WO2022034603A1

WO2022034603A1 - "system and method for optimizing operations of milling machines in industrial setting"

Info

Publication number: WO2022034603A1
Application number: PCT/IN2021/050196
Authority: WO
Inventors: Jignesh GUPTA; Harish Nair; AshishKumar SHUKLA
Original assignee: Siemens Ltd.
Priority date: 2020-08-11
Filing date: 2021-03-02
Publication date: 2022-02-17

Abstract

A system and method for optimizing operations of one or more milling machines in an industrial setting utilizing an industrial control system. The method comprises receiving data related to multiple operating parameters with corresponding one or more output values for each of the one or more milling machines, with one or more of the multiple operating parameters being assigned a unique identifier. The method further comprises authenticating the assigned unique identifier. The method further comprises determining an optimal value for each of one or more of the multiple operating parameters having corresponding assigned unique identifier authenticated thereby. The method further comprises instructing, by the server, the industrial control system, to configure the one or more milling machines based on the corresponding determined optimal value for each of one or more of the multiple operating parameters thereof.

Description

Beschreibung / Description

Bezeichnung der Erfindung / Title of the invention

“SYSTEM AND METHOD FOR OPTIMIZING OPERATIONS OF MILLING MACHINES IN INDUSTRIAL SETTING”

The present disclosure generally relates to industrial machines optimization, and more particularly to a method and a system for optimizing operations of industrial machines in an industrial setting. The present disclosure further relates to remotely monitoring industrial machines, and providing data analysis and predictive maintenance using artificial intelligence.

State of the art industrial machines are highly specialised to perform operations like for instance milling, turning, drilling, boring, punching, punch pressing, bending, welding and assembly operations. Such machines are a substantial investment to most potential customers, and therefore the productivity (output) that the machines contribute to the business is a key factor and thus need to be optimized. Poor efficiency is commonly observed in industrial machines, due to the excessive overhead and technical challenges faced in manual tuning. Milling machines are an example of complex industrial machines whose outputs are affected by a multiplicity of non-linear, time-varying states that are mutually coupled in an uncertain manner.

Management, control and monitoring of operations performed by an industrial machine need expertise and experience from a machine operator as well as software -based support systems to work out. In practice, experienced operators spend huge efforts on fine-tuning the machines to achieve near-optimal performance. Operators may only tune the machines in a trial- and-error fashion, trying to understand the industrial setting of the machines with their experience and sometimes simple heuristics. However, high complexity of modern industrial machines often leads to ineffectiveness of conventional tuning techniques used by the operators. Furthermore, a determined tuned configuration for such a machine may not adapt well to the varying environmental and equipment conditions, thus each machine may need to be independently and individually optimized based on its unique condition and characteristics, which if done manually is a significant overburden.

Some sophisticated optimization approaches to mill operation such as rule-based expert systems and highly parameterized analytic models have been attempted. More recently artificial neural networks have been introduced to solve certain problems of mill operation. However, there still exists issues with fetching and authentication of data related to the industrial machines, which is another challenge for optimizing operations of industrial machines in an industrial setting.

For example, industrial controllers and their associated I/O devices are central to the operation of modem automation systems. These controllers interact with field devices on the plant floor to control automated processes relating to such objectives as product manufacture, material handling, batch processing, supervisory control, and other such applications. Industrial controllers store and execute user-defined control programs to effect decisionmaking in connection with the controlled process. A huge amount of data is generated from such industrial devices and its associated I/O, telemetry devices for near real-time metering, motion control devices and other devices employed in an industrial plant. Moreover, since many industrial facilities operate on a 24-hour basis, their associated automation systems can generate a vast amount of potentially useful data at high rates. For an enterprise with multiple industrial facilities, the amount of generated automation data further increases.

The large quantity of industrial data makes it possible to apply a broad range of analytics for optimization of industrial machines. However, access to the industrial data is typically limited to applications and devices that share a common network with the industrial controllers that collect and generate the data. As such, plant personnel wishing to leverage the industrial data generated by their systems in another application (e.g., a reporting or analysis tool, notification system, visualization application, backup data storage, etc.) are required to maintain such applications on-site using local resources. One major problem associated with exiting monitoring systems is that, although a given industrial setting may comprise multiple plant facilities at geographically diverse locations (or multiple mobile systems having variable locations), the scope of such applications is limited only to data available on controllers residing on the same local network as the application. Moreover, the analysis of data is usually done manually which is time consuming and prone to errors and might further lead to repercussions in the operations of the industrial facility.

Another major problem is that existing systems fail to integrate with a varied range of Original Equipment Manufacturers (OEMs), and thereby failing to integrate with all systems and devices across different verticals in the one or more industrial facilities. Moreover, several other challenges in the existing industrial monitoring systems include unavailability of maintenance and breakdown history, unavailability of historic data, unavailability of required database, absence of manufacturing trends analysis on anytime and anywhere basis, unavailability of self-service application, and unavailability of report-based analysis systems.

In light of the above, it is an object of the present disclosure to provide a method and a system for optimizing operations of one or more industrial machines in an industrial setting.

The object of the present disclosure is also achieved by a method for optimizing operations of one or more milling machines in an industrial setting utilizing an industrial control system. The method comprises receiving, from the industrial control system, by a server, data related to multiple operating parameters with corresponding one or more output values for each of the one or more milling machines, with one or more of the multiple operating parameters being assigned a unique identifier. The method further comprises authenticating, by the server, the assigned unique identifier of one or more of the multiple operating parameters. The method further comprises determining, by the server, using a trained machine learning function, an optimal value for each of one or more of the multiple operating parameters having the corresponding assigned unique identifier authenticated thereby, based on the data related to multiple operating parameters with corresponding one or more output values for each of the one or more milling machines, such that the corresponding determined optimal value when implemented for each of one or more of the multiple operating parameters results in one or more optimized output values for the one or more milling machines. The method further comprises instructing, by the server, the industrial control system, to configure the one or more milling machines based on the corresponding determined optimal value for each of one or more of the multiple operating parameters thereof.

In one or more embodiments, the method further comprises determining, by the server, using the trained machine learning function, one or more anomalies in at least one component of the one or more milling machines if the one or more output values for each of the one or more milling machines substantially differs from the one or more optimized output values therefor, post configuring the one or more milling machines based on the corresponding determined optimal value for each of one or more of the multiple operating parameters thereof.

In one or more embodiments, the method further comprises scheduling, by the industrial control system, a maintenance activity for the at least one component with the one or more anomalies determined therefor.

In one or more embodiments, the method further comprises implementing, by the server, an authentication key corresponding to each of the assigned unique identifier of one or more of the multiple operating parameters for authentication thereof. Herein, the authentication key is valid for a limited time period. The method further comprises authenticating, by the server, the assigned unique identifier of one or more of the multiple operating parameters at predefined regular time intervals.

In one or more embodiments, the one or more operating parameters comprise at least one of: feed value, feed time, motor operating power value, motor operating time value, separator value, separator time, and wherein the one or more output values comprise at least one of: gate value, gate time, reject value, reject time, bin weight value, bin weight time.

In one or more embodiments, the method further comprises training the trained machine learning function using the data related to multiple operating parameters with corresponding one or more output values for each of the one or more milling machines.

In one or more embodiments, the industrial control system is one of a distributed industrial control system (DCS) or a programmable logic controller (PLC).

The object of the present disclosure is also achieved by a system for optimizing operations of one or more milling machines in an industrial setting utilizing an industrial control system. The system comprises a one or more processing units, and a memory communicatively coupled to the one or more processing units, the memory comprising an optimization module configured to perform the method steps described above.

The object of the present disclosure is further achieved by a computer-program product, having computer-readable instructions stored therein, that when executed by a processing unit, cause the processing unit to perform the method steps described above.

The object of the present disclosure is further achieved by a computer readable medium on which program code sections of a computer program are saved, the program code sections being loadable into and/or executable in a system to make the system execute the method steps described above when the program code sections are executed in the system.

Still other aspects, features, and advantages of the invention are readily apparent from the following detailed description, simply by illustrating a number of particular embodiments and implementations, including the best mode contemplated for carrying out the invention. The invention is also capable of other and different embodiments, and its several details can be modified in various obvious respects, all without departing from the scope of the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.

A more complete appreciation of the present disclosure and many of the attendant aspects thereof will be readily obtained as the same becomes better understood by reference to the following description when considered in connection with the accompanying drawings:

FIG 1 is a schematic block diagram representation of a system for optimizing operations of one or more milling machines in an industrial setting, in accordance with an embodiment of the present disclosure;

FIG 2 is a schematic block diagram representation of a computing device optimizing operations of one or more milling machines in an industrial setting, in accordance with an embodiment of the present disclosure;

FIG 3 is a block diagram illustration of a licensing module for process of securing data between license issuer and an end user application, in accordance with an embodiment of the present disclosure;

FIG 4 is a flowchart depicting steps of a process for generating and validating a license, in accordance with an embodiment of the present disclosure;

FIG 5 is a flowchart depicting steps of a process for validating an expiry of a license, in accordance with an embodiment of the present disclosure;

FIG 6 is a flowchart depicting steps of a process for providing data analysis and predictive maintenance, in accordance with an embodiment of the present disclosure;

FIG 7 is a flowchart illustrating a method for optimizing operations of the one or more milling machines in an industrial setting utilizing the industrial control system, in accordance with an embodiment of the present disclosure. Examples of a method, a system, a computer-program product and a computer readable medium for optimizing operations of one or more milling machines in an industrial setting utilizing an industrial control system are disclosed herein. Various embodiments are described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention. It is apparent, however, to one skilled in the art that the embodiments of the invention may be practiced without these specific details or with an equivalent arrangement. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the embodiments of the invention.

The term “industrial setting,” as used herein, may refer to any industrial facility such as a factory or any type of manufacturing unit, which may house various industrial machines. Herein, the term "industrial setting" refers to the whole of machineries or parts thereof which cooperate to allow a production process of any kind to be carried out. The industrial setting may employ an industrial control system for managing operations of various machines therein. The term "industrial control system," as used herein, generally refers to any type or form of system and/or mechanism that controls and/or performs manufacturing, service, and/or production operations. The industrial control system may encompass several types of control systems used in industrial production, including process control systems (PCS), supervisory control and data acquisition (SCADA) systems, distributed control systems (DCS), and other smaller control system configurations such as programmable logic controllers (PLC) often found in the industrial sectors and critical infrastructures. In implementations, based on information received from remote stations, automated or operator- driven supervisory commands can be transmitted to the devices (which may include remote station control devices, field devices, and the like) of an industrial control system. FIG 1 is a schematic block diagram representation of a system 100 for implementation in an industrial setting. In present implementation, the system 100 may reside on and may be executed by a computer, which may be connected to a network (e.g., the internet or a local area network). Examples of computer may include, but are not limited to, a personal computer(s), a laptop computer(s), mobile computing device(s), a server computer, a series of server computers, a mainframe computer(s), or a computing cloud(s). In some implementations, each of the aforementioned may be generally described as a computing device. In certain implementations, a computing device may be a physical or virtual device. In many implementations, a computing device may be any device capable of performing operations, such as a dedicated processor, a portion of a processor, a virtual processor, a portion of a virtual processor, portion of a virtual device, or a virtual device. In some implementations, a processor may be a physical processor or a virtual processor. In some implementations, a virtual processor may correspond to one or more parts of one or more physical processors. In some implementations, the instructions/logic may be distributed and executed across one or more processors, virtual or physical, to execute the instructions/logic.

In some implementations, the instruction sets and subroutines of the system 100, which may be stored on storage device, such as storage device coupled to computer may be executed by one or more processors and one or more memory architectures included within computer. In some implementations, storage device may include but is not limited to: a hard disk drive; a flash drive, a tape drive; an optical drive; a RAID array (or other array); a random-access memory (RAM); and a read-only memory (ROM).

In some implementations, network may be connected to one or more secondary networks (e.g., network), examples of which may include but are not limited to: a local area network; a wide area network; or an intranet, for example.

In some implementations, computer may include a data store, such as a database (e.g., relational database, object-oriented database, triplestore database, etc.) and may be located within any suitable memory location, such as storage device coupled to computer. In some implementations, data, metadata, information, etc. described throughout the present disclosure may be stored in the data store. In some implementations, computer may utilize any known database management system such as, but not limited to, DB2, in order to provide multi-user access to one or more databases, such as the above noted relational database. In some implementations, the data store may also be a custom database, such as, for example, a flat file database or an XML database. In some implementations, any other form(s) of a data storage structure and/or organization may also be used. In some implementations, the system 100 may be a component of the data store, a standalone application that interfaces with the above noted data store and/or an applet / application that is accessed via client applications. In some implementations, the above noted data store may be, in whole or in part, distributed in a cloud computing topology. In this way, computer and storage device may refer to multiple devices, which may also be distributed throughout the network.

In some implementations, computer may execute application for optimizing operations of one or more milling machines in an industrial setting utilizing an industrial control system, as described above. In some implementations, the system 100 and/or application may be accessed via one or more of client applications. In some implementations, the system 100 may be a standalone application, or may be an applet / application / script / extension that may interact with and/or be executed within application a component of application and/or one or more of client applications. In some implementations, application may be a standalone application, or may be an applet / application / script / extension that may interact with and/or be executed within the system 100, a component of the system 100, and/or one or more of client applications. In some implementations, one or more of client applications may be a standalone application, or may be an applet / application / script / extension that may interact with and/or be executed within and/or be a component of the system 100 and/or application. Examples of client applications may include, but are not limited to, a standard and/or mobile web browser, an email application (e.g., an email client application), a textual and/or a graphical user interface, a customized web browser, a plugin, an Application Programming Interface (API), or a custom application. The instruction sets and subroutines of client applications which may be stored on storage devices coupled to user devices may be executed by one or more processors and one or more memory architectures incorporated into user devices.

In some implementations, one or more of storage devices may include but are not limited to: hard disk drives; flash drives, tape drives; optical drives; RAID arrays; random access memories (RAM); and read-only memories (ROM). Examples of user devices (and/or computer) may include, but are not limited to, a personal computer, a laptop computer, a smart/data-enabled, cellular phone, a notebook computer, a tablet, a server, a television, a smart television, a media capturing device, and a dedicated network device.

In some implementations, one or more of client applications may be configured to effectuate some or all of the functionality of the system 100 (and vice versa). Accordingly, in some implementations, the system 100 may be a purely server-side application, a purely client-side application, or a hybrid server-side / client-side application that is cooperatively executed by one or more of client applications and/or the system 100.

In some implementations, one or more of client applications may be configured to effectuate some or all of the functionality of application (and vice versa). Accordingly, in some implementations, application may be a purely server-side application, a purely client-side application, or a hybrid server-side / client-side application that is cooperatively executed by one or more of client applications and/or application. As one or more of client applications the system 100, and application taken singly or in any combination, may effectuate some or all of the same functionality, any description of effectuating such functionality via one or more of client applications the system 100, application or combination thereof, and any described interaction(s) between one or more of client applications the system 100, application or combination thereof to effectuate such functionality, should be taken as an example only and not to limit the scope of the disclosure. In some implementations, one or more of users may access computer and the system 100 (e.g., using one or more of user devices) directly through network or through secondary network. Further, computer may be connected to network through secondary network with phantom link line. The system 100 may include one or more user interfaces, such as browsers and textual or graphical user interfaces, through which users may access the system 100.

In some implementations, the various user devices may be directly or indirectly coupled to communication network, such as communication network and communication network hereinafter simply referred to as network and network respectively. For example, user device may be directly coupled to network via a hardwired network connection. Alternatively, user device may be wirelessly coupled to network via wireless communication channel established between user device and wireless access point (i.e., WAP) which in turn may be directly coupled to network. WAP may be, for example, an IEEE 802.11a, 802.11b, 802.11g, 802.1 lac, 802.11ae, Wi-Fi®, RFID, and/or BluetoothTM (including BluetoothTM L_OW Energy) device that is capable of establishing wireless communication channel between user device and WAP. In other examples, user device may be wirelessly coupled to network via wireless communication channel established between user device and cellular network / bridge which may be directly coupled to network. User devices may execute an operating system, examples of which may include but are not limited to, Android®, Apple® iOS®, Mac® OS X®; Red Hat® Linux®, or a custom operating system.

In some implementations, some or all of the IEEE 802.1 lx specifications may use Ethernet protocol and carrier sense multiple access with collision avoidance (i.e., CSMA/CA) for path sharing. The various 802.1 lx specifications may use phase-shift keying (i.e., PSK) modulation or complementary code keying (i.e., CCK) modulation, for example, BluetoothTM (including BluetoothTM L_OW E_nergy) _{s a} telecommunications industry specification that allows, e.g., mobile phones, computers, smart phones, and other electronic devices to be interconnected using a short-range wireless connection. Other forms of interconnection (e.g., Near Field Communication (NFC)) may also be used. FIG 2 illustrates a schematic diagram of a computing device 200 for optimizing operations of one or more milling machines in an industrial setting, in accordance with an embodiment of the present disclosure. In an example, the computing device 200 may be a computer-program product 200 programmed for performing the said purpose. In another example, the computing device 200 may be a computer readable medium on which program code sections of a computer program are saved, the program code sections being loadable into and/or executable in a system to make the system execute the steps for performing the said purpose. The computing device 200 may be incorporated in one or more physical packages (e.g., chips). By way of example, a physical package includes an arrangement of one or more materials, components, and/or wires on a structural assembly (e.g., a baseboard) to provide one or more characteristics such as physical strength, conservation of size, and/or limitation of electrical interaction. It is contemplated that in certain embodiments the computing device can be implemented in a single chip. The system 100 of the present disclosure as discussed in the preceding paragraphs may include or be embodied in the computing device 200. It may be appreciated that the two systems 100 and 200 (and the corresponding components/elements) may be equivalent for the purposes of the present disclosure.

In one embodiment, the computing device 200 includes a communication mechanism such as a bus 202 for passing information among the components of the computing device 200. The computing device 200 includes one or more processing units 204 and a memory unit 206. Generally, the memory unit 206 is communicatively coupled to the one or more processing units 204. Hereinafter, the one or more processing units 204 are simply referred to as processor 204 and the memory unit 206 is simply referred to as memory 206. Herein, in particular, the processor 204 has connectivity to the bus 202 to execute instructions and process information stored in the memory 206. The processor 204 may include one or more processing cores with each core configured to perform independently. A multi-core processor enables multiprocessing within a single physical package. Examples of a multi-core processor include two, four, eight, or greater numbers of processing cores. Alternatively, or in addition, the processor 204 may include one or more microprocessors configured in tandem via the bus 202 to enable independent execution of instructions, pipelining, and multithreading. The processor 204 may also be accompanied with one or more specialized components to perform certain processing functions and tasks such as one or more digital signal processors (DSP) 208, or one or more application- specific integrated circuits (ASIC) 210. A DSP 208 typically is configured to process real-world signals (e.g., sound) in real time independently of the processor 204. Similarly, an ASIC 210 can be configured to performed specialized functions not easily performed by a general purposed processor. Other specialized components to aid in performing the inventive functions described herein include one or more field programmable gate arrays (FPGA) (not shown), one or more controllers (not shown), or one or more other special-purpose computer chips.

As used herein, the term "processor" refers to a computational element that is operable to respond to and processes instructions that drive the system. Optionally, the processor includes, but is not limited to, a microprocessor, a microcontroller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or any other type of processing circuit. Furthermore, the term "processor" may refer to one or more individual processors, processing devices and various elements associated with a processing device that may be shared by other processing devices. Additionally, the one or more individual processors, processing devices and elements are arranged in various architectures for responding to and processing the instructions that drive the system.

The processor 204 and accompanying components have connectivity to the memory 206 via the bus 202. The memory 206 includes both dynamic memory (e.g., RAM, magnetic disk, writable optical disk, etc.) and static memory (e.g., ROM, CD-ROM, etc.) for storing executable instructions that when executed perform the method steps described herein for optimizing operations of one or more milling machines in an industrial setting utilizing an industrial control system. In particular, the memory 206 includes a module arrangement 212 to perform steps for optimizing operations of one or more milling machines in an industrial setting utilizing an industrial control system. The memory 206 also stores the data associated with or generated by the execution of the inventive steps. Herein, the memory 206 may be volatile memory and/or non-volatile memory. The memory 206 may be coupled for communication with the processing unit 204. The processing unit 204 may execute instructions and/or code stored in the memory 206. A variety of computer- readable storage media may be stored in and accessed from the memory 206. The memory 206 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 removable media drive for handling compact disks, digital video disks, diskettes, magnetic tape cartridges, memory cards, and the like.

It is to be understood that the system and methods described herein may be implemented in various forms of hardware, software, firmware, special purpose processors, or a combination thereof. One or more of the present embodiments may 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 description, a computer- usable or computer-readable medium can be any apparatus that can contain, store, communicate, propagate, 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, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation mediums in and of themselves as signal carriers are not included in the definition of physical computer-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 digital versatile disc (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.

The various modules of the system 100 as described hereinafter may be comprised (stored) in the memory 206 (as described in the preceding paragraphs) to enable the system 100 for optimizing operations of one or more milling machines in an industrial setting utilizing an industrial control system.

Referring back to FIG 1, as illustrated, the system 100 is implemented in an industrial setting, in accordance with one or more embodiments of the present disclosure. As shown, the system 100 is associated with one or more industrial facilities, such as an industrial facility 101A and an industrial facility 101N, indicating that any number of industrial facilities may be associated with the system 100. Each of the industrial facility 101A-101N includes one or more industrial machines. The present disclosure has been described in terms of the industrial machines being milling machines (with the two terms being interchangeably used herein), with each of the industrial facility 101A-101N including a milling machine 102A and a milling machine 102N, indicating that any number of milling machines may be present each of the industrial facility 101A-101N. Further, each of the industrial facility 101A-101N includes an industrial control system, for example the industrial facility 101A may include an industrial control system 104A and the industrial facility 101N may include an industrial control system 104N. The industrial control systems 104A-104N may be on-premise cloud agents for storing data as received from the industrial devices 102A-102N in the respective industrial facility 101A-101N.

The industrial control systems 104A-104N may comprise one or more automation systems operating within the respective industrial facilities. Exemplary automation systems can include, but are not limited to, batch control systems (e.g., mixing systems), continuous control systems (e.g., PID control systems), or discrete control systems. The industrial control systems 104A-104N may include devices such as industrial controllers (e.g., programmable logic controllers or other types of programmable automation controllers such as SCADA, DCS and the like); field devices such as sensors and meters; motor drives; operator interfaces (e.g., human-machine interfaces, industrial monitors, graphic terminals, message displays, etc.); industrial robots, barcode markers and readers; vision system devices (e.g., vision cameras); smart welders; or other such industrial devices. Exemplary automation systems may include one or more industrial controllers that facilitate monitoring and control of their respective processes. The industrial controllers exchange data with the field devices using native hardwired I/O or via a plant network such as Ethernet/IP, Data Highway Plus, ControlNet, Devicenet, or the like. A given industrial controller typically receives any combination of digital or analog signals from the field devices indicating a current state of the devices and their associated processes (e.g., temperature, position, part presence or absence, fluid level, etc.), and executes a user-defined control program that performs automated decision-making for the controlled processes based on the received signals. The industrial controller then outputs appropriate digital and/or analog control signalling to the field devices in accordance with the decisions made by the control program. These outputs can include device actuation signals, temperature or position control signals, operational commands to a machining or material handling robot, mixer control signals, motion control signals, and the like.

In the present embodiments, the industrial control systems 104A-104N may be one of a distributed industrial control system (DCS) or a programmable logic controller (PLC). The industrial control systems 104A-104N may be configured for data acquisition, packaging, and transmission of industrial data generated by the industrial machines 102A-102N. The industrial control systems 104A-104N acts as a generic gateway to collect data items from the various industrial machines 102A-102N of one or more industrial facilities 101A-101N, and packages the collected data according to a generic, uniform data packaging scheme used to move the on-premise data to a server 108. In particular, the data from the industrial control systems 104A-104N is provided to the server 108 (such as, cloud computing server) over a communication network 106.

The server 108 is configured to receive, from the industrial control system 104A-104N, data related to multiple operating parameters with corresponding one or more output values for each of the one or more milling machines 102A-102N. The said data may be obtained from multiple sensors associated with each of the one or more milling machines 102A-102N in the industrial facilities 101A-101N, and therefrom collected by the industrial control systems 104A-104N therein. For the example of milling machines 102A-102N, the one or more operating parameters comprise at least one of: feed value, feed time, motor operating power value, motor operating time value, separator value, separator time, and wherein the one or more output values comprise at least one of: gate value, gate time, reject value, reject time, bin weight value, bin weight time. Herein, “feed value” and “feed time” are inputs to the milling machines 102A-102N and define rate of feed input thereto. Similarly, “motor operating power value” and “motor operating time value” are inputs to motor of the milling machines 102A-102N and define power delivery thereof. Furthermore, similarly, “separator value” and “separator time” are inputs to the milling machines 102A-102N and define rate of separation therein. Also, gate value, gate time, reject value, reject time, bin weight value, bin weight time are outputs of the milling machines 102A-102N, with objective being to increase the gate value (corresponding to the gate time) and reduce the reject value (corresponding to the reject time) and bin weight value (corresponding to the bin weight time). For this purpose, the said inputs to the milling machines 102A-102N, namely, feed value, feed time, motor operating power value, motor operating time value, separator value, separator time need to be optimized, and thereby optimized values therefor may need to be determined.

Herein, the server 108 includes multiple modules, with each module comprising one or more units being responsible for performing at least one of the discrete operations of the system 100, and the various modules coordinating with each other to achieve the functionality of optimizing operations of one or more milling machines 102A-102N in the industrial facilities 101A-101N. The definition of the term “module” as referred to herein is to be understood as constituting hardware circuitry such as a CCD, CMOS, SoC, AISC, FPGA, a processor or microprocessor (a controller) configured for a certain desired functionality, or a communication module containing hardware such as transmitter, receiver or transceiver, or a non-transitory medium comprising machine executable code that is loaded into and executed by hardware for operation, and do not constitute software per se. In addition, the controllers shown herein are hardware that are comprised of components, for example, a processor or microprocessor configured for operation by the algorithms shown in the flowcharts and described herein. Referring to FIG 1 in detail, as illustrated, the server 108 includes a database module 110 in communication with the industrial control systems 104A-104N. In the present embodiments, the database module 110 may be one or a combination of an on-premise storage agent and a cloud-based storage agent. Further, in the present embodiments, with the industrial control systems 104A-104N being one of a distributed industrial control system (DCS) or a programmable logic controller (PLC), the database module 110 implements one of an open platform communications united architecture (OPC UA) protocol or an open platform communications data access (OPC DA) protocol to be disposed in communication with the industrial control systems 104A-104N.

In the server 108, the database module 110 provides a database to collect data related to multiple operating parameters with corresponding one or more output values for each of the one or more milling machines 102A-102N. The term "database" as used herein refers to an organized body of digital information regardless of the manner in which the data or the organized body thereof is represented. Optionally, the database is implemented using hardware, software, firmware and/or any combination thereof. For example, the organized body of related data is in a form of a table, a map, a grid, a packet, a datagram, a file, a document, a list or in any other form. The database includes any data storage software and systems, such as, for example, a relational database like IBM DB2 and Oracle 9. Optionally, the database is used interchangeably herein as database management system, as is common in the art. Furthermore, the database management system refers to the software program for creating and managing one or more databases. Optionally, the database, when in operation, supports relational operations, regardless of whether it enforces strict adherence to the relational model, as understood by those of ordinary skill in the art. Additionally, the information is stored in the cells of the database.

Further, as illustrated, the server 108 includes a licensing module 112. Herein, the licensing module 112 is configured to assign one or more of the multiple operating parameters, as received for each of the one or more milling machines 102A-102N, a unique identifier. The licensing module 112 is further configured to authenticate the assigned unique identifier of one or more of the multiple operating parameters. As used herein, the term “unique identifier” includes anything used to identify a particular operating parameter of a particular milling machine of the one or more milling machines 102A-102N. The licensing module 112 may be employed such that a customer may be able to provide only those operating parameters, which may ultimately be used to optimize the milling operation in the corresponding industrial facility 101A-101N, for which the customer may have procured license. So, for example, if the customer may have procured license for optimizing the milling operation based only on the said feed value of the milling machines 102A-102N in a given industrial facility 101A, therefore when the data related to multiple operating parameters is sent to the server 108, only data corresponding to the said feed value is implemented for optimizing operations of the milling machines 102A-102N in the industrial facility 101A. If the customer may also wish to use, say, motor operating power value for even better optimization of the milling machines 102A-102N in the industrial facility 101 A, with the present licensing module 112, the customer would be required to procure corresponding license therefor. It may be appreciated that with the disclosed license module 112, the present system 100 is capable of acquiring data from any operating machine, irrespective of OEM’s (Original Equipment Manufacturer).

FIG 3 is a block diagram illustration of a scheme 300 for the licensing module 112 for process of securing data between license issuer and an end user application, in accordance with an embodiment of the present invention. At block 302, an end user application is configured to generate a unique identification (UID) and is encrypted using the public key of the license issuer. At block 304, the encrypted UID is shared with the license issuer along the with private key and purchase requirement. At block 306, the UID is verified by the license issuer using the private key of the end user application that is shared with the license user. The license issuer modularizes a set of permissions to be issued to the end user application based on the requirement. At block 308, the set of licensing permissions are encrypted and transmitted to the end user application. At block 310, the end user application installs the license pack, validates the license pack and initiates the application with licensed features. FIG 4 is a flowchart 400 depicting steps of a process for generating and validating a license between an end user 400a and a license issuer 400b, in accordance with an embodiment of the present invention. At step 402, a unique identifier (UID) is generated. The UID uniquely identifies a particular user device. At step 404, the UID for a particular user is encrypted with an encryption algorithm such as Base64 and Minimum Distance Separable (MDS) algorithm. The examples for encryption and decryption are used herein only as an exemplary embodiment. A person skilled in the art would understand that any cryptography algorithm known in the art may be used for this purpose. At step 406, the encrypted UID is transmitted to the license issuer along with requirements (as represented by block 401) of the user. The requirements 401 may include what particular industrial plant the user is willing to access, which particular vertical of information is user interested in such as production, management, sales, and the like, what particular services is the user willing to access, and so forth. At step 408, the encrypted UID along with the user requirements can received at the issuer end and stored in a license database. At step 410, the UID and the requirements are associated with a markup language file for example an Extensible Markup Language (XML) file to generate a license pack. At step 412, the license pack is paired with RS A algorithm and is subsequently digitally signed by the license issuer. At step 414, the license pack is encrypted with Base64. At step 416, the final license pack is generated and stored in the license database. Further, the generated license pack is transmitted to the end user device in response to the request. At step 418, the license pack is received, installed and validated using the UID, and a decision is made whether the license pack is valid or not. If the license pack is successfully validated, step 420 is executed and the application is started at the user end with the requested features. If the license pack is not successfully validated (rejected), step 422 is executed and request for a new license pack is raised from the end user device.

In the present embodiments, the licensing module 112, in the server 108, implements an authentication key corresponding to each of the assigned unique identifier of one or more of the multiple operating parameters for authentication thereof. Herein, the authentication key is valid for a limited time period. The licensing module 112, in the server 108, is further configured to authenticate the assigned unique identifier of one or more of the multiple operating parameters at predefined regular time intervals. This way a given industrial facility, say industrial facility 101A, may not be able to implement expired licenses for optimization of the operations of the milling machines 102A-102N therein.

The license module 112 provides tools and method for licensing software. The tools and method ensure that the user end application license is being complied with various algorithms which make it tamper proof and portable. According to one aspect of the licensing module 112, laptop software licenses are electronically issued as digital certificates that can be distributed in one-to-one correlation with client and can be traced to an issuing authority. Another aspect of the licensing module 112 is to generate a unique authentication key at client side using license generator and send to issuer with requirements for license. License issuer generate the license pack according to requirements and send it back to client where it gets verified with to unique key. FIGs 5-7 explain about workflow of generating license, encoding, decoding, securing and validating.

FIG 5 is a flowchart 500 depicting steps of a process for validating an expiry of a license, in accordance with another embodiment of the present disclosure. The following steps are executed to determine whether a particular license pack is valid or not. At step 502, the license pack is validated and a decision is made whether license pack has expired or not. If the license pack has expired, step 504 is executed to request a new license from the license issuer. At step 506, a new license pack is generated by the license issuer and encrypted with an encryption key. At step 508, the upgrade and/or renewed license pack is transmitted back to the end user device. At step 510, the received license pack is verified for the particular UID. If the license pack has not expired, step 512 is executed and the application is run at the user end device without any interruption.

Referring back to FIG 1, as illustrated, the server 108 further includes an Artificial Intelligence (Al) module 114. The Al module 114 implements a machine learning function, in the server 108, to determine an optimal value for each of one or more of the multiple operating parameters having the corresponding assigned unique identifier authenticated thereby, based on the data related to multiple operating parameters with corresponding one or more output values for each of the one or more milling machines 102A-102N, such that the corresponding determined optimal value when implemented for each of one or more of the multiple operating parameters results in one or more optimized output values for the one or more milling machines 102A-102N. In embodiments of the present disclosure, the trained machine learning function, in the Al module 114, may only utilize the one or more of the multiple operating parameters with the assigned unique identifier having been authenticated by the licensing module. The server 108 is further configured to instruct the industrial control systems 104A-104N to configure the one or more milling machines 102A-102N based on the corresponding determined optimal value for each of one or more of the multiple operating parameters thereof. The use of machine learning in industrial setting may be contemplated by a person skilled in the art, and this specific machine learning algorithms have not been explained herein for the brevity of the present disclosure.

In one or more embodiments, the Al module 114 is configured to train the trained machine learning function using the data related to multiple operating parameters with corresponding one or more output values for each of the one or more milling machines. For training the machine learning function for purposes of the present disclosure, the Al module 114 may divide the collected data into a first data portion and a second data portion. The collected data may be divided with the first data portion being approximately 70% of the collected data and the second data portion being approximately remaining 30% of the collected data. The Al module 114 may train the machine learning function based on the first data portion. The Al module 114 may then predict, using the trained machine learning function, one or more output values for the one or more milling machines 102A-102N for a test set of operating parameters, with the said test set of operating parameters being equivalent to one of the set of operating parameters from the second data portion having corresponding one or more output values available therewith. The Al module 114 may further determine a difference between the predicted one or more output values for the operating parameters with the corresponding one or more output values for the equivalent set of operating parameters from the second data portion. The Al module 114 may then utilize the trained machine learning function as a trained model if the determined difference is lesser than a predefined threshold. Herein, the Al module 114 may further configured to clean the collected data to filter null values therefrom. In one or more embodiments, the Al module 114 is configured to train the machine learning function using linear regression of the data in the first data portion.

Further, as illustrated in FIG 1, the server 108 includes a data processing module 116 to provide data analysis and predictive maintenance for the one or more milling machines 102A-102N in the industrial facilities 101A-101N. Herein, the data processing module 116 is configured to determine using the trained machine learning function of the Al module 114, one or more anomalies in at least one component of the one or more milling machines 102A- 102N if the one or more output values for each of the one or more milling machines 102A- 102N substantially differs from the one or more optimized output values therefor, post configuring the one or more milling machines 102A-102N based on the corresponding determined optimal value for each of one or more of the multiple operating parameters thereof. That is, even after the optimized output values for milling operations, as determined, have been applied to the corresponding one or more milling machines 102A-102N and the expected one or more optimized output values for the one or more milling machines 102A- 102N may still not be achieved, it may be concluded that there is possibly a fault with one of the components in the one or more milling machines 102A-102N which may not have achieved the expected one or more optimized output values therefor. In such case, the data processing module 116 is configured to schedule, by the corresponding industrial control system 104A-104N, a maintenance activity for the at least one component with the one or more anomalies determined therefor.

FIG 6 is a flowchart 600 depicting steps of a process for providing data analysis and predictive maintenance, in accordance with an embodiment of the present invention. At step 602, connection between industrial devices such as physical devices and/or sensors associated with the industrial devices and PLC/DCS is established. The data from the sensors of the industrial devices may be stored in an on-premise cloud storage, or directly transmitted to the PLC/DCS for further processing. At step 604, a connection is established between PLC/DCS and an operating station in the industrial facility. At step 606, one or more modules of the system such as data processing module 116. In an example, the data processing module 116 is an executable file developed on any programming language platform. For example, the data processing module 116 is an “.exe” file developed on C# Programming Language platform. The software would be installed on PC which is connected to plant network to acquire data through OPC (Open Platform Communication) protocol or any other data communication protocols known in the art. The software also acts as a gateway to store data on local cloud and on dedicated web cloud. The data processing module 116 also includes features such as SMS Service for Alarm/ Alert and electronic mail service for reporting. At step 608, the license module 112 is configured to receive and validate requests as received in the system 100 from a given UID associated with a particular user device. Based on the UID, the data presentation and features of the system 100 are selectively provided to the particular user device. At step 610, a connection with the industrial control systems 104A-104N is established via OPC protocol. At step 612, an access to tags associated with different industrial control systems 104A-104N is provided. At step 614, tags or parameters are configured accordingly to perform one or more actions based on the analysed data. For example, the tags may be configured for alerts or alarms, for announcements, or for generating email reports and the like. At step 616, the data is stored in a local storage or webbased cloud databases, such as the database module 110.

Once the packaged data has been provided to the server 108, the data is processed according to the functionalities of each of the modules 110, 112, 114, 116 in the server 108, the processed data may be retrieved by a user management module 118 therein. The user management module 118 may allow user devices 120A-120N in the system 100 to view the processed data from the remote server 108. The user management module 118 may manage the username, password, etc. for enabling access for the user (customer) to, for example, define operating parameters with corresponding one or more output values for each of the one or more milling machines 102A-102N based on the available licenses, as may be confirmed by the licensing module 112. Other than managing basic user information like access credentials, the user management module 118 can also manage additional user information specific to individual applications via the means of extensible user-defined schemas. The present disclosure further provides a method for optimizing operations of one or more milling machines 102A-102N in an industrial setting. FIG 7 is a flowchart 700 illustrating steps involved in a method for optimizing operations of the one or more milling machines 102A-102N in an industrial setting utilizing the industrial control system 104A-104N, in accordance with an embodiment of the present disclosure. At step 702, the method includes receiving, from the industrial control system 104A-104N, by the database module 110 of the server 108, data related to multiple operating parameters with corresponding one or more output values for each of the one or more milling machines 102A-102N, with one or more of the multiple operating parameters being assigned a unique identifier. At step 704, the method includes authenticating, by the licensing module 112 of the server 108, the assigned unique identifier of one or more of the multiple operating parameters. At step 706, the method includes determining, by the Al module 114 of the server 108, using the trained machine learning function, an optimal value for each of one or more of the multiple operating parameters having the corresponding assigned unique identifier authenticated thereby, based on the data related to multiple operating parameters with corresponding one or more output values for each of the one or more milling machines 102A-102N, such that the corresponding determined optimal value when implemented for each of one or more of the multiple operating parameters results in one or more optimized output values for the one or more milling machines 102A-102N. At step 708, the method includes instructing, by the server 108, the industrial control system 104A-104N, to configure the one or more milling machines 102A-102N based on the corresponding determined optimal value for each of one or more of the multiple operating parameters thereof.

In one or more embodiments, the method further comprises determining, by the server 108, using the trained machine learning function in the Al module 114 thereof, one or more anomalies in at least one component of the one or more milling machines 102A-102N if the one or more output values for each of the one or more milling machines 102A-102N substantially differs from the one or more optimized output values therefor, post configuring the one or more milling machines 102A-102N based on the corresponding determined optimal value for each of one or more of the multiple operating parameters thereof. In one or more embodiments, the method further comprises scheduling, by the industrial control system 104A-104N, a maintenance activity for the at least one component with the one or more anomalies determined therefor.

In one or more embodiments, the method further comprises implementing, by the server 108, an authentication key corresponding to each of the assigned unique identifier of one or more of the multiple operating parameters for authentication thereof. Herein, the authentication key is valid for a limited time period. The method further comprises authenticating, by the licensing module 112 of the server 108, the assigned unique identifier of one or more of the multiple operating parameters at predefined regular time intervals.

In one or more embodiments, the method further comprises training the trained machine learning function using the data related to multiple operating parameters with corresponding one or more output values for each of the one or more milling machines 102A-102N.

In one or more embodiments, the industrial control system 104A-104N is one of a distributed industrial control system (DCS) or a programmable logic controller (PLC).

The present system 100 and method allows for acquiring data from any operating machine, irrespective of OEM’s (Original Equipment Manufacturer). The operation and maintenance personnel of any industrial plant are presented with powerful yet simplistic data statics that aids in quick and accurate decision making and hence increases efficiency of operations in the industrial facility. Moreover, one or more actions can be performed based on analysis of the data, such as scheduling of maintenance activities, generating notifications regarding activities in the industrial facility, broadcasting announcements in the industrial facility in case of an emergency and the like to run the operation in the industrial facility in an efficient manner. Furthermore, another advantage is to create a seamless interface and ease of handling setup, mimicking the present IT software’s and applications to improve acceptability. Moreover, the invention aims at providing a single platform for any interface in the industrial plant, for dashboarding, production data, intercommunication analytics and diagnostics. All such verticals can be provided using a single application to the users.

Furthermore, an advantage of the present disclosure is to maintain industrial plants and optimize the processes therein in an efficient manner, integration with other facilities in the industrial plant ensures that an operation is carried out in the industrial plant in an efficient manner. Other advantages of the disclosed system 100 and method includes reduced storage requirements on device, easy to connect with varied industrial devices and easy implementation across verticals, geographical locations and protocols used, seamless flow of information among various systems, thus bringing data transparency in plant operations, Al powered predictive maintenance and plant data analytics, loosely coupled applications, interface and protocol independent solution, availability of both cloud and on-premise solutions.

According to various embodiments of the present disclosure, at least a part of devices (e.g., modules or functions thereof) or methods (e.g., operations) according to various embodiments of the present disclosure may be implemented as instructions stored in a computer-readable storage non-transitory medium in the form of a programming module. In the case where the instructions are performed by at least one processor, the at least one processor may perform functions corresponding to the instructions. The computer-readable storage medium may be, for example, a memory. At least a part of the programming module may be implemented (e.g., executed) by the processor. At least a part of the programming module may include, for example, a module, a program, a routine, sets of instructions, or a process for performing at least one function. While the present disclosure has been described in detail with reference to certain embodiments, it should be appreciated that the present disclosure is not limited to those embodiments. In view of the present disclosure, many modifications and variations would be present themselves, to those skilled in the art without departing from the scope of the various embodiments of the present disclosure, as described herein. The scope of the present disclosure is, therefore, indicated by the following claims rather than by the foregoing description. All changes, modifications, and variations coming within the meaning and range of equivalency of the claims are to be considered within their scope.

Reference Numerals system 100 industrial facilities 101A-101N industrial devices 102A-102N industrial control systems 104A-104N communication network 106 server 108 database module 110 licensing module 112

Artificial Intelligence (Al) module 114 data proces sing module 116 computing device 200 bus 202 processing unit 204 memory unit 206

DSP 208

ASIC 210 module arrangement 212 scheme 300 block 302 block 304 block 306 block 308 block 310 flowchart 400 end user 400a license issuer 400b block 401 block 402 block 404 block 406 block 408 block 410 block 412 block 414 block 416 block 418 block 420 block 422 flowchart 500 step 502 step 504 step 506 step 508 step 510 step 512 flowchart 600 step 602 step 604 step 606 step 608 step 610 step 612 step 614 step 616 flowchart 700 step 702 step 704 step 706 step 708

Claims

PATENT CLAIMS

1. A method for optimizing operations of one or more milling machines in an industrial setting utilizing an industrial control system, the method comprising: receiving, from the industrial control system, by a server, data related to multiple operating parameters with corresponding one or more output values for each of the one or more milling machines, with one or more of the multiple operating parameters being assigned a unique identifier; authenticating, by the server, the assigned unique identifier of one or more of the multiple operating parameters; determining, by the server, using a trained machine learning function, an optimal value for each of one or more of the multiple operating parameters having the corresponding assigned unique identifier authenticated thereby, based on the data related to multiple operating parameters with corresponding one or more output values for each of the one or more milling machines, such that the corresponding determined optimal value when implemented for each of one or more of the multiple operating parameters results in one or more optimized output values for the one or more milling machines; and instructing, by the server, the industrial control system, to configure the one or more milling machines based on the corresponding determined optimal value for each of one or more of the multiple operating parameters thereof.

2. The method according to claim 1 further comprising determining, by the server, using the trained machine learning function, one or more anomalies in at least one component of the one or more milling machines if the one or more output values for each of the one or more milling machines substantially differs from the one or more optimized output values therefor, post configuring the one or more milling machines based on the corresponding determined optimal value for each of one or more of the multiple operating parameters thereof.

3. The method according to claim 2 further comprising scheduling, by the industrial control system, a maintenance activity for the at least one component with the one or more anomalies determined therefor.

4. The method according to claim 1 further comprising: implementing, by the server, an authentication key corresponding to each of the assigned unique identifier of one or more of the multiple operating parameters for authentication thereof, wherein the authentication key is valid for a limited time period; and authenticating, by the server, the assigned unique identifier of one or more of the multiple operating parameters at predefined regular time intervals.

5. The method according to claim 1, wherein the one or more operating parameters comprise at least one of: feed value, feed time, motor operating power value, motor operating time value, separator value, separator time, and wherein the one or more output values comprise at least one of: gate value, gate time, reject value, reject time, bin weight value, bin weight time.

6. The method according to claim 1 further comprising training the trained machine learning function using the data related to multiple operating parameters with corresponding one or more output values for each of the one or more milling machines.

7. The method according to claim 1, wherein the industrial control system is one of a distributed industrial control system (DCS) or a programmable logic controller (PLC).

8. A system for optimizing operations of one or more milling machines in an industrial setting utilizing an industrial control system, the system comprising: one or more processing units; and a memory communicatively coupled to the one or more processing units, the memory comprising an optimization module configured to perform the method steps as claimed in claims 1 to 7.

9. A computer-program product, having computer-readable instructions stored therein, that when executed by a processor, cause the processor to perform method steps according to any of the claims 1-7.

10. A computer readable medium on which program code sections of a computer program are saved, the program code sections being loadable into and/or executable in a system to make the system execute the method steps according to any of the claims 1 to 7 when the program code sections are executed in the system.