WO2021033019A1

WO2021033019A1 - System and method for remote monitoring and early detection of fault in valves

Info

Publication number: WO2021033019A1
Application number: PCT/IB2019/058719
Authority: WO
Inventors: Mahesh Joshi; S Mohandas; S Kathiresaperumal
Original assignee: L & T Valves Limited
Priority date: 2019-08-21
Filing date: 2019-10-12
Publication date: 2021-02-25

Abstract

The present invention relates to a system and method for remote monitoring and early detection of fault in valves. The system (110) incorporates sensors (214) operatively coupled to processor(s) (202) to monitor and evaluate health of the valves (402). The system (110) incorporates a health evaluation engine (212) to compare real-time health parameters of valve with standard health parameters to generate alert and warning signal. The system (110) also incorporates a machine learning model (218) that enables a fault prediction engine (216) to early detect fault in the valves (402) before actual occurrence of the fault and sends signals to the computing devices (108) of users to perform preventive, diagnostic, maintenance and replacement operation. The system (110) further incorporates a troubleshoot engine (220) configured with augmented reality to provide augmented reality based troubleshooting instructions to computing devices (108) of users to guide them to perform troubleshooting operation.

Description

SYSTEM AND METHOD FOR REMOTE MONITORING AND EARLY DETECTION OF FAULT IN VALVES

TECHNICAL FIELD

[0001] The present disclosure relates to the field of industrial automation system. More particularly, the present disclosure relates to a system and method for remote monitoring and early detection of fault in valves of an industrial unit in real time.

BACKGROUND

[0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.

[0003] In recent years, diagnosis and maintenance of smaller key components like valves in industrial machine/unit and those used in power plant, chemical, petroleum, and/or other processes have grown increasingly more complex with proliferation of these smaller components considering safety and reliability.

[0004] Due to the failure of any such smaller key component (valves), the industrial machine and the overall industrial process experience failure and downtime. Maintenance or replacement of the faulty valve that failed the industrial process is a time consuming and complex process. Firstly, the maintenance process of faulty valves requires time for identification of location of the faulty valve and then requires additional time for determining the reason for the fault which caused failure in the valve. Further, maintenance and troubleshooting of the faulty valve of the industrial unit requires additional time for maintenance of the valve and diagnosis of the fault.

[0005] This interruption of the industrial process due to the faulty valve leads to increased downtime, thereby causing delay in production and monetary losses. Failure of the valve during operation can also create hazardous operating conditions if the valves provide erroneous or inaccurate data to the industrial machines.

[0006] Inaccurate measurement of various performance parameters associated with the valve or any failure in measuring any critical performance parameter of the valve leads to inaccuracy and failure in determining the health of the valve, which greatly hampers the detection of faulty valve and maintenance of the faulty valve. [0007] Currently, the failure of the valves functionality is determined and captured at the site of the industrial machine location. This requires the end user and/or a technician dealing with the machine to visit the location for determination of health of the valve of the machine. Also, the technician is unable to plan proactively and/or take preventive steps for maintenance of the machine.

[0008] There is therefore a need in the art to provide an efficient system and method for remote monitoring and early detection or prediction of fault in the valves of industrial units for improved and reliable preventive, diagnostic and maintenance operation, which also provides augmented reality based service to guide users about installation, maintenance and troubleshooting of the valve in case of failure.

OBJECTS OF THE PRESENT DISCLOSURE

[0009] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.

[00010] It is an object of the present disclosure to provide a system and method for remote monitoring of valves of an industrial unit.

[00011] It is an object of the present disclosure to provide a system and method to monitor health of valves of an industrial unit, which generates an alert when the health deviates from standard operating parameters.

[00012] It is an object of the present disclosure to provide a system and method for early detection of fault in valves of an industrial unit.

[00013] It is an object of the present disclosure to provide a system and method that provides users with steps to be performed for troubleshooting of fault in the valves [00014] It is an object of the present disclosure to provide a system and method for remote monitoring of valves of an industrial unit to perform preventive, diagnostic, maintenance and replacement operation.

[00015] It is an object of the present disclosure to provide a system and method for remote monitoring and early detection of faults in valves of an industrial unit, which provides steps to be performed for troubleshooting of fault in the valves.

[00016] It is an object of the present disclosure to provide a system and method for remote monitoring and early detection of faults in valves of an industrial unit, which provides augmented reality based representation of steps to be performed for troubleshooting of fault in the valves. SUMMARY

[00017] The present disclosure relates to the field of industrial automation system. More particularly, the present disclosure relates to a system and method for remote monitoring and early detection of fault in valves of an industrial unit in real time.

[00018] An aspect of the present disclosure pertains to a system for remote monitoring and early detection of fault in valve of an industrial unit, the system may comprising: one or more sensors operatively coupled to one or more valves of the industrial unit, the one or more sensors may be configured to collect one or more health parameters of the one or more valves, wherein the one or more health parameters may comprise any or a combination of parameters selected from leakage, vibration, position, torque, cycles of operation, pressure, temperature and flow; a computing unit comprising one or more processors configured with a machine learning model to execute one or more instructions stored in a memory of the computing unit, the one or more processors may be operatively coupled to the one or more sensors and may be configured to receive the one or more health parameters from the one or more sensors; a health evaluation engine, which when executed by the one or more processors, may monitor each of the one or more valves and evaluate health associated with each of the one or more valves by comparing the received one or more health parameters with standard health parameters of the respective one or more valves such that when variance exists between the received one or more health parameters and corresponding standard health parameters, a corresponding set of first signal may be generated; and a fault prediction engine, which when executed by the one or more processors, may enable the machine learning model to early detect a fault in the one or more valves, and may further generate a corresponding set of second signal based on the early detected fault; wherein the generated set of second signal may be processed by the one or more processors so as to be represented on at least one computing device of users; and wherein the processed set of second signal may be transmitted to the at least one computing device of users to perform any or a combination of preventive, diagnostic, maintenance and replacement operation.

[00019] In an aspect, the system may comprise a troubleshooting engine, which when executed by the one or more processors, may send one or more augmented representation of instructions related to troubleshooting operation to a display of the at least one computing device of users to guide the users to perform troubleshoot operation.

[00020] In an aspect, the one or more augmented representation of instructions related to troubleshoot operation may comprise any or a combination of actions to be performed during maintenance and service of the respective one or more valve, installation guide for the one or more valves, and documents, drawings and blueprints related to the one or more valves, and wherein the one or more augmented representation may be stored in any or a combination of a database associated with the system and a cloud based server.

[00021] In an aspect, the one or more processors may be configured to prepare a training data set and a testing data set for the machine learning model to train the machine learning model to early detect the fault in the one or more valves, and wherein the training data set and the testing data base may be based on the received one or more health parameters of the respective one or more valves and the generated set of first signal.

[00022] In an aspect, the generated set of second signal may be in form of an alert that is visually or audibly presented on the at least one computing device of users.

[00023] In an aspect, the system may be configured to generate a health and diagnostic report for each of the one or more valves based on a data corresponding to the set of first signal and the set of second signal, and wherein the system may be configured to send the health and diagnostic report to the at least one computing device of users.

[00024] In an aspect, the at least one computing device may comprise any or a combination of a mobile phone, computer, laptop, server and cloud based server.

[00025] In aspect, the one or more sensors may comprise any or a combination of leakage sensor, vibration sensor, position sensor, torque sensor, pressure sensor, temperature sensor and flow sensor.

[00026] Another aspect of the present disclosure pertains to a method for performing remote monitoring and early detection of fault in valves of an industrial unit, the method comprising the steps of: collecting, from one or more sensors operatively coupled to one or more valves of the industrial unit, one or more health parameters of the one or more valves, wherein the one or more health parameters may comprise any or a combination of parameters selected from leakage, vibration, position, torque, cycles of operation, pressure, temperature and flow; receiving, at a computing unit comprising one or more processors configured with a machine learning model, the respective one or more health parameters from the one or more sensors; comparing, by the one or more processor, the received one or more health parameters with standard health parameters of the respective one or more valves; generating, by the one or more processor, a set of first signal when variance exists between the received one or more health parameters and corresponding standard health parameters; early detecting, by machine learning model of the one or more processors, fault in the one or more valves based on the received one or more health parameters of the respective one or more valves and the received set of first signal; and generating, by the one or more processors, a corresponding set of second signal as alert and warning to perform any or a combination of preventative, diagnostic, maintenance and replacement operation, wherein the set of second signal may be based on the predicted fault in the one or more valves.

[00027] In an aspect, the method may comprise the step of sending, by the one or more processors, one or more augmented representation of instructions to a display of the at least one computing device of users to guide the users to perform troubleshoot operation, and wherein the one or more augmented representation of instructions may comprise any or a combination of actions to be performed during maintenance and service of the respective one or more valve, installation guide for the one or more valves, and documents, drawings and blueprint related to the one or more valves.

[00028] Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components

[00029] Within the scope of this application it is expressly envisaged that the various aspects, embodiments, examples and alternatives set out in the preceding paragraphs, in the claims and/or in the following description and drawings, and in particular the individual features thereof, may be taken independently or in any combination. Features described in connection with one embodiment are applicable to all embodiments, unless such features are incompatible

BRIEF DESCRIPTION OF DRAWINGS

[00030] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. The diagrams are for illustration only, which thus is not a limitation of the present disclosure.

[00031] FIG. 1 illustrates exemplary implementation architecture of the proposed system, in accordance with an embodiment of the present invention

[00032] FIG. 2 illustrates an exemplary module diagram for the proposed system, in accordance with an embodiment of the present invention.

[00033] FIG. 3 illustrates an exemplary process flow diagram for monitoring and evaluation of health parameter valves of the industrial units, in accordance with an embodiment of the present invention. [00034] FIG. 4 illustrates exemplary high-level system architecture for monitoring the health information of the industrial unit by collecting information via sensors attached to valves of the industrial machines, in accordance with an embodiment of the present invention.

[00035] FIG. 5 illustrates an exemplary process flow diagram for early detection of fault in valves of the industrial units, in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION

[00036] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.

[00037] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.

[00038] Embodiments of the present invention may be provided as a computer program product, which may include a machine-readable storage medium tangibly embodying thereon instructions, which may be used to program a computer (or other electronic devices) to perform a process. The machine -readable medium may include, but is not limited to, fixed (hard) drives, magnetic tape, floppy diskettes, optical disks, compact disc read-only memories (CD-ROMs), and magneto-optical disks, semiconductor memories, such as ROMs, PROMs, random access memories (RAMs), programmable read-only memories (PROMs), erasable PROMs (EPROMs), electrically erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other type of media/machine -readable medium suitable for storing electronic instructions (e.g., computer programming code, such as software or firmware).

[00039] If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic. [00040] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.

[00041] The use of “including”, “comprising” or “having” and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof as well as additional items. The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item. Further, the use of terms “first”, “second”, and “third”, and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another

[00042] The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.

[00043] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all groups used in the appended claims.

[00044] Exemplary embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. These embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. Moreover, all statements herein reciting embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure).

[00045] The present disclosure relates to the field of industrial automation system. More particularly, the present disclosure relates to a system and method for remote monitoring and early detection of fault in valves of an industrial unit in real time.

[00046] According to an aspect, the present invention elaborates upon a system for remote monitoring and early detection of fault in valve of an industrial unit, the system including: one or more sensors operatively coupled to one or more valves of the industrial unit, the one or more sensors can be configured to collect one or more health parameters of the one or more valves, wherein the one or more health parameters can include any or a combination of parameters selected from leakage, vibration, position, torque, cycles of operation, pressure, temperature and flow; a computing unit including one or more processors configured with a machine learning model to execute one or more instructions stored in a memory of the computing unit, the one or more processors can be operatively coupled to the one or more sensors and can be configured to receive the one or more health parameters from the one or more sensors; a health evaluation engine, which when executed by the one or more processors, can monitor each of the one or more valves and evaluate health associated with each of the one or more valves by comparing the received one or more health parameters with standard health parameters of the respective one or more valves such that when variance exists between the received one or more health parameters and corresponding standard health parameters, a corresponding set of first signal can be generated; and a fault prediction engine, which when executed by the one or more processors, can enable the machine learning model to early detect a fault in the one or more valves, and can further generate a corresponding set of second signal based on the early detected fault; wherein the generated set of second signal can be processed by the one or more processors so as to be represented on at least one computing device of users; and wherein the processed set of second signal can be transmitted to the at least one computing device of users to perform any or a combination of preventive, diagnostic, maintenance and replacement operation.

[00047] In an embodiment, the system can include a troubleshooting engine, which when executed by the one or more processors, can send one or more augmented representation of instructions related to troubleshooting operation to a display of the at least one computing device of users to guide the users to perform troubleshoot operation. [00048] In an embodiment, the one or more augmented representation of instructions related to troubleshoot operation can include any or a combination of actions to be performed during maintenance and service of the respective one or more valve, installation guide for the one or more valves, and documents, drawings and blueprints related to the one or more valves, and wherein the one or more augmented representation can be stored in any or a combination of a database associated with the system and a cloud based server.

[00049] In an embodiment, the one or more processors can be configured to prepare a training data set and a testing data set for the machine learning model to train the machine learning model to early detect the fault in the one or more valves, and wherein the training data set and the testing data base can be based on the received one or more health parameters of the respective one or more valves and the generated set of first signal.

[00050] In an embodiment, the generated set of second signal can be in form of an alert that is visually or audibly presented on the at least one computing device of users.

[00051] In an embodiment, the system can be configured to generate a health and diagnostic report for each of the one or more valves based on a data corresponding to the set of first signal and the set of second signal, and wherein the system can be configured to send the health and diagnostic report to the at least one computing device of users.

[00052] In an embodiment, the at least one computing device can include any or a combination of a mobile phone, computer, laptop, server and cloud based server.

[00053] In embodiment, the one or more sensors can include any or a combination of leakage sensor, vibration sensor, position sensor, torque sensor, pressure sensor, temperature sensor and flow sensor.

[00054] According to another aspect, the present disclosure elaborates upon a method for performing remote monitoring and early detection of fault in valves of an industrial unit, the method including the steps of: collecting, from one or more sensors operatively coupled to one or more valves of the industrial unit, one or more health parameters of the one or more valves, wherein the one or more health parameters can include any or a combination of parameters selected from leakage, vibration, position, torque, cycles of operation, pressure, temperature and flow; receiving, at a computing unit including one or more processors configured with a machine learning model, the respective one or more health parameters from the one or more sensors; comparing, by the one or more processor, the received one or more health parameters with standard health parameters of the respective one or more valves; generating, by the one or more processor, a set of first signal when variance exists between the received one or more health parameters and corresponding standard health parameters; early detecting, by machine learning model of the one or more processors, fault in the one or more valves based on the received one or more health parameters of the respective one or more valves and the received set of first signal; and generating, by the one or more processors, a corresponding set of second signal as alert and warning to perform any or a combination of preventative, diagnostic, maintenance and replacement operation, wherein the set of second signal can be based on the predicted fault in the one or more valves.

[00055] In an embodiment, the method can include the step of sending, by the one or more processors, one or more augmented representation of instructions to a display of the at least one computing device of users to guide the users to perform troubleshoot operation, and wherein the one or more augmented representation of instructions can include any or a combination of actions to be performed during maintenance and service of the respective one or more valve, installation guide for the one or more valves, and documents, drawings and blueprint related to the one or more valves.

[00056] FIG. 1 illustrates exemplary implementation architecture of the proposed system, in accordance with an embodiment of the present invention.

[00057] In an embodiment, the proposed industrial monitoring and early fault detection system 110 can determine and evaluate health of the valves and early detect fault in the valves to initiate maintenance and replacement of the faulty valves before actual occurrence of the fault.

[00058] In an implementation, the system 110 can be embedded with/incorporated with one or more Internet of Things (IoT) devices. In a typical network architecture of the present disclosure can include a plurality of network devices such as transmitter, receivers, and/or transceivers that may include one or more IoT devices.

[00059] As used herein, the IoT devices can be a device that includes sensing and/or control functionality as well as a WiFi™ transceiver radio or interface, a Bluetooth™, transceiver radio or interface, a Zigbee™ transceiver radio or interface, an Ultra-Wideband (UWB) transceiver radio or interface, a Wi-Fi-Direct transceiver radio or interface, a Bluetooth™ Low Energy (BLE) transceiver radio or interface, and/or any other wireless network transceiver radio or interface that allows the IoT device to communicate with a wide area network and with one or more other devices. In some embodiments, an IoT device does not include a cellular network transceiver radio or interface, and thus may not be configured to directly communicate with a cellular network. In some embodiments, an IoT device may include a cellular transceiver radio, and may be configured to communicate with a cellular network using the cellular network transceiver radio. [00060] User may interact with the network devices using an application, a web browser, a proprietary program, or any other program executed and operated by the access device. In some embodiments, the access device may communicate directly with the network devices (e.g., communication signal). For example, the access device may communicate directly with network devices using Zigbee™ signals, Bluetooth™ signals, WiFi™ signals, infrared (IR) signals, UWB signals, WiFi-Direct signals, BLE signals, sound frequency signals, or the like. In some embodiments, the access device may communicate with the network devices via the gateways and/or a cloud network.

[00061] Local area network may include a wireless network, a wired network, or a combination of a wired and wireless network. A wireless network may include any wireless interface or combination of wireless interfaces (e.g., Zigbee™, Bluetooth™, WiFi™, IR, UWB, WiFi-Direct, BLE, cellular, Long-Term Evolution (LTE), WiMax™, or the like). A wired network may include any wired interface (e.g., fiber, Ethernet, powerline, Ethernet over coaxial cable, digital signal line (DSL), or the like). The wired and/or wireless networks may be implemented using various routers, access points, bridges, gateways, or the like, to connect devices in the local area network. For example, the local area network may include gateway and gateway. Gateway can provide communication capabilities to network devices and/or access device via radio signals in order to provide communication, location, and/or other services to the devices. The gateway is directly connected to the external network and may provide other gateways and devices in the local area network with access to the external network. The gateway may be designated as a primary gateway.

[00062] The network access provided by gateway may be of any type of network familiar to those skilled in the art that can support data communications using any of a variety of commercially-available protocols. For example, gateways may provide wireless communication capabilities for the local area network 100 using particular communications protocols, such as WiFi™ (e.g., IEEE 802.11 family standards, or other wireless communication technologies, or any combination thereof). Using the communications protocol(s), the gateways may provide radio frequencies on which wireless enabled devices in the local area network can communicate. A gateway may also be referred to as a base station, an access point, Node B, Evolved Node B (eNodeB), access point base station, a Femtocell, home base station, home Node B, home eNodeB, or the like.

[00063] Gateways may include a router, a modem, a range extending device, and/or any other device that provides network access among one or more computing devices and/or external networks. For example, gateway may include a router or access point or a range extending device. Examples of range extending devices may include a wireless range extender, a wireless repeater, or the like.

[00064] A router gateway may include access point and router functionality, and may further include an Ethernet switch and/or a modem. For example, a router gateway may receive and forward data packets among different networks. When a data packet is received, the router gateway may read identification information (e.g., a media access control (MAC) address) in the packet to determine the intended destination for the packet. The router gateway may then access information in a routing table or routing policy, and may direct the packet to the next network or device in the transmission path of the packet. The data packet may be forwarded from one gateway to another through the computer networks until the packet is received at the intended destination.

[00065] Referring now to FIG. 1, in an embodiment FIG. 1 indicates a network implementation 100 of an industrial valve monitoring and early fault detection system 110. [00066] Although the present subject matter is explained considering that the industrial monitoring and early fault detection system is implemented as an application on a server 102, it may be understood that the industrial monitoring early fault detection system 110 can also be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a server, a network server, a cloud-based environment and the like. It would be appreciated that the industrial monitoring and early fault detection system 110 may be accessed by multiple users 106-1, 106-2... 106-N (collectively referred to as users 106 and individually referred to as the user 106 hereinafter), through one or more computing devices 108-1, 108-2... 108-N (collectively referred to as computing devices 108 hereinafter), or applications residing on the computing devices 108. In an aspect, the proposed industrial monitoring and early fault detection system 110 can be operatively coupled to a website and so be operable from any Internet enabled computing device 108. Examples of the computing devices 108 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device, and a workstation. The computing devices 108 are communicatively coupled to the proposed industrial monitoring system 110 through a network 104. It may be also understood that the proposed industrial monitoring and early fault detection system 110 is a system for determining and evaluating health information of valves of the industrial machines and early detection of fault in the valves to initiate maintenance and replacement of the faulty valves before actual occurrence of the fault [00067] In one implementation, the network 104 can be a wireless network, a wired network or a combination thereof. The network 104 can be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the likes. Further, the network 104 can either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further the network 106 can include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.

[00068] As discussed, the computing device 108 (which may include multiple devices in communication in a hard- wired or wireless format) may include at least one of the following: a mobile wireless device, a smartphone, a mobile computing device, a wireless device, a hard-wired device, a network device, a docking device, a personal computer, a laptop computer, a pad computer, a personal digital assistant, a wearable device, a remote computing device, a server, a functional computing device, or any combination thereof. While, in one preferred and non-limiting embodiment, the primary computing device 108 is a smartphone (which may include the appropriate hardware and software components to implement the various described functions), it is also envisioned that the computing device 108 be any suitable computing device configured, programmed, or adapted to perform one or more of the functions of the described system.

[00069] FIG. 2 illustrates an exemplary module diagram for the proposed system 110, in accordance with an embodiment of the present invention.

[00070] In an embodiment, the proposed system 110 can include one or more processor(s) 202. The one or more processor(s) 202 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) 202 can be configured to fetch and execute computer-readable instructions stored in a memory 204 of the proposed system 110. The memory 204 can store one or more computer-readable instructions or routines, which can be fetched and execute to create or share the data units over a network service. In an exemplary embodiment, memory can include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, but not limited to the like. [00071] In an embodiment, the proposed system 110 can also include an interface(s) 206. The interface(s) 206 can include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 206 can facilitate communication of proposed system 110 with various computing devices 108 coupled to the proposed system 110.

[00072] In an embodiment, the proposed system can include one or more sensors 214 operatively coupled to valves of the industrial unit. The sensors 214 can be configured to collect one or more health parameters of the one or more valves. In an embodiment, the one or more sensors 214 can include any or a combination of leakage sensor, vibration sensor, position sensor, torque sensor, pressure sensor, temperature sensor and flow sensor. In an exemplary embodiment, the one or more sensors can be a intrusive sensor, which can be placed inside the valve.

[00073] In an exemplary embodiment, the one or more health parameters can include any or a combination of parameters selected from leakage, vibration, position, torque, cycles of operation, pressure, temperature and flow, but not limited to the likes. In an embodiment, the one or more processors 202 can operatively coupled to the one or more sensors 214 and configured to receive the one or more health parameters from the one or more sensors 214. [00074] In an exemplary embodiment, the leakage sensors can include any or a combination of spot leak detector, a hydroscopic tape-based sensor and a rope-style sensor, but not limited to the likes, to sense leakages in the valve.

[00075] In an exemplary embodiment, the vibration sensors can include any or a combination of an electromagnetic linear velocity transducer, electromagnetic tachometer generator, capacitive accelerometer, piezo-electric accelerometer, potentiometric accelometer, reluctive accelerometer, servo accelerometer, strain-gauge accelerators, eddy- current sense probe and capacitance proximity sensor, but not limited to the likes, to sense vibrations in the valve.

[00076] In an exemplary embodiment, the position sensors can include any or a combination of potentiometric position sensor, capacitance position sensor, magneto strictive position sensor, eddy current-based position sensor, hall effect-based magnetic position sensor, infra-red position sensor and optical position sensor, but not limited to the likes. [00077] In an exemplary embodiment, the temperature sensors can include any or a combination of thermistors, resistance temperature detector, thermocouple and semiconductor-base sensors, but not limited to the likes. [00078] In an exemplary embodiment, the flow sensors can include any or a combination of a turbine-flow sensor, electromagnetic flow sensor, thermal mass flow senor, vortex flow sensor and a coriolis flow sensor, but not limited to the likes, to measure flow of fluid in the valves.

[00079] In an embodiment, the proposed system 110 can include a health evaluator 212 (also referred to as a heath variance evaluator 212 or a health evaluator engine 212, interchangeably), which can be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the health evaluator 212. In examples described herein, such combinations of hardware and programming can be implemented in several different ways. For example, the programming for the health evaluator engine 212 can be processor executable instructions stored on a non- transitory machine -readable storage medium and the hardware for the health evaluator engine 212 can include a processing resource (for example, one or more processors), to execute such instructions. In an implementation, the machine -readable storage medium can store instructions that, when executed by the processing resource, implement the health evaluator engine 212. In such examples, the proposed system 110 can include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine -readable storage medium can be separate but accessible to the proposed system 110 and the processing resource.

[00080] In an embodiment, the health evaluation engine 212, which when executed by the one or more processors 202 can monitor the one or more valves and evaluate health associated with each of the one or more valves by comparing the received one or more health parameters with standard health parameters of the respective one or more valves such that when variance exists between the received one or more health parameters and corresponding standard health parameters, a corresponding set of first signal is generated.

[00081] In an embodiment, the generated first signal can be processed by the one or more processors 202 so as to be represented on a display 210 of the system 110 and/or on displays of the computing devices 108 of users.

[00082] In an embodiment, the proposed system 110 can include a fault predictor 216 (also referred to as a fault detector engine 216 or an early fault detector engine 216, interchangeably) which can be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the early fault detector 216. In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the early fault detector engine 216 can be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the early fault detector engine 216 can include a processing resource (for example, one or more processors), to execute such instructions. In an implementation, the machine -readable storage medium can store instructions that, when executed by the processing resource, implement the early fault detector engine 216. In such examples, the proposed system 110 can include the machine- readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine -readable storage medium can be separate but accessible to the proposed system 110 and the processing resource.

[00083] In an embodiment, the early fault detector engine 2016, which when executed by the one or more processors 202 can enable a machine learning model 218 of the proposed system 110 to early detect a fault in the one or more valves, and further generate a corresponding set of second signal based on the early detected fault. In an embodiment, the generated second signal can be processed by the one or more processors 202 so as to be represented on the computing devices 108 of users and/or display 210 of the system 110. [00084] In an embodiment, the generated set of second signal can be processed by the one or more processors 202 so as to be represented on the display 210 and/or computing devices 108 of users.

[00085] In an embodiment, the proposed system can include a troubleshoot engine 220, which when executed by the one or more processors 202 can send one or more augmented representation of instructions related to troubleshooting operation for the valves to a display of the computing devices 108 of users and the display 210 of the proposed system 110 to guide the users to perform troubleshoot operation.

[00086] In an exemplary embodiment, the one or more augmented representation of instructions related to troubleshoot operation can include any or a combination of actions to be performed during maintenance and service of the respective one or more valve, installation guide for the one or more valves, assembly and disassembly of the valve, service action guide lines, and documents, drawings and blueprints related to the one or more valves, but not limited to the likes.

[00087] In an embodiment, the augmented reality or augmented representation of instructions or steps to be performed for troubleshoot operation provides an interactive experience of a real-world environment to a display of computing devices 108 of user, where objects that reside in the real-world environment are enhanced by computer-generated perceptual information, sometimes across multiple sensory modalities, including visual, auditory, haptic, somatosensory and olfactory. The primary value of augmented reality is the manner in which components of the digital world blend into a user’s perception of the real world, not as a simple display of data, but through the integration of immersive sensations, which are perceived as natural parts of an environment. In an embodiment, the augmented representation of objects of the real world can include the installation guide, documents, blueprint and health and diagnostic report related to the valves. In another embodiment, the augmented representation of real world environment can include illustrations related ot any or a combination of actions to be performed during maintenance and service of the respective one or more valve, installation guide for the one or more valves.

[00088] In an embodiment, the troubleshoot engine 220 can be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the troubleshoot engine 220. In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the troubleshoot engine 220 can be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the troubleshoot engine 220 can include a processing resource (for example, one or more processors), to execute such instructions. In an implementation, the machine -readable storage medium can store instructions that, when executed by the processing resource, implement the troubleshoot engine 220. In such examples, the proposed system 110 can include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine -readable storage medium can be separate but accessible to the proposed system 110 and the processing resource [00089] In an embodiment, the one or more augmented representation can be stored in any or a combination of a database 208 associated with the system 110 and the server 102.

[00090] FIG. 3 illustrates an exemplary process flow diagram for monitoring and evaluation of health parameter valves of the industrial units, in accordance with an embodiment of the present invention.

[00091] Referring to FIG. 3, one or more sensors 214 coupled to the one or more valves of the industrial units can receive and collect health information of the valves at a step 302 of the process flow method for monitoring and evaluation of health parameter valves of the industrial units.

[00092] At a step 304, the collected health information at the step 302 can be compared to the standard health parameters to determine if any variance exists between the standard health information and the collected health information. Based on the comparison, it can be determined whether an action is required at a step 306. On non detection of the variance between the received and the collected health information of the valves the no further action is taken and the sensors are instructed to continue receiving health information from the valves at the step 302. Otherwise, the flow can proceeds to a step 308 of generating a first set of signal via the one or more processors 202 to perform preventive, operative and predictive operations at the valves. Also, the generated first set of signals can represented to raise/generate alarms or notifications on the attached one or more client devices at a step 310.

[00093] FIG. 4 illustrates exemplary high-level system architecture for monitoring the health information of the industrial unit by collecting information via sensors attached to valves of the industrial machines, in accordance with an embodiment of the present invention.

[00094] In an embodiment, the one or more sensors are attached to one or more valves

(402-1, 402-2, 402-3.402-N) (collectively referred to as valves 402, herein) of the industrial units. Information of the valves functioning and health can be gathered via the sensors 214 that are coupled to the valves 402 of the machines at the industrial unit.

[00095] In an embodiment, numerous sensors can be often used to monitor one or more health parameters of the valves 402 of the industrial machines. In an exemplary embodiment, the types of sensors can include vibration, temperature, motion, sound, pressure, but not limited to the likes. The sensors 214 can be disposed in various locations in/on or around the valves 402 industrial machines. In some instances, the sensors may communicate with other devices via a wired connection or a wireless connection. [00096] In an embodiment, the sensors 214 can serve multiple purposes, including enhanced sensing of properties of the industrial machine and the environment surrounding the machine using the multiple sensors.

[00097] In an embodiment, the sensors 214 can include a processor that can receive data and may process the data for various types of analysis. For example, the sensors can continuously monitor certain properties associated with the valves 402 of the industrial machines and the environment surrounding of the valves 402 over time to determine whether the valves and/or the environment surrounding the valve is suitable for the purposes of the proposed system 110. When health measurements acquired by the sensors 214 are outside a range of expected measurements, the processor may perform a preventative action, such as send a notification to a technician/user and/or send a command to the industrial monitoring system and to the attached one or more client devices. [00098] In an embodiment, the sensors 214 can include a wireless communication component (e.g., antenna) that can enable wirelessly transmitting and receiving data.

[00099] In an embodiment, when the sensors 214 can determine that certain unwanted or undesired conditions exist related to the health of the industrial valve of the industrial machine or the surrounding environment, the sensors 214 can disable certain operational functions of the connected computing devices used by a technician/operator.

[000100] In an aspect of the disclosure, the collected health information by the sensors 214 can be transmitted and collected at a gateway 404 to be transferred and stored at a IoT server 406 (also referred to as IOT platform 406, herein).

[000101] In an aspect of the disclosure, the collected health information at the valves can be communicated to the IoT server 406. The IoT server can include one or more servers, one or more computing devices, and the like. Further, the server can include a number of computers that can be connected through a real-time communication network, such as the Internet, Ethernet, EtherNet/IP, Control Net, or the like, such that the multiple computers may operate together as a single entity. The real-time communication network may include any network that enables various devices to communicate with each other at near real-time.

[000102] In an embodiment, the IoT server 406 can be capable of communicating via the industrial monitoring unit 410 to the one or more computing devices 104 of stakeholder and users. As such, the IoT server 406 can be capable of wired or wireless communication between the industrial monitoring unit 410 and a computing interface. [000103] In an embodiment, after receiving the health information data at the IoT server 406 by the sensors, large-scale data analysis operations can be performed on the server. The health information data can be stored in a data storage unit of the server.

[000104] In an embodiment, the IoT server 406 can forward the acquired health information or analysed information to different connected computing devices 108 and various industrial automation equipment, but not limited to the likes. As such, the IoT server 406 can maintain a communication connection with various industrial automation equipment, computing devices, and the like.

[000105] In an embodiment, the sensors described above can be coupled to the industrial machine. The sensors may be located at any suitable position on the industrial machine. In some embodiments, the sensors can be physically coupled to the industrial machine using any suitable mechanism (e.g., bolts, screws, adhesives, magnets). The sensors can be configured to obtain data, read data, receive data, process data, transmit data, and the like. [000106] In an embodiment, the industrial monitoring unit 410 can communicate to the computing devices 108 of clients and stakeholders via a wireless or wired communication. [000107] In an embodiment, the sensors can communicate the valve’s health information to the IoT server 406. The server performs analysis and determines one or more preventative actions. The IoT server 406 performs the analysis and/or to perform the one or more preventative actions, diagnostics, and/or predictive operations that are communicated to the computing devices 108 of client and stakeholders.

[000108] In an exemplary embodiment, the preventative action can include sending a command (e.g., power off command), sending an alert, triggering an alarm on the one or more connected devices 412, but not limited to the likes.

[000109] In an exemplary embodiment, the diagnostics of the health information of the valves can include determining what is causing the variance between the determined health information of the valves and the standard health information.

[000110] In an embodiment, the IoT server 406 can receive health information from the one or more gas sensors, temperature sensors, pressure sensors, motion sensors, vibration sensors, and/or sound sensors over time. The IoT server can also determine the standard health information of the respective sensors. For example, the IoT server can continuously monitor the received data signals from each of the one or more sensors to learn or determine a range for expected measurements. In some embodiments, the IoT server can monitor the health information for a threshold period of time (e.g., 10 min, 30 min, 60 min) to determine the range of expected standard health measurements. In other embodiments, the IoT server may monitor the health information until a threshold number of readings (e.g., 5, 10, 15, 20) are received that indicate health measurements within a threshold range to each other so as to determine the range of expected health measurements.

[000111] In an embodiment, at the industrial monitoring unit 410, one or more portal applications are available. The portal applications require the users to login to the unit 410, to access alerts/notifications. The alerts/notifications can be generated due to determination of variance in the received health information and the standard health information of the valves at the industrial machines. Upon the user logging to the application the IoT server 406 transfers the corresponding alerts/notifications to the one or more client computing devices, for the user/technician to take further action and track and/or monitor operations of the valves of the industrial machines.

[000112] In an embodiment, the client computing devices 108 can depict visualizations associated with the health information of the valves. In an exemplary embodiment, the display of the client computing device can be a touch display capable of receiving inputs from the user of the computing device. Also, the display can serve as a user interface to communicate with the valves of the industrial machines and or the sensors attached to the industrial machines. The display can be used to display a graphical user interface (GUI) for operating the industrial machines, for tracking the maintenance and performing various procedures (e.g., lockout, placing machine offline, replacing component, servicing device) for the industrial machines, and the like.

[000113] In an embodiment, the system can continuously monitor the health parameters of the valve in real-time. In an implementation, the system can transmit automatic alert and warning to maintenance engineer and product engineer of the industrial plant in form any or a combination of an e-mail, sms, notification, voice call, with the health parameters associated with the valve at the time of detection of fault.

[000114] FIG. 5 illustrates an exemplary process flow diagram for early detection of fault in valves of the industrial units, in accordance with an embodiment of the present invention.

[000115] In an embodiment, referring to FIG. 5 illustrates a process flow 500 diagram for early detection of fault in valves of the industrial unit using the machine learning model 218 associated with the proposed system. In an embodiment, the fault prediction engine 216, which when executed by the one or more processors 202 can enable the machine learning unit 218 to early detect a fault in the one or more valves, and further generate a corresponding set of second signal based on the early detected fault.

[000116] In an embodiment, the process flow 500 can include a step 502 of receiving, by the one or more processors 202, the health parameters and the generated first signal from the step 302 and the step 308, respectively. In an embodiment, the process flow can include a step 504 of preparing, by the one or more processors 202, a training data set and a testing data for the machine learning unit 218 based on the received health parameters and the first signal at the step 502. In an embodiment, the process flow 500 can include a step 506 of training, by the one or more processors 202, the machine learning model 218 to early detect fault in the valves based on the training data set and the testing dataset of the step 504.

[000117] In an embodiment, the process flow 500 can include a step 508 of early detecting or predicting, by the machine learning unit 218, an early fault in the valves based on the received health parameters and the first signal at the step 502, and the training data set and the testing dataset of the step 506. [000118] In an embodiment, the process flow 500 can include a step 510 of generating, by the one or more processors 202, a second signal based on the early determined or predicted fault associated with respective valves of the industrial machine or unit. In an embodiment, the generated second signal can be processed by the one or more processors 202 and further send to the computing devices 202 of users to perform any or a combination of preventive, diagnostic, maintenance and troubleshoot operations.

[000119] One of the technical problems solved by the present invention is to provide real time remote monitoring of health parameters of each valves of an industrial unit. Another technical feature od the present invention is to early-detect/predict fault in valves of the industrial unit before actual occurrence of the fault. This enables providing early warning to users or service team regarding maintenance, diagnosis, preventive and troubleshoot operation of the valve, thereby by reducing down time of the industrial process and improved reliability.

[000120] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE INVENTION

[000121] The proposed disclosure provides a system and method for remote monitoring of valves of an industrial unit.

[000122] The proposed disclosure provides a system and method to monitor health of valves of an industrial unit, which generates an alert when the health deviates from standard operating parameters.

[000123] The proposed disclosure provides a system and method for early detection of fault in valves of an industrial unit.

[000124] The proposed disclosure provides a system and method for providing users with steps to be performed for troubleshooting of fault in the valves

[000125] The proposed disclosure provides a system and method for remote monitoring of valves of an industrial unit to perform preventive, diagnostic, maintenance and replacement operation. [000126] The proposed disclosure provides a system and method for remote monitoring and early detection of faults in valves of an industrial unit, which provides steps to be performed for troubleshooting of fault in the valves.

[000127] The proposed disclosure provides a system and method for remote monitoring and early detection of faults in valves of an industrial unit, which provides augmented reality based representation of steps to be performed for troubleshooting of fault in the valves.

Claims

We Claim:

1. A system for remote monitoring and early detection of fault in valve of an industrial unit, the system comprising: one or more sensors operatively coupled to one or more valves of the industrial unit, the one or more sensors configured to collect one or more health parameters of the one or more valves, wherein the one or more health parameters comprises any or a combination of parameters selected from leakage, vibration, position, torque, cycles of operation, pressure, temperature and flow; a computing unit comprising one or more processors configured with a machine learning model to execute one or more instructions stored in a memory of the computing unit, the one or more processors operatively coupled to the one or more sensors and configured to receive the one or more health parameters from the one or more sensors; a health evaluation engine, which when executed by the one or more processors, monitors each of the one or more valves and evaluate health associated with each of the one or more valves by comparing the received one or more health parameters with standard health parameters of the respective one or more valves such that when variance exists between the received one or more health parameters and corresponding standard health parameters, a corresponding set of first signal is generated; and a fault prediction engine, which when executed by the one or more processors, enables the machine learning model to early detect a fault in the one or more valves, and further generate a corresponding set of second signal based on the early detected fault; wherein the generated set of second signal is processed by the one or more processors so as to be represented on at least one computing device of users; and wherein the processed set of second signal is transmitted to the at least one computing device of users to perform any or a combination of preventive, diagnostic, maintenance and replacement operation.

2. The system as claimed in claim 1, wherein the system comprises a troubleshooting engine, which when executed by the one or more processors, sends one or more augmented representation of instructions related to troubleshooting operation to a display of the at least one computing device of users to guide the users to perform troubleshoot operation.

3. The system as claimed in claim 2, wherein the one or more augmented representation of instructions related to troubleshoot operation comprises any or a combination of actions to be performed during maintenance and service of the respective one or more valve, installation guide for the one or more valves, and documents, drawings and blueprints related to the one or more valves, and wherein the one or more augmented representation are stored in any or a combination of a database associated with the system and a cloud based server.

4. The system as claimed in claim 1, wherein the one or more processors are configured to prepare a training data set and a testing data set for the machine learning model to train the machine learning model to early detect the fault in the one or more valves, and wherein the training data set and the testing data base are based on the received one or more health parameters of the respective one or more valves and the generated set of first signal.

5. The system as claimed in claim 1, wherein the generated set of second signal is in form of an alert that is visually or audibly presented on the at least one computing device of users.

6. The system as claimed in claim 1, wherein the system is configured to generate a health and diagnostic report for each of the one or more valves based on a data corresponding to the set of first signal and the set of second signal, and wherein the system is configured to send the health and diagnostic report to the at least one computing device of users.

7. The system as claimed in claim 1, wherein the at least one computing device comprises any or a combination of a mobile phone, computer, laptop, server and cloud based server.

8. The system as claimed in claim 1, wherein the one or more sensors comprises any or a combination of leakage sensor, vibration sensor, position sensor, torque sensor, pressure sensor, temperature sensor and flow sensor.

9. A method for performing remote monitoring and early detection of fault in valves of an industrial unit, the method comprising the steps of: collecting, from one or more sensors operatively coupled to one or more valves of the industrial unit, one or more health parameters of the one or more valves, wherein the one or more health parameters comprises any or a combination of parameters selected from leakage, vibration, position, torque, cycles of operation, pressure, temperature and flow; receiving, at a computing unit comprising one or more processors configured with a machine learning model, the respective one or more health parameters from the one or more sensors; comparing, by the one or more processor, the received one or more health parameters with standard health parameters of the respective one or more valves; generating, by the one or more processor, a set of first signal when variance exists between the received one or more health parameters and corresponding standard health parameters; early detecting, by machine learning model of the one or more processors, fault in the one or more valves based on the received one or more health parameters of the respective one or more valves and the received set of first signal; and generating, by the one or more processors, a corresponding set of second signal as alert and warning to perform any or a combination of preventative, diagnostic, maintenance and replacement operation, wherein the set of second signal is based on the predicted fault in the one or more valves.

10. The method as claimed in claim 9, wherein the method comprises the step of sending, by the one or more processors, one or more augmented representation of instructions to a display of the at least one computing device of users to guide the users to perform troubleshoot operation, and wherein the one or more augmented representation of instructions comprises any or a combination of actions to be performed during maintenance and service of the respective one or more valve, installation guide for the one or more valves, and documents, drawings and blueprint related to the one or more valves.