WO2023280987A1

WO2023280987A1 - Method, system and framework for facilitating predictive maintenance for machine parts in an industrial environment

Info

Publication number: WO2023280987A1
Application number: PCT/EP2022/068930
Authority: WO
Inventors: Dhananjay BHAVSAR; Nitesh PARAB; Sandeep VAIDYA; Siva Prasad Katru; Sravya KURADA
Original assignee: Siemens Aktiengesellschaft
Priority date: 2021-07-09
Filing date: 2022-07-07
Publication date: 2023-01-12
Also published as: CN117957506A; EP4352583A1

Abstract

The present invention provides a method, system, and framework for facilitating and scheduling predictive maintenance in an industrial environment. The application based ecosystem comprises an original equipment manufacturer (OEM) application and an end user application. The OEM application and the end user application may be at least one of a web based computer application or a standalone application. The application based ecosystem comprises architecture where information can flow between each end user and each OEM with necessary permissions. The application further comprises a machine learning model which is trained based on diagnostic information associated with the plurality of machine part s.

Description

METHOD, SYSTEM AND FRAMEWORK FOR FACILITATING PREDICTIVE MAINTENANCE FOR MACHINE PARTS IN AN INDUSTRIAL ENVIRONMENT

Description

The present invention relates to a field of engineering of industrial automation, and more particularly relates to a method, system, and framework for scheduling and facilitating predictive maintenance in an industrial environment.

A technical installation such as an industrial plant comprises a gigantic number of machine parts, each of which performs a large number of functions. Examples of the machine parts may include valves, positioners, controllers, cooling fans, sen sors, pipes, boilers, compressors, motors, and the like.

Efficiency of each of the machine parts may deteriorate during its lifespan. A rate at which the efficiency of the machine parts deteriorate may depend on a large number of factors such as a temperature of operation, an acidic nature of raw material handled by the machine parts, a Quality of build of the machine parts, a workmanship of a manufacturer, and a quality of ma terials used in the machine parts. Every year, a large number of machine parts may have to refurbished or replaced. It is laboursome to maintain an inventory of machine parts and to schedule predictive maintenance for all of the machine parts in an industrial environment. The machine parts of the indus trial environment are manufactured by one or more original equipment manufacturers (OEM). The one or more original equip ment manufacturers (OEM) may be incapable of analyzing perfor mance of the machine parts which are already installed in the industrial environments. Without such analysis, it is diffi cult for the OEMs to improve the machine parts which are man ufactured by the OEMs. It is further difficult to calculate an amount of money to be spent to purchase spare parts from the OEMs.

In light of above, there exists a need for an efficient method and system for facilitating predictive maintenance of machine parts in a technical installation.

Therefore, it is an object of the present invention to provide a method, system, and framework for scheduling and facilitat ing predictive maintenance in an industrial environment.

The object of the invention is achieved by a method of sched uling and facilitating predictive maintenance in an industrial environment, a system for scheduling and facilitating predic tive maintenance in an industrial environment, and a framework for scheduling and facilitating predictive maintenance in an industrial environment.

The above-mentioned and other features of the invention will now be addressed with reference to the accompanying drawings of the present invention. The illustrated embodiments are in tended to illustrate, but not limit the invention.

The present invention is further described hereinafter with reference to illustrated embodiments shown in the accompanying drawings, in which:

FIG 1 is a block diagram of an industrial environment ca pable of scheduling and facilitating predictive maintenance of machine parts, according to an embod iment of the present invention; FIG 2 is a block diagram of an industrial framework, such as those shown in FIG. 1, in which an embodiment of the present invention can be implemented;

FIG 3 is a block diagram of an automation module, such as those shown in FIG 2, in which an embodiment of the present invention can be implemented; and

FIG 4A-J is a schematic representation illustrating an exem plary method of facilitating predictive maintenance of machine parts, according to an embodiment of the present invention.

FIG. 5 is a schematic representation of an exemplary embodiment of an industrial framework configured to schedule and facilitate predictive maintenance of machine parts in the technical installation, in accordance with an embodiment of the present invention.

Various embodiments are described with reference to the draw ings, wherein like reference numerals are used to refer the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide thorough understanding of one or more embodiments. It may be evident that such embodiments may be practiced without these specific details.

FIG 1 is a block diagram of an industrial environment 100 capable of scheduling and facilitating predictive maintenance in accordance with an embodiment of the present invention. In FIG 1, the industrial environment 100 includes an industrial framework 102, a technical installation 106 and one or more client devices 120A-N. As used herein "industrial environ ment" refers to a processing environment comprising configu rable computing physical and logical resources, for example, networks, servers, storage, applications, services, etc., and data distributed over a platform, such as cloud computing platform. The industrial environment 100 provides on-demand network access to a shared pool of the configurable computing physical and logical resources. The industrial framework 102 is communicatively connected to the technical installation 106 via the network 104 (such as Local Area Network (LAN), Wide Area Network (WAN), Wi-Fi, Internet, any short range or wide range communication). The industrial framework 102 is also connected to the one or more client devices 120A-N via the network 104.

The industrial framework 102 is connected to one or more sen sors 108A-N connected to the one or more machine parts of the technical installation 106 via the network 104. The one or more sensors 108A-N may include pressure sensors, positioning sensors, temperature sensors, and the like. The one or more machine parts may include valves, pipes, robots, switches, automation devices, programmable logic controllers (PLC)s, hu man machine interfaces (HMIs), motors, valves, pumps, actua tors, sensors and other industrial equipment (s). The one or more sensors 108A-N may be connected to each other or several other components (not shown in FIG 1) via physical connections. The physical connections may be through wiring between the one or more sensors 108A-N. Alternatively, the one or more sensors 108A-N may also be connected via non-physical connections (such as Internet of Things (IOT)) and 5G networks. Although, FIG 1 illustrates the industrial framework 102 connected to one tech nical installation 106, one skilled in the art can envision that the industrial framework 102 can be connected to several technical installations 106 located at different geographical locations via the network 104.

The client devices 120A-N may be a desktop computer, laptop computer, tablet, smart phone and the like. Each of the client devices 120A-N is provided with an control tool 122A-N for controlling the one or more machine parts in the technical installation 106. The client devices 120A-N can access cloud applications (such as providing performance visualization of the one or more sensors 108A-N via a web browser). Throughout the specification, the terms "client device" and "user device" are used interchangeably. In one example, one or more client devices 120A-N is accessible to original equipment manufactur ers.

The industrial framework 102 may be a standalone server de ployed at a control station or may be a remote server on a cloud computing platform. In a preferred embodiment, the in dustrial framework 102 may be a cloud-based system. The indus trial framework 102 is capable of delivering applications (such as cloud applications) for managing a technical installation 106 comprising one or more sensors 108A-N and the machine parts. The industrial framework 102 may comprise a platform 110 (such as a cloud computing platform), an automation module 112, a server 114 including hardware resources and an operating system (OS), a network interface 116 and a database 118. The network interface 116 enables communication between the indus trial framework 102, the technical installation 106, and the client device(s) 120A-N. The interface (such as cloud inter face) (not shown in FIG 1) may allow original equipment manu facturers and one or more end users at the one or more client device (s) 120A-N to access information captured by the one or more sensors 108A-N. The server 114 may include one or more servers on which the OS is installed. The servers 114 may comprise one or more processors, one or more storage devices, such as, memory units, for storing data and machine-readable instructions for example, applications and application pro gramming interfaces (APIs), and other peripherals required for providing computing (such as cloud computing) functionality. The platform 110 enables functionalities such as data recep tion, data processing, data rendering, data communication, etc. using the hardware resources and the OS of the servers 114 and delivers the aforementioned services using the appli cation programming interfaces deployed therein. The platform 110 may comprise a combination of dedicated hardware and soft ware built on top of the hardware and the OS. The platform 110 may further comprise an automation module 112 configured for facilitating predictive maintenance of machine parts in the technical installation 106. Details of the automation module 112 is explained in FIG. 3.

The database 118 stores the information relating to the tech nical installation 106 and the client device(s) 120A-N. The database 118 is, for example, a structured query language (SQL) data store or a not only SQL (NoSQL) data store. In an exem plary embodiment, the database 118 may be configured as cloud- based database implemented in the industrial environment 100, where computing resources are delivered as a service over the platform 110. The database 118, according to another embodi ment of the present invention, is a location on a file system directly accessible by the automation module 112. The database 118 is configured to store engineering project files, engi neering programs, object behavior model, parameter values as sociated with the one or more engineering objects 108A-N, test results, simulation results, status messages, one or more sim ulation instances, graphical programs, program logics, program logic patterns, and engineering object properties, one or more engineering object blocks, relationship information between the engineering objects, requirements, program update messages and the like.

FIG 2 is a block diagram of an industrial framework 102, such as those shown in FIG 1, in which an embodiment of the present invention can be implemented. In FIG 2, the industrial frame work 102 includes a processor(s) 202, an accessible memory 204, a storage unit 206, a communication interface 208, an input-output unit 210, a network interface 212 and a bus 214.

The processor(s) 202, as used herein, means any type of com putational circuit, such as, but not limited to, a micropro cessor unit, microcontroller, complex instruction set compu ting microprocessor unit, reduced instruction set computing microprocessor unit, very long instruction word microprocessor unit, explicitly parallel instruction computing microprocessor unit, graphics processing unit, digital signal processing unit, or any other type of processing circuit. The processor(s) 202 may also include embedded controllers, such as generic or programmable logic devices or arrays, application specific in tegrated circuits, single-chip computers, and the like.

The memory 204 may be non-transitory volatile memory and non volatile memory. The memory 204 may be coupled for communica tion with the processor(s) 202, such as being a computer- readable storage medium. The processor(s) 202 may execute ma chine-readable instructions and/or source code stored in the memory 204. A variety of machine-readable instructions may be stored in and accessed from the memory 204. The memory 204 may include any suitable elements for storing data and machine- readable instructions, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, a hard drive, a remov able media drive for handling compact disks, digital video disks, diskettes, magnetic tape cartridges, memory cards, and the like. In the present embodiment, the memory 204 includes an integrated development environment (IDE) 216. The IDE 216 includes an automation module 112 stored in the form of ma chine-readable instructions on any of the above-mentioned storage media and may be in communication with and executed by the processor(s) 202.

When executed by the processor(s) 202, the automation module 112 causes the processor(s) 202 to schedule and facilitate predictive maintenance of machine parts in the technical in stallation 106.

The storage unit 206 may be a non-transitory storage medium configured for storing a database (such as database 118) which comprises server version of the plurality of programming blocks associated with the set of industrial domains.

The communication interface 208 is configured for establishing communication sessions between the one or more client devices 120A-N and the industrial framework 102. The communication interface 208 allows the one or more engineering applications running on the client devices 120A-N to import/export engineering project files into the industrial framework 102.

The input-output unit 210 may include input devices a keypad, touch-sensitive display, camera (such as a camera receiving gesture-based inputs), etc. capable of receiving one or more input signals, such as user commands to schedule the predictive maintenance. Also, the input-output unit 210 may be a display unit for displaying a graphical user interface which visual izes a plurality of process values associated with the machine parts in the technical installation 106 and also displays the status information associated with each of teh one or more sensors 108A-N. The bus 214 acts as interconnect between the processor 202, the memory 204, and the input-output unit 210.

The network interface 212 may be configured to handle network connectivity, bandwidth and network traffic between the indus trial framework 102, client devices 120A-N and the technical installation 106.

Those of ordinary skilled in the art will appreciate that the hardware depicted in FIG 2 may vary for particular implemen tations. For example, other peripheral devices such as an op tical disk drive and the like, Local Area Network (LAN), Wide Area Network (WAN), Wireless (e.g., Wi-Fi) adapter, graphics adapter, disk controller, input/output (I/O) adapter also may be used in addition or in place of the hardware depicted. The depicted example is provided for the purpose of explanation only and is not meant to imply architectural limitations with respect to the present disclosure.

Those skilled in the art will recognize that, for simplicity and clarity, the full structure and operation of all data processing systems suitable for use with the present disclo sure is not being depicted or described herein. Instead, only so much of the industrial framework 102 as is unique to the present disclosure or necessary for an understanding of the present disclosure is depicted and described. The remainder of the construction and operation of the industrial framework 102 may conform to any of the various current implementation and practices known in the art.

FIG 3 is a block diagram of an automation module 112, such as those shown in FIG 2, in which an embodiment of the present invention can be implemented. In FIG 3, the automation module 112 comprises a request handler module 302, an object behavior model generation module 304, an analysis module 306, a sched uler module 308,an engineering object database 310, a valida tion module 312 and a deployment module 314. FIG. 3 is ex plained in conjunction with FIG. 1 and FIG. 2.

The request handler module 302 is configured for receiving the request to perform predictive maintenance of machine parts in the technical installation 106. For example, the request is received from one of the one or more users external to the industrial environment 100 via a network. In alternative em bodiment, the request is received from the one or the one or more client devices 120A-N via the network.

The object behavior model generation module 304 is configured to generate an artificial intelligence model based on a plu rality of parameters associated with each machine part in the technical installation 106.

The analysis module 306 is configured for analyzing the gen erated artificial intelligence model associated with each ma chine part in the technical installation.

The scheduler module 308 is configured for scheduling predic tive maintenance tasks for each machine part of the machine parts.

The engineering object database 310 is configured for gener ating an engineering object library comprising one or more object behavior models, for the machine parts in the technical installation 106, and also for the one or more sensors 108A- N, physical connections between the one or more sensors 108A- N, and a plurality of parameter values associated with the one or more sensors 108A-N and the physical connections. The en gineering object database 310 is configured for continuously updating the engineering object library with updated versions of the engineering programs. Also, the engineering object da tabase 310 is configured for maintaining the engineering ob ject library in an ontology schema.

The validation module 312 is configured to generate a simula tion instance for the machne parts of the technical installa tion 106.

The deployment module 314 is configured for deploying predic tive maintenance tasks for teh machine parts installed in the technical installation 106.

In one example, a plurality of machine parts in the technical installation 106 may comprise a plurality of control valves used in the technical installation 106. Further, a plurality of original equipment manufacturers (OEM) may manufacture the plurality of machine parts. The plurality of OEMs may supply the plurality of machine parts to a plurality of end users. Each of the plurality of machine parts may comprise one or more sensors such as the one or more sensors 108A-N. The one or more sensors 108A-N may comprise valve positioners.

The plurality of OEMs dispatch the plurality of machine parts to the plurality of end users. The plurality of machine parts are installed along with the one or more sensors 108A-N. Each of the plurality of end users may possess the plurality of machine parts manufactured by multiple OEMS of the plurality of OEMs. Further, each machine part of the plurality of machine parts may comprise the one or more sensors 108A-N. The automation module 112 may be configured to receive diag nostic information associated with the plurality of machine parts. The automation module 112 may be configured to analyze the diagnostic information and display results of analysis to one or more end users. In one example, the automation module 112 may be implemented as an application program used by the one or more end users via the one or more client devices 120A- N. In one example, the automation module 112 is a part of an industrial framework for scheduling and facilitating predic tive maintenance on the plurality of machine parts manufac tured by the plurality of original equipment manufacturers.

For example, referring to FIG. 5, a schematic representation is shown of an industrial framework 500 configured to schedule and facilitate predictive maintenance of a plurality of ma chine parts in the technical installation 106. The industrial framework 500 is implemented in the industrial environment 100 which comprises the industrial framework 102, the one or more sensors 108A-N, the plurality of machine parts, the one or more client devices 120A-N, and the automation module 112. In one example, the industrial framework 500 is an application based ecosystem.

The industrial framework 500 comprises a service provider server 502, a plurality of machine parts (504A and 504B) in stalled in factories of a plurality of end users (506A, 506B, and 506C).

Each of the plurality of machine parts (504A and 504B) are manufactured by each of a plurality of original equipment man ufacturers (OEM) (508A and 508B). For example, a first type (504A) of machine parts is manufactured by a first OEM 508A. A second type (504B) of machine parts is manufactured by a second OEM 508B. Each of the plurality of end users (506A-C) is enabled to access a specific end user app of a plurality of end user apps (510A-C). A first end user 506A accesses a first end user app 510A. A second end user 506B accesses a second end user app 510B. A third end user 506C accesses a third end user app 510C. Similarly, the plurlaity of OEMs (508A-B)can access a different app of a plurality of oem apps (512A-B). A first OEM 508A can access a first oem app 512A. A second OEM 508B can access a second oem app 512B.

The plurality of OEM apps (512A-B) and the plurality of end user apps (510A-C) are executable in any of the one or more client devices (120A-N). The service provider server 502 is implementable in the industrial framework 102. The plurality of OEM apps (512A-B) and the plurality of end user apps (510A- C) may be at least one of a web based computer application or a standalone application.

Each of the plurality of OEM apps 512A-B receives information associated with machine parts manufactured by the respective OEM associated with the particular OEM app of the plurality of OEM apps 512A-B. For example, a first OEM app 512A receives information about the first type 504A of machine parts manu factured by the first OEM 508A. It is noted that the first type 504A of machine parts manufactured by the first OEM 508A is present in a factory/technical installation 106 of the plu rality of end users (506A-C). The received information com prises diagnostic data associated with the each machine parts in the first type 504A of machine parts manufactured by the first OEM 508A. The diagnostic data comprises a maintenance data, a wear and tear data, one or more key performance indi ces, a prediction of failure of the particular machine part, and an analysis of industrial down time caused by the first type 504A of machine parts manufactured by the first OEM 508A. The first OEM application 512A is configured to analyze the received information to provide the following information to the first OEM 508A:

1.Customer and application wise performance monitoring of each machine part of the first type 504A of machine parts manufactured by the first OEM 508A.

2.Number of machine parts among the first type 504A of machine parts, which are in operation and number of ma chine parts which are under maintenance

3.Maintenance scheduling of the first type 504A of machine parts and details on necessary spares required to perform the maintenance.

4.Prediction on failure and reason for failure of the first type 504A of machine parts.

5.Analysis on frequent failure of the first type 504A of machine parts.

6.A comparison of performance of the first type 504A of machine parts, when installed in the factory of the first end user 506A, the second end user 506B, and the third end user 506C. The comparison is provided in terms of an installation in which the machine parts are installed, and an application in which the machine parts are used.

7.Track a warranty on a sensor associated with each of the first type 504A of machine parts. For example, in a case where the sensor is a valve positioner and the machine part is a control valve. The first OEM app 512A displays information associated with positioner warranty and con trol valve warranty

8.Application specific inventory of control valves vs in stalled base of that application

9.Analysis of downtime caused by the first type 504A of machine parts. 10. Planning of Spares and replacement of machine parts in the first type 504A of machine parts.

The plurality of end user apps 510A-B receive information from the plurality of machine parts 504A-B installed in a large number of factories of the plurality of end users (506A-C). The received information comprises diagnostic data associated with the each of the plurality of machine parts 504A-B. The diagnostic data comprises a maintenance data, a wear and tear data, one or more key performance indices, a prediction of failure of the particular machine part, and an analysis of industrial down time caused by the plurality of machine parts 504A-B. In one example, the information is received from the service provider server 502. In another example, the infor mation is generated by the automation module 112 based on historical plant data associated with the plurality of machine parts (504A-B). The historical plant data comprises infor mation associated with historical KPIs of the plurality of machine parts.

The first end user application 510A is configured to analyze the received information to provide the following information to the first OEM 508A:

1.Maintenance schedule for maintenance of machine parts and Process instruments

2.Planning of spares and inventory of machine parts and Process instruments 3.Comparative Performance evaluation of each type of ma chine parts of the plurality of machine parts (504A-B) with respect to a specific OEM which has manufactured the particular type of machine parts. Each of the plurality of OEM 508A-B are rated based on the comparative perfor mance analysis.

4.Alarm history for machine part monitoring

5.Prediction on process irregularities in the plurality of machine parts 504A-B.

6.Process media leakages around the plurality of machine parts 504A-B, especially in cases where the plurality of machine parts 504A-B comprises control valves.

7.A predicted life of the plurality of machine parts 504A- B)

8.Performance analysis and comparison of performance be tween different types of machine parts (504A-B) manufac tured by different OEMS of the plurality of oems 512A-B.

9.Analysis of downtime (with respect to time and cost) as sociated with each machine part of the plurality of ma chine parts 504A-B.

10. Forecast Valve maintenance expenditure associated with the plurality of machine parts 504A-B

11. Spares management and warranty period monitoring of the plurality of machine parts 504A-B

12. Air consumption comparison utility for carbon foot print monitoring of each of the plurality of machine parts 504A-B

13. Comparative analysis of design characteristics of each of the plurality of machine parts 504A-B vs actual characteristics of the plurality of machine parts 504A- B, with reference to an OEM of the plurality of OEMS 508A- B, which has manufactured the particular machine part of the plurality of machine parts 504A-B. 14. Variations in energy consumption by each of the plu rality of machine parts 504A-B with reference to an OEM of the plurality of OEMS 508A-B, which has manufactured the particular machine part of the plurality of machine parts 504A-B. The variation in energy consumption is analyzed based on surrounding ambient temperature which varies according to seasonality.

15. Pipeline insulation condition monitoring (tempera ture losses due to weak insulations)

16. Information associated with Integration of machine part test bench in the industrial framework 500.

The one or more client devices 120A-N may also host application programs used by the original equipment manufacturers. The one or more end users may upload data associated with each of the OEMs into a cloud server. In such cases, an application program used by the OEM may be configured to analyze data in the cloud server to generate one or more data insights about the OEMs. In one example, the application program used by the OEM may be hosted in the cloud server. Thus, the OEM may use the appli cation program to retrieve information about the plurality of machine parts manufactured by the OEMs even in cases where the plurality of machine parts are already installed in the tech nical installation 106. The information associated with the plurality of machine parts include a location of the plurality of machine parts and diagnostic information associated with the plurality of machine parts. The one or more end users has information about the plurality of machine parts installed in the technical installation 106.

Referring to FIG. 4A, FIG. 4A is a schematic representation of a scenario in which the plurality of machine parts comprises a plurality of control valves, in accordance to an embodiment of the present invention. The automation module 112 is config ured to schedule and predict maintenance of the plurlaity of machine parts. The automation module 112 has an input layer 402, an intelligence layer 404, an output layer 406, and a visualization layer 408. In such a case, the input layer 402 is configured to receive a maintenance schedule for the plu rality of machine parts. Further, the input layer 402 is con figured to receive informaiton about spares required for the scheduled maintenance. The input layer 402 is configured to receive from one or more positioners of the plurality of ma chine parts, diagnostic data comprising a number of strokes (STRKS), a number of times a direction of a positioner has changed (CHDIR), online pneumatic leakages (ONLK), Static friction (STIC), and end stop behaviour (ZERO, OPEN), and con trol valve deviation (DEVI).

Next, the intelligence layer 404 is configured to compare the information received by input layer 402, with thresholds which are already configured in the positioner during a parameteri zation of the plurality of the machine parts. The intelligence layer 404 first considers a first threshold. Whenever the first threshold gets violated, the output layer 406 is configured to generate an alarm. The output layer 406 is configured to sched ule a related maintenance event.

The intelligence layer 404 further comprises a machine learn ing model. The machine learning model analyzes historical data associated with the diagnostic information and predicts time slots in which the generated thresholds are violated by the diagnostic models. For example, if a particular key perfor mance indicator is predicted to have wear and tear then, by a particular date, then the intelligence layer 404 causes the output layer 406 to generate an alarm. Further, the machine learning model is trained to map the diagnostic information with one or more faults which frequently occur in the plurality of machine parts. The machine learning model is trained to map one or more spares to each of the one or more faults. The intelligence layer 404 is configured to use the machine learn ing model to identify a plurality of spares which is required to rectify a plurality of faults in the plurality of machine parts of the technical installation 106. From the data analy sis, the intelligence layer 404 is configured to mape a fault- to-spares mapping table. In one example, the machine learning model are regression-based models which predict a maintenance event given the diagnostic information as input.

The intelligence layer 404 is further configured to analyze historical test data that is obtained from the one or more end users. The intelligence layer 404 further utilizes the map pings made by the machine learning model, between the plurality of maintenance event/faults, remedy information along with a plurality of spare parts required for the maintenance.

Combining the maintenance event prediction derived from the generated model and the fault-to-spares mapping, the intelli gence layer 404 is configured to predic the fault and spares required.

The intelligence layer 404 is further configured to perform the timeseries analysis on the diagnostic information by ap plying Autoregressive integrated moving average (ARIMA) models and forecast fault occurrence in the plurality of machine parts.

In one example, the output layer 406 is configured to transform prediction information to visualization layer 410 via friendly communication exchange formats through JSON, XML formats. The visualization layer 410 is responsible for presenting re sults generated by the intelligence layer 404 and the output layer 406 in a web application in an intuitive way via rich user interfaces.

FIG. 4B is a schematic representation of a scenario in which the plurality of OEMs, which manufacture the plurality of ma chine parts are rated, in accordance to an embodiment of the present invention. The input layer 402 is configured to receive a plurality of criteria associated with a plurality of machine parts, such as average lifespans, list of faults, a list of maintenance done for the plurality of machine parts, a count of times similar faults are repeated. The intelligence layer 404 is configured to rate the plurality of OEMs are rated based on the received plurality of criteria. For example, a first OEM may manufacture control valves which have less faults in comparison with control valves manufactured by a second OEM. Further, a similar valve manufactured by a same OEM may provide different performances for different applications. The intel ligence layer 404 analyzes the plurality of criteria of the plurality of machine parts when the plurality of machine parts are assigned for a plurality of applications. Further, the output layer 406 is configured to display result of the anal ysis. The result of the analysis comprises information regard ing relative performance of the plurality of machine parts in each of the plurality of applications.

FIG. 4C is a schematic representation of a scenario in which alarms generated due to each of the plurality of machine parts are monitored, in accordance to an embodiment of the present invention.

The input layer 402 is configured to collate alarms generated due to faults in the plurality of machine parts. The input layer 402 recieves all the diagnostic information as mentioned in FIG. 4A, in addition, the input layer 402 also monitors dead band (DBUP and DBDOWN), temperature (TMAX and TMIN) and average set point (PAVG). The intelligence layer 404 analyses the diagnostic information and the alarms generated due to the plurality of machine parts. The output layer 406 is configured to display a relation between each of the alarms generated due to each machine parts of the plurality of machine parts. The visualization layer 408 is configured to output Key perfor mance trends associated with the generated alarms, and also provides troubleshooting tips for each generated alarms.

FIG. 4D is a schematic representation of a scenario in which process irregularities are predicted in each process performed by the plurality of cotnrol valves, in accordance to an embod iment of the present invention.

The input layer 402 is configured to receive all the diagnostic information mentioned with reference to FIG. 4A-C. Further, the intelligence layer 404 is configured to determine a fixed set of objectives for each process done by the plurality of machine parts. For a particular process, for a fixed set of objectives, there is a typical range of the diagnostic infor mation. The intelligence layer 404 is configured to analyze the diagnostic information to predict process irregularities. The output layer 406 and the visualization layer 408 are con figured to notify an operator about the predicted process ir regularities .

FIG. 4E is a schematic representation of a scenario in which process media leakages are predicted in the plurality of cotnrol valves, in accordance to an embodiment of the present invention. The intelligence layer 404 is configured to analyze the diag nostic information to predict occurence of process media leak ages in the plurality of machine parts. For example, the in telligence layer 404 may be configured to analyze the minimum and maximum temperature and an ambient temperature to predict process media leakages. In one example, a machine learning model is used to predict the process media leakages.

FIG. 4F is a schematic representation of a scenario in which a lifespan of the plurality of machine parts is predicted, in accordance to an embodiment of the present invention. The in telligence layer 404 is configurdd to predict a lifespan of a positioner of the plurality of machine parts. In one example, a counter is used to count a number of cycles of expansion and contraction of the plurality of machine parts. Each of the plurality of machine parts have a total limited number of expansion-contraction cycle. The intelligence layer 404 deter mines remaining lifespan of the plurality of machine parts based on the count of the number of cycles of expansion and contraction and total limited number of expansion-contraction cycles.

FIG. 4G is a schematic representation of a scenario in which a downtime caused by the plurality of machine parts is pre dicted, in accordance to an embodiment of the present inven tion.

The input layer 402 is configured to receive an operator log of an operator of the technical installation 106. The input layer 402 is configured to determine an amount of time logged for rectification of faults in each of the plurality of machine parts. The input layer 402 is further configured to log any spare consumed in rectification of the faults. The output layer 408 is configured to display data associated with downtime caused by each of the plurality of machine parts and also the spares consumed for the rectification process.

FIG. 4H is a schematic representation of a scenario in which a budget required to maintain the plurality of machine parts is predicted, in accordance to an embodiment of the present invention.

The input layer 402 is configured to received budget for maintenance in a fiscal year. The intelligence layer 404 is configured to analyze any predicted maintenance events, and spares consumption predicted for the fiscal year and predicts a remaining budget at an end of the fiscal year. For example, an end user is enabled to define the fiscal year in the input layer 402. The end user is further enabled to enter the budget for maintenance activity for the plurality of machine parts. The operator of the technical installation 106 is configured to enter data about any spare consumed, during closing of each maintenance event. The intelligence layer 406 is configured to calculate the remaining budget.

FIG. 41 is a schematic representation of a scenario in which spares required to maintain the plurality of machine parts is ordered, in accordance to an embodiment of the present inven tion.

The input layer 402 is configured to determine a list of spares required for each maintenance event.

The output layer 406 is configured to order spares in the list from the plurality of oems. FIG. 4J is a schematic representation of a scenario in which energy consumed by the plurality of machine parts is predicted, in accordance to an embodiment of the present invention.

The intelligence layer 404 is configured to analyze the diag nostic information to calculate energy consumed by each of the plurality of machine parts. Further, the energy consumed by each machine part made by a first OEM is compared with energy consumed by each machine part made by a second oem. Also, the energy consumed by each positioner made by the first OEM is compared with energy consumed by each positioner is made by the second oem. The output layer 408 is configured to present the difference in energy consumed to the end user.

In an exemplary embodiment of the present invention, the input layer 402 utilizes process data received from valve positioner measurements made by the positioners, through typical Indus trial Internet of things (IioT) connectivity networks. Data obtained from the positioners involves direct KPIs and auxil iary information such as static data and plant information.

The present invention can take a form of a computer program product comprising program modules accessible from computer- usable or computer-readable medium storing program code for use by or in connection with one or more computers, processors, or instruction execution system. For the purpose of this de scription, a computer-usable or computer-readable medium can be any apparatus that can contain, store, communicate, propa gate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The medium can be electronic, magnetic, optical, electromag netic, infrared, or semiconductor system (or apparatus or de vice) or a propagation mediums in and of themselves as signal carriers are not included in the definition of physical com puter-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, random access memory (RAM), a read only memory (ROM), a rigid magnetic disk and optical disk such as compact disk read-only memory (CD-ROM), compact disk read/write, and DVD. Both processors and program code for implementing each aspect of the technology can be centralized or distributed (or a combination thereof) as known to those skilled in the art.

While the present invention has been described in detail with reference to certain embodiments, it should be appreciated that the present invention is not limited to those embodiments. In view of the present disclosure, many modifications and var iations would be present themselves, to those skilled in the art without departing from the scope of the various embodiments of the present invention, as described herein. The scope of the present invention is, therefore, indicated by the follow ing claims rather than by the foregoing description. All changes, modifications, and variations coming within the mean ing and range of equivalency of the claims are to be considered within their scope. All advantageous embodiments claimed in method claims may also be apply to system/apparatus claims.

List of reference signs

1.an industrial environment 100

2.an industrial control system 102

3.one or more client devices 120A-N

4.a network 104

5.one or more engineering objects 108A-N

6.one or more client devices 120A-N

7.a platform 110

8.an automation module 112

9.a server 114

10.a network interface 116

11. a database 118

12. a processor(s) 202

13. an accessible memory 204

14.a storage unit 206

15.a communication interface 208

16.an input-output unit 210

17.a network interface 212

18.a bus 214

19.an integrated development environment (IDE) 216

20.a request handler module 302,

21.an object behavior model generation module 304,

22.an analysis module 306,

23.a modifier module 308

24.an engineering object database 310

25.a validation module 312

26.a deployment module 314.

27.An input layer 402

28.An intelligence layer 404

29.An output layer 408

30.A visualization layer 410

31.A service provider server 502

32.A plurality of machine parts 504A-B

33.A plurality of end users 506A-C

34.A plurality of oems 508A-B

35.A plurality of end user apps 510A-C

36.A plurality of oem apps 512A-B

Claims

1.A method of facilitating predictive maintenance of machine parts in a technical installation (106), the method com prising : receiving, by a processing unit (202), diagnostic information associated with a plurality of machine parts; receiving, by the processing unit (202), information about spares associated with a scheduled maintenance; comparing, by the processing unit (202), the re ceived information with one or more thresholds associated with the plurality of machine parts; generating, by the processing unit (202), at least one alarm based on a result of comparison of the received information with the one or more thresholds associated with the plurality of machine parts; and scheduling, by the processing unit (202), a mainte nance event based on the generated alarm.

2. The method according to claim 1, further comprising: analyzing using a machine learning model, by the processing unit (202), historical data associated with diagnostic information associated with the plurality of machine parts; predicting, by the processing unit (202), a plural ity of time slots in which the one or more thresholds are violated by the plurality of machine parts; mapping, by the processing unit (202), the diagnos tic information with a plurality of spare parts associ ated with the plurality of machine parts; generating, by the processing unit (202), a list of spare parts required to perform the scheduled maintenance activity.

3.The method according to any of the claims 1 or 2, further comprising: analyzing, by the processing unit (202), the plural ity of machine parts based on a plurality of OEMs of the plurality of the plurality of machine parts; and displaying, by the processing unit (202), a result of the analysis.

4.The method according to any of the claims 1 to 3, further comprising: determining, by the processing unit (202), a plural ity of process irregularities based on an analysis of the diagnostic information associated with the plurality of machine parts; and notifying, by the processing unit (202), an operator of the determined process irregularities.

5.The method according to any of the claims 1 to 3, further comprising: predicting, by the processing unit (202), a lifespan of each of the plurality of machine parts based on an analysis of the diagnostic information; and notifying, by the processing unit (202), an operator of the determined lifespan of the plurality of machine parts.

6.The method according to any of the claims 1 to 3, further comprising: predicting, by the processing unit (202), energy consumed by each of the plurality of machine parts based on an analysis of the diagnostic information; and notifying, by the processing unit (202), an operator of the predicted energy consumed by each of the plurality of machine parts.

7.An industrial framework (102) for facilitating predictive maintenance in a plurality of machine parts, wherein the industrial framework (102) comprises: a processing unit (202); and a memory (204) coupled to the processing unit (202), wherein the memory comprises a plurality of end user apps (50A-C) and a plurality of oem apps (512A-B) stored in the form of machine-readable instructions executable by the one or more processor(s), wherein the plurality of end user apps (50A-C) and the plurality of oem apps (512A-B) is capable of performing a method according to any of the claims 1 to 6.

8.The industrial framework (102) of claim 7, wherein: a first OEM app (512A) of the plurality of oem apps (512A-B) is configured to: receive information about one or more machine parts manufactured by a first OEM (508A); and analyze the received information to generate information about:

1)number of machine parts in operation,

2)number of machine parts in maintenance,

3)maintenance schedule for the plurality of machine parts,

4)prediction of failure in the plurality of machine parts,

5) comparison of performance statistics of each machine part with other machine parts in the plurality of machine parts, and 6)warranty information associated with each of the plurality of machine parts.

9.The industrial framework (102) of claim 7, wherein: the plurality of end user apps (510A-B) is con figured to: receive diagnostic information from the plural ity of machine parts (504A-B); and analyze the received diagnostic information to generate information about:

1)number of machine parts in operation,

2)alarm history for the plurality of ma chine parts,

3)maintenance schedule for the plurality of machine parts,

4)prediction of failure in the plurality of machine parts,

5)comparison of performance statistics of each machine part with other machine parts in the plurality of machine parts, and

6)prediction of process irregularities for each of the plurality of machine parts,

7)prediction of air consumption compari son for each of the plurality of machine parts .

10. An industrial environment (100) comprising: an industrial framework (102) as claimed in claim

9; a technical installation (106) comprising the plu rality of machine parts (504A-C); and a plurality of human machine interfaces (120A-N) communicatively coupled to the industrial framework (102) via a network (104), wherein the industrial framework (102) is configured to perform a method ac- cording to any of the claims 1 to 6.

11. A computer-program product, having machine-readable in structions stored therein, that when executed by a pro cessing unit (202), cause the processors to perform a method according to any of the claims 1-6.