information and history is retrieved. In aspects, the scanned number is associated with components that comprise the machine and with characteristics of the components or machine. In examples, the characteristics may be machine layout, a service history of the component, a service history of the machine, service requirements of the machine, service requirements of the component, operating life of the machine, or operating characteristic of the component. Other examples are possible. 
     At step  206 , based upon the characteristics of the machine or components, a customized maintenance route is produced for the machine through the facility. The customized maintenance route specifies a sequence of maintenance actions and, in some aspects, the timing of these actions for the machine that are adjusted in real-time as the characteristics change. In aspects, each of the characteristics is weighted in the analysis. 
     At step  208 , the route is rendered to a user. In aspect, the route may be rendered to the user on a computer screen. Other actions can also be taken. For example, parts can be ordered, alerts can be sent (that alert supervisory personnel of performance issues), and the operation of the assembly line or grouping of machines can be adjusted to take into account the maintenance schedule (and, in some aspects, the timing of when the actions in the schedule are performed) being performed. 
     As mentioned, the timing of when components of the machine need to be replaced may also be determined. For example, a prediction may be made (e.g., based upon the analysis) of how long a particular component will be operable before the component becomes inoperable. In this way, a decision may be made to wait to replace a particular component rather than always replace a component. 
     In examples, the various steps described above are performed at the cloud. In other examples, the steps may be performed at the facility, or in the area where the machine is located (e.g., at the factory if the machine is an industrial machine, or at the wind farm if the machine is a windmill). 
     Referring now to  FIG. 3 , one example of producing a customized route of a machine through a maintenance facility is described. Many or all of the steps of  FIG. 3  may be implemented by a control circuit (e.g., the control circuit  106  of  FIG. 1 ). 
     At step  302 , the part number of the machine is scanned in by using some type of scanning device. Alternatively, the part number may also be manually entered by a human operator. The scanning device may be any type of scanning device such as a scanner capable of detecting and determining bar codes. In this example, the scan indicates a part number for the machine of X 1  and the machine has two components Y 1  (a blower) and Y 2  (a pulley). 
     At step  304 , the history  306  of the parts Y 1  and Y 2  is retrieved from a database (e.g., the database  108  of  FIG. 1 ). The history  306  may be described by any type of data structure or combination of data elements in any type of format or computer language. 
     In this example, the history  306  indicates that subpart Y 1  (the blower) was replaced less than 6 months ago. In aspects, the history  306  may also indicate the date that subpart Y 1  was replaced. 
     Additionally, the history  306  indicates the subpart Y 2  (the pulley) was replaced more than five years ago. In aspects, the history  306  may also indicate the date that subpart Y 2  was replaced. 
     At step  308 , an evaluation is performed. The goal of the evaluation is to determine, based upon the history  306 , whether a particular part should be replaced. Additionally, the evaluation determines a schedule or sequence of maintenance actions to be performed as the machine passes through a maintenance facility. 
     In the present example, the maintenance facility includes or is organized into a sequence of stations or maintenance areas. These stations specialize in the replacement of certain parts or group of parts of the machine. By carefully crafting the schedule based upon the history of the components (and possibly other factors such as machine layout), it is possible (and in many cases likely) that the machine need not proceed to each of the stations. Unlike previous approaches, the approaches described herein do not mandate that the entire machine be disassembled during maintenance of the machine, thereby reducing the time and decreasing the cost of maintenance operations. 
     Returning now to step  308 , a determination is made as to whether a replacement to a part has been made less than 9 months ago. If the answer to this step is affirmative, then it is assumed that the part is acceptable and need not be replaced. In the present case, part Y 1  was replaced less than 9 months ago (6 months ago). Part Y 2  was, however, replaced more than 9 months ago (5 years ago). 
     At step  310 , a schedule or sequence of operations is formed. At step  312 , it is determined that part Y 2  (the pulley) was not recently serviced and needs to be replaced. At step  314 , it is determined that part Y 1  (the blower) was recently serviced and need not be replaced. 
     At step  316 , the schedule is implemented. In this case, the schedule specifies that the machine should be transmitted to the pulley disassembly area, but should not go to the blower disassembly area. 
     It will be appreciated that the example of  FIG. 3  is only one example of an approach for determining a maintenance schedule, and that other examples are possible. For example, various factors besides the time since a part was replaced can also be evaluated. These characteristics can include historical characteristics (e.g., the time since a part was replaced) and other characteristics such as machine layout, the service history of the machine, the service requirements of the machine, the service requirements of the component, the operating life of the machine, or the operating characteristic of the component. Other examples are possible. 
     The various characteristics can be weighted in determining the maintenance schedule. For example, service history (the time since a component was replaced) may be given a higher weighting factor in determining whether to replace a component than the machine layout (when these two factors are used in determining a maintenance schedule). In other examples, the machine layout is weighted higher than the service history. 
     It will also be understood that the schedules produced can be altered in real-time and on-the-fly. For instance, as the machines enter a facility schedules can be developed. In aspects, these schedules can be dynamically changed and updated as the machine moves through a factory. 
     In one example, operator availability (to disassemble a machine and replace a part) is a factor in determining a schedule. Consequently, whether the part is to be replaced and when the part is to be replaced can be dynamically altered in real-time as the machine proceeds through a maintenance facility based upon operator availability. 
     Referring now to  FIG. 4 , one example of a dynamically changing schedule is described. This example describes a maintenance schedule for a machine A 1  having components B 1 , B 2 , and B 3 . The present example assumes service history and operator availability are the factors used in determining a maintenance schedule, and that these factors are weighted equally. 
     More specifically, a maintenance facility  400  includes maintenance stations  402 ,  404 , and  406 . Each of these stations is operated by a human operator. Station  402  in the facility  400  replaces part B 1 . Station  404  replaces part B 2 . Station  406  replaces part B 3 . 
     An analysis produces maintenance schedules, which can be rendered on a display screen (e.g., at a smart phone or personal computer). It will be appreciated that other actions may also occur. For instance, once the scheduled actions can occur to implement the schedule (e.g., send an alert to an operator to report to a station to perform maintenance on the machine). 
     An initial schedule  420  is formed at time t 1  when the machine enters the facility  400  and based upon an analysis of the service history determines that the machine A 1  should go to station  404  (to replace part B 2 ) at 12:00 am on Monday, and go to station  406  (to replace part B 3 ) Tuesday at 4:00 pm. An analysis determines that the part B 1  was recently replaced and thus need not be replaced now, while the other parts B 2  and B 3  have not recently been replaced and should be replaced now. 
     However, after entering the facility  400  on Sunday at 12:00 am, information is received where the operator of station  404  is not available on Monday, but is available on Tuesday (and the station  404  is also available at this time). A check reveals that the operator of station  406  is available on Monday (and station  406  is also available at this time). A determination is made to dynamically change the order of the operation as shown in schedule  422 . In the schedule  422 , the machine A 1  should go to station  406  (to replace part B 3 ) at 12:00 am on Monday, and go to station  404  (to replace part B 2 ) Tuesday at 4:00 pm. 
     Thus, the schedules  420  and  422  are altered in real-time and on-the-fly. As operator availability (to disassemble a machine and replace a part) changes, the schedule changes even after the machine enters the facility  400 . It will be appreciated that the example shown in  FIG. 4  is one example, and that other examples that utilize a wide variety of other factors are possible and can be used. 
     As mentioned, the approaches described herein may optionally be implemented using a computerized industrial interne 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&#39;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&#39;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. 5  illustrates generally an example of portions of a first AMP  500 . As further described herein, one or more portions of an AMP can reside in an asset cloud computing system  520 , 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 AMP  500  includes a first asset community  502  that is communicatively coupled with the asset cloud computing system  520 . In an example, a machine module  510  receives information from, or senses information about, at least one asset member of the first asset community  502 , and configures the received information for exchange with the asset cloud computing system  520 . In an example, the machine module  510  is coupled to the asset cloud computing system  520  or to an enterprise computing system  530  via a communication gateway  505 . 
     In an example, the communication gateway  505  includes or uses a wired or wireless communication channel that extends at least from the machine module  510  to the asset cloud computing system  520 . The asset cloud computing system  520  includes several layers. In an example, the asset cloud computing system  520  includes at least a data infrastructure layer, a cloud foundry layer, and modules for providing various functions. In the example of  FIG. 5 , the asset cloud computing system  520  includes an asset module  521 , an analytics module  522 , a data acquisition module  523 , a data security module  524 , and an operations module  525 . Each of the modules  521 - 525  includes or uses a dedicated circuit, or instructions for operating a general purpose processor circuit, to perform the respective functions. In an example, the modules  521 - 525  are communicatively coupled in the asset cloud computing system  520  such that information from one module can be shared with another. In an example, the modules  521 - 525  are co-located at a designated datacenter or other facility, or the modules  521 - 525  can be distributed across multiple different locations. 
     An interface device  540  can be configured for data communication with one or more of the machine modules  510 , the gateway  505 , or the asset cloud computing system  520 . The interface device  540  can be used to monitor or control one or more assets. In an example, information about the first asset community  502  is presented to an operator at the interface device  540 . The information about the first asset community  502  can include information from the machine module  510 , or the information can include information from the asset cloud computing system  520 . In an example, the information from the asset cloud computing system  520  includes information about the first asset community  502  in the context of multiple other similar or dissimilar assets, and the interface device  540  can include options for optimizing one or more members of the first asset community  502  based on analytics performed at the asset cloud computing system  520 . 
     In an example, an operator selects a parameter update for the first wind turbine  501  using the interface device  540 , and the parameter update is pushed to the first wind turbine via one or more of the asset cloud computing system  520 , the gateway  505 , and the machine module  510 . In an example, the interface device  540  is in data communication with the enterprise computing system  530  and the interface device  540  provides an operation with enterprise-wide data about the first asset community  502  in 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 device  540 , the asset cloud computing system  520 , the enterprise computing system  530 , or elsewhere. 
     Returning again to the example of  FIG. 5 , some capabilities of the first AMP  500  are illustrated. The example of  FIG. 5  includes the first asset community  502  with multiple wind turbine assets, including the first wind turbine  501 . 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 AMP  500 . 
     In an example, the multiple turbine members of the asset community  502  include assets from different manufacturers or vintages. The multiple turbine members of the asset community  502  can 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 community  502  can 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 community  502  can be in use (or non-use) under similar or dissimilar environmental conditions, or can have one or more other common or distinguishing characteristics. 
       FIG. 5  further includes the device gateway  505  configured to couple the first asset community  502  to the asset cloud computing system  520 . The device gateway  505  can further couple the asset cloud computing system  520  to one or more other assets or asset communities, to the enterprise computing system  530 , or to one or more other devices. The first AMP  500  thus represents a scalable industrial solution that extends from a physical or virtual asset (e.g., the first wind turbine  501 ) to a remote asset cloud computing system  520 . The asset cloud computing system  520  optionally 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 module  524  in the asset cloud computing system  520 . 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 gateway  505  and the data acquisition module  524 , and the asset cloud computing system  520  can be configured to use the sensor information in its analysis of one or more assets, such as using the analytics module  522 . 
     In an example, the first AMP  500  can use the asset cloud computing system  520  to retrieve an operational model for the first wind turbine  501 , such as using the asset module  521 . The model can be stored locally in the asset cloud computing system  520 , or the model can be stored at the enterprise computing system  530 , or the model can be stored elsewhere. The asset cloud computing system  520  can use the analytics module  522  to apply information received about the first wind turbine  501  or its operating conditions (e.g., received via the device gateway  505 ) to or with the retrieved operational model. Using a result from the analytics module  522 , the operational model can optionally be updated, such as for subsequent use in optimizing the first wind turbine  501  or 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 turbine  501  can be analyzed at the asset cloud computing system  520  to inform selection of an operating parameter for a remotely located second wind turbine that belongs to a different second asset community. 
     The first AMP  500  includes a machine module  510 . The machine module  510  includes a software layer configured for communication with one or more industrial assets and the asset cloud computing system  520 . In an example, the machine module  510  can be configured to run an application locally at an asset, such as at the first wind turbine  501 . The machine module  510  can be configured for use with or installed on gateways, industrial controllers, sensors, and other components. In an example, the machine module  510  includes 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 system  520 . 
     In an example, the asset cloud computing system  520  can include the operations module  525 . The operations module  525  can include services that developers can use to build or test Industrial Internet applications, or the operations module  525  can include services to implement Industrial Internet applications, such as in coordination with one or more other AMP modules. In an example, the operations module  525  includes a microservices marketplace where developers can publish their services and/or retrieve services from third parties. The operations module  525  can 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 gateway  505  between the machine module  510  and the asset cloud computing system  520 . 
     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 module  510  is 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 module  510  or at the asset cloud computing system  520 . 
     In response to the detected error conditions, the AMP can issue various mitigating commands to the asset, such as via the machine module  510 , 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 module  510  can communicate asset information to the asset cloud computing system  520 . 
     In an example, the asset cloud computing system  520  can 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 modules  510 , to effectively and efficiently improve performance of designated (e.g., similarly-situated) assets. 
     In an example, the asset cloud computing system  520  includes 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 system  520  is 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 system  520  can 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 system  520  and 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 system  520 . 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 system  520  and 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 AMP  500  can support two-way TLS, such as between a machine module and the security module  524 . In an example, two-way TLS may not be supported, and the security module  524  can treat client devices as OAuth users. For example, the security module  524  can 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 of  FIG. 5 , it will be understood that the approaches described herein with respect to  FIGS. 1-4  may be implemented using the AMP  500 , which may be deployed at the first asset community  502 , at the wind turbine  501 , or in the cloud  520 . In one example, the apparatus  102  of  FIG. 1  may be deployed at any of these locations. 
     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.