KR102814298B1 - Opc-ua based integrated data management system and method - Google Patents

Abstract

An OPC-UA-based integrated data management system according to one embodiment of the present invention includes: a data collection device connected to a plurality of operational equipment operating in a smart factory through a standard communication protocol for integrating and connecting different communication methods of each manufacturer, and collecting field data from each operational equipment in real time; an edge gateway receiving the collected field data from the data collection device, converting the data to be suitable for a communication network to which a cloud server is connected, and transmitting the converted field data to a cloud server through the communication network; and a cloud server receiving the converted field data from the edge gateway, storing it in a database, and managing it.

Description

OPC-UA BASED INTEGRATED DATA MANAGEMENT SYSTEM AND METHOD

Embodiments of the present invention relate to a data management system and method, and more particularly, to an OPC-UA based integrated data management system and method.

In most manufacturing industries today, a large number of field devices, sensors, and equipment are interconnected as system components in line with the smart manufacturing innovation trend, and OPC UA (Open Platform Communication Unified Architecture) is being applied as a technology to provide interoperability between these various field devices.

In a manufacturing environment such as a factory, the process of collecting and analyzing data is necessary to promote a smart factory, and in order to promote a smart factory, implementation through a single system of field facilities may be necessary to improve the efficiency of data collection and analysis.

However, due to the diversity of data collection and communication methods of each field facility and machine, it may be difficult to integrate them into one method. Therefore, an OPC-UA-based integrated data management system and method for a single system of field facilities to promote a smart factory is required.

Related prior art includes Korean Patent Publication No. 10-2020-0064315 (Title of the invention: OPC UA-based data processing method and device, publication date: 2020.06.08.).

One embodiment of the present invention provides an OPC-UA-based integrated data management system and method that integrates the systems of field operating equipment by collecting field data from each operating equipment in operation in a smart factory, transmitting the collected data to a cloud server, and storing and managing the collected data in a database for monitoring and analysis, thereby enabling data analysis according to a single standard.

The problems to be solved by the present invention are not limited to the problem(s) mentioned above, and other problem(s) not mentioned will be clearly understood by those skilled in the art from the description below.

An OPC-UA-based integrated data management system according to one embodiment of the present invention includes: a data collection device connected to a plurality of operational equipment operating in a smart factory through a standard communication protocol for integrating and connecting different communication methods of each manufacturer, and collecting field data from each operational equipment in real time; an edge gateway receiving the collected field data from the data collection device, converting the data to be suitable for a communication network to which a cloud server is connected, and transmitting the converted field data to a cloud server through the communication network; and a cloud server receiving the converted field data from the edge gateway, storing it in a database, and managing it.

The above standard communication protocol may include OPC-UA (Open Platform Communications Unified Architecture).

The above cloud server can analyze field data stored in the above database using AI deep learning to monitor each of the above operating devices and provide the results of the monitoring to the operator terminal.

The above cloud server can generate cost charging amount information for a user of an operator terminal based on a cost charging policy according to at least one of the computational volume, data usage, and data communication volume of the cloud server, and provide the generated cost charging amount information to the operator terminal.

The above cloud server can analyze sensing data received through sensors installed in each of the plurality of operating devices, diagnose the operating status of each of the operating devices or the communication status between the operating devices and the cloud server, and provide the diagnosis results to the operator terminal.

The cloud server may generate cost charging amount information for a user of an operator terminal based on a first cost charging policy for a time period in which the failure did not occur, if a failure occurs in at least one of the operation state and the communication state, and may generate cost charging amount information for a user of the operator terminal based on a second cost charging policy different from the first cost charging policy for a time period in which the failure occurred, if a failure occurs in at least one of the operation state and the communication state.

The above cloud server detects a partial discharge signal of an ultra-harmonic band generated due to the operating status of each of the above operating devices through three or more sensors installed in each of the above operating devices, estimates a fault location using a distance measurement method or a triangulation method using the time difference between the signals detected by the sensors, and matches the estimated fault location using the location data of the operating devices stored in advance to determine whether the operating device in which the partial discharge occurred is faulty.

An OPC-UA-based integrated data management method according to one embodiment of the present invention includes a step of connecting a data collection device to a plurality of operational equipment operating in a smart factory through a standard communication protocol for integrating and connecting different communication methods of each manufacturer, and collecting field data from each operational equipment in real time; a step of allowing an edge gateway to receive the collected field data from the data collection device, convert the data to be suitable for a communication network connected to a cloud server, and transmit the converted field data to a cloud server through the communication network; and a step of allowing the cloud server to receive the converted field data from the edge gateway, store it in a database, and manage it.

Specific details of other embodiments are included in the detailed description and the accompanying drawings.

According to one embodiment of the present invention, field data is integrated and collected from each piece of operational equipment operating in a smart factory, transmitted to a cloud server, and stored and managed in a database for monitoring and analysis, thereby integrating the systems of field operational equipment and enabling data analysis according to a single standard.

FIG. 1 is an overall configuration diagram illustrating a network configuration of an OPC-UA-based integrated data management system according to one embodiment of the present invention.
FIG. 2 is a diagram illustrating a process of data transmission and deep learning of an OPC-UA-based integrated data management system according to one embodiment of the present invention.
Figure 3 is a block diagram illustrating a detailed configuration of the cloud server of Figure 1.
FIG. 4 is a diagram illustrating a process for processing an update for operation-related software of an operating device using an OTA technique according to one embodiment of the present invention.
FIG. 5 is an overall flowchart illustrating an OPC-UA-based integrated data management method according to one embodiment of the present invention.

The advantages and/or features of the present invention, and the methods for achieving them, will become clearer with reference to the embodiments described in detail below together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and the embodiments are provided only to make the disclosure of the present invention complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. Like reference numerals refer to like elements throughout the specification.

In addition, in order to efficiently explain the technical components that make up the present invention, the preferred embodiments of the present invention to be implemented below omit, as much as possible, the system functional components that are already provided in each system functional component or are commonly provided in the technical field to which the present invention belongs, and mainly explain the functional components that must be additionally provided for the present invention. If a person having ordinary knowledge in the technical field to which the present invention belongs, among the functional components that are not illustrated below and are omitted, the functions of the components that have been used in the past will be easily understood, and the relationship between the omitted components as described above and the components added for the present invention will also be clearly understood.

In addition, in the following description, the terms "transmission", "communication", "transmission", "reception", and other similar meanings of signals or information include not only direct transmission of signals or information from one component to another, but also transmission via another component. In particular, "transmission" or "transmitting" a signal or information to one component indicates the final destination of the signal or information, and does not mean the direct destination. The same applies to "reception" of a signal or information.

Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings.

FIG. 1 is an overall configuration diagram illustrating a network configuration of an OPC-UA-based integrated data management system according to one embodiment of the present invention, and FIG. 2 is a diagram illustrating a process such as data transmission and deep learning of an OPC-UA-based integrated data management system according to one embodiment of the present invention.

Referring to FIGS. 1 and 2, an OPC-UA-based integrated data management system (100) according to one embodiment of the present invention may be configured to include a data collection device (110), an edge gateway (120), and a cloud server (130).

The above data collection device (110) is connected to a plurality of operating equipment (101) operating in a smart factory and can collect field data in real time from each operating equipment (101).

However, the above-mentioned plurality of operating devices (101) use different communication methods depending on the manufacturer. Therefore, in this embodiment, a standard communication protocol can be used to integrate and connect the operating devices (101) using different communication methods depending on the manufacturer.

That is, the data collection device (110) can be connected to a plurality of operating equipment (101) installed in the smart factory using the standard communication protocol, and through this, the field data can be integrated and collected in real time from each operating equipment (101).

Here, the standard communication protocol may include OPC-UA (Open Platform Communications Unified Architecture). In addition, the field data may include data regarding the operating status of each of the operating equipment (101) and the communication status between the operating equipment (101) and the cloud server (130).

In addition, the field data may further include data from various sensors installed or built into the operating equipment (101), such as an acceleration sensor, a noise sensor, a light sensor, a gyro sensor, a humidity sensor, a magnetic sensor, an input sensor, a pressure sensor, a temperature sensor, etc.

The edge gateway (120) can receive the collected field data from the data collection device (110). The edge gateway (120) can convert the received field data to be suitable for the communication network to which the cloud server (130) is connected. The edge gateway (120) can transmit the field data converted to be suitable for the communication network to the cloud server (130) through the communication network.

In this way, the edge gateway (120) can serve as a relay for transmission and reception of data such as field data, command data for remote processing, etc. between the data collection device (110) and the cloud server (130).

Meanwhile, the edge gateway (120) can group sensors installed in each of the plurality of operating devices (101) according to a wireless communication method including at least one of Wi-Fi, Bluetooth, and Zigbee, and independently allow communication occupation for the wireless communication method for each group according to the grouping. The edge gateway (120) can selectively connect a wireless communication method associated with the overlapping channel when there is channel overlap between each group.

At this time, the edge gateway (120) can dynamically determine the priority and connection time of the selective connection for each wireless communication method associated with the corresponding overlapping channel based on the size of the group associated with each wireless communication method, the average data amount per group, and the average transmission cycle.

The cloud server (130) can receive field data converted to be suitable for the communication network from the edge gateway (120) and store it in a database. In addition, the cloud server (130) can manage the field data stored in the database to perform functions such as monitoring and analysis. Hereinafter, the cloud server (130) will be described in more detail with reference to FIG. 2.

FIG. 3 is a block diagram illustrating the detailed configuration of the cloud server (130) of FIG. 1.

Referring to FIGS. 1 and 3, the cloud server (130) may include a monitoring unit (310), a billing unit (320), a diagnosis prescription unit (330), a fault determination unit (340), a remote processing unit (350), and a control unit (360).

The above monitoring unit (310) can analyze field data stored in the database using AI deep learning to perform monitoring on each of the operating equipment (101) and provide the results of the monitoring to the operator terminal (102).

The above-mentioned cost charging unit (320) can generate cost charging amount information for a user of the operator terminal (102) based on a cost charging policy according to at least one of the computational amount, data usage amount, and data communication amount of the cloud server, and provide the generated cost charging amount information to the operator terminal (102).

Here, the cost charging unit (320) may utilize one of a plurality of preset cost charging policies selected based on input information received through the operator terminal (102). In the case of the cost charging policy, the amount may be set to be charged more as the server's computational volume increases, as the data usage increases, and as the data communication volume increases.

The above-mentioned cost charging unit (320) can generate cost charging amount information for the user of the operator terminal based on the first cost charging policy for a time period in which the failure did not occur, if at least one of the operating status of the operating equipment (101) and the communication status between the operating equipment (101) and the cloud server (130) does not fail.

On the other hand, if a failure occurs in at least one of the operating status of the operating equipment (101) and the communication status between the operating equipment (101) and the cloud server (130), the cost charging unit (320) can generate cost charging amount information for the user of the operator terminal (102) based on a second cost charging policy different from the first cost charging policy for the time period in which the failure occurred.

Here, the second cost charging policy is such that if a failure occurs in at least one of the operating status of the operating equipment (101) and the communication status between the operating equipment (101) and the cloud server (130), the system operated by the user is not operating smoothly, so the charging amount for unit data (data size) or unit time can be relatively reduced and adjusted.

The above diagnostic prescription unit (330) analyzes sensing data received through sensors installed in each of the plurality of operating devices (101), diagnoses the operating status of each of the operating devices (101) or the communication status between the operating devices (101) and the cloud server (130), and provides the diagnosis results to the operator terminal (102).

For example, the diagnostic prescription unit (330) can analyze field data stored in the database based on AI deep learning and perform a fault diagnosis on the operating equipment (101) based on the results of the analysis.

To this end, the above diagnostic prescription unit (330) can train a deep learning model for fault diagnosis using big data consisting of field data collected according to the operating time of the operating equipment (101) through time series analysis as learning data.

The above diagnosis prescription unit (330) can classify the failure type of the operating equipment (101) using the above fault diagnosis deep learning model. In addition, the diagnosis prescription unit (330) can perform a fault diagnosis on the operating equipment (101) using classification information on the failure type of the operating equipment (101).

The above diagnosis prescription unit (330) can estimate the life of the operating equipment (101) by identifying the operating time of the operating equipment (101) through time series analysis. The above diagnosis prescription unit (330) can apply the life estimation data of the operating equipment (101) and the failure and maintenance history data to the deep learning model for failure diagnosis to produce data on whether the operating equipment (101) has reached the end of its life and the risk of wear and failure.

Here, the above failure and maintenance history data may include failure name, failure date and time, part name, replacement date and time, etc.

The above diagnostic prescription unit (330) can extract prescription information matching the fault diagnosis information for the operating equipment (101) from the database and provide the extracted prescription information to the operator terminal (102).

To this end, the diagnosis prescription unit (330) can search for fault diagnosis information on the operating equipment (101) from the database using the generated data, that is, data on whether the operating equipment (101) has reached its end of life and the risk of wear and tear failure. Accordingly, the diagnosis prescription unit (330) can extract prescription information (inspection or replacement of equipment/parts (101) before failure) matching the searched fault diagnosis information from the database, and provide the extracted prescription information to the operator terminal (102).

The above diagnosis prescription unit (330) can provide the prescription information to the operator terminal (102) as a push notification when the operating equipment (101) is diagnosed as broken. In addition, the diagnosis prescription unit (330) can provide the prescription information to the repair shop terminal as a push notification in response to the operator's request for fault action.

The above fault determination unit (340) detects a partial discharge signal of an ultra-harmonic band generated due to the operating state of each of the above operational equipment (101) through three or more sensors installed in each of the above operational equipment (101), estimates the fault location using a distance measurement method or a triangulation measurement method using the time difference between the signals detected by the sensors, and matches the estimated fault location using the location data of the operational equipment (101) stored in advance to determine whether the operational equipment (101) in which the partial discharge occurred is faulty.

Specifically, the fault determination unit (340) can estimate the fault location of the operating equipment (101) by using a distance measurement method or a triangulation method using the time difference between the partial discharge signals detected by the sensors. The fault determination unit (340) can match the estimated fault location using the location data of the operating equipment (101) stored in advance, and can determine whether the operating equipment (101) in which the partial discharge has occurred is faulty by using the matching information of the fault location.

Once the fault location is estimated in this way, the fault determination unit (340) can match the coordinates of the estimated fault location using the location data of the operating equipment (101) stored in advance. At this time, the location data of the operating equipment (101) can be made up of three-dimensional data consisting of x, y, and z-axis coordinates.

According to one embodiment of the present invention, the area for the boundary portion between the operating equipment (101) is set to match the actual size of the operating equipment (101), so that the operating equipment (101) in which a failure has occurred can be accurately determined through the estimated failure location coordinates.

The above fault determination unit (340) can convert the noise sensor data, i.e., the noise data, among the above field data into a spectrogram image, and perform AI deep learning based on the converted spectrogram image, the equipment type, and the collection location information of the noise data. The above fault determination unit (340) can determine whether the equipment related to the noise data is faulty based on the result of the AI deep learning.

Specifically, the above-described fault determination unit (340) classifies noise data collected in advance for each operating equipment (101) into one of the following types: normal sound, shaft failure sound, bearing damage sound, cavitation sound, impeller failure sound, and motor bearing sound, and performs data classification for learning, and can convert the noise data classified for each operating equipment (101) into a spectrogram image.

The above fault determination unit (340) can use the spectrogram image as learning data to divide it into a training set and a test set at a preset ratio, and perform training and testing functions for a preset fault diagnosis deep learning model using the divided training set and test set.

The above fault determination unit (340) performs AI deep learning using the spectrogram image, the type of operating equipment (101), and the collection location information of the noise data as input values of the fault diagnosis deep learning model, and can determine whether the operating equipment (101) related to the noise data is faulty based on the result of the AI deep learning.

The above fault determination unit (340) performs a calculation with a softmax regression function based on the result of the AI deep learning to output a fault probability value for the operating equipment (101) as an output value, and sets a target value for the output value so that an error between the output value and the target value can be corrected using the gradient descent method. Through this correction, the fault determination unit (340) can more accurately determine whether the operating equipment (101) is faulty.

The above fault determination unit (340) can predict a fault in the operating equipment (101) through the trend of the field data collected by the data collection device (110). That is, the fault determination unit (340) sets a reference period, analyzes the trend of the data stored in the database based on the set period, and applies the collected field data to the trend of the analyzed data to predict a fault in the operating equipment (101).

The above remote processing unit (350) can process an update for the operation-related software of the above operating equipment (101) using the OTA (Over The Air) technique when the above operating equipment (101) is diagnosed as being faulty. That is, as shown in Fig. 4, the above remote processing unit (350) can execute a cloud-based software update for the above operating equipment (101) using the OTA technique.

For reference, OTA refers to a method for wirelessly distributing new software, firmware, settings, and encryption key updates to devices such as mobile phones and set-top boxes. Firmware is mounted on non-volatile memory such as ROM, EPROM, and flash memory, so it cannot be changed after production. Therefore, a technology for wireless distribution is needed to solve the inconvenience of physical connection that is absolutely necessary to add firmware bugs or new functions, and that is OAT. OAT can be transmitted to all users from a single central control center (users cannot reject or modify updates), and updates are immediately applied to all users through the channel.

The above control unit (360) can control the overall operation of the cloud server (130), that is, the monitoring unit (310), the billing unit (320), the diagnosis prescription unit (330), the fault determination unit (340), the remote processing unit (350), etc. The control unit (360) can be implemented by functionally including some or all of the components, such as the monitoring unit (310), the billing unit (320), the diagnosis prescription unit (330), the fault determination unit (340), the remote processing unit (350). That is, the control unit (360) can perform some functions of the components, or alternatively, can perform all functions of the components.

The control unit (360) controls the overall operation of the cloud server (130) and may include a processor such as a CPU. The control unit (360) may control other components included in the cloud server (130) to perform operations corresponding to user input received through the input/output unit. Here, the processor may process commands within the computing device, and such commands may include commands stored in a memory or storage device to display graphic information for providing a GUI (Graphical User Interface) on an external input/output device, such as a display connected to a high-speed interface. In another embodiment, a plurality of processors and/or a plurality of buses may be appropriately utilized together with a plurality of memories and memory types. In addition, the processor may be implemented as a chipset formed by chips including a plurality of independent analog and/or digital processors.

The devices described above may be implemented as hardware components, software components, and/or a combination of hardware components and software components. For example, the devices and components described in the embodiments may be implemented using one or more general-purpose computers or special-purpose computers, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing instructions and responding to them. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. In addition, the processing device may access, store, manipulate, process, and generate data in response to the execution of the software. For ease of understanding, the processing device is sometimes described as being used alone, but those skilled in the art will appreciate that the processing device may include multiple processing elements and/or multiple types of processing elements. For example, the processing device may include multiple processors, or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are also possible.

The software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing device to perform a desired operation or may independently or collectively command the processing device. The software and/or data may be permanently or temporarily embodied in any type of machine, component, physical device, virtual equipment, computer storage medium or device, or transmitted signal waves, for interpretation by the processing device or for providing instructions or data to the processing device. The software may also be distributed over network-connected computer systems, and stored or executed in a distributed manner. The software and data may be stored on one or more computer-readable recording media.

FIG. 5 is an overall flowchart illustrating an OPC-UA-based integrated data management method according to one embodiment of the present invention.

The OPC-UA based integrated data management method described herein can be understood as a concept including components and functions of an OPC-UA based integrated data management system according to one embodiment of the present invention.

The above OPC-UA based integrated data management method is only one embodiment of the present invention, and various steps may be added as needed, and the steps below may also be implemented by changing the order, so the present invention is not limited to each step and the order described below.

Referring to FIGS. 1 and 5, in step (510), the data collection device (110) is connected to the plurality of operating equipment (101) through a standard communication protocol for integrating and connecting different communication methods of each manufacturer in the plurality of operating equipment (101) operating in a smart factory, thereby integrating and collecting field data from each operating equipment (101) in real time.

Next, in step (520), the edge gateway (120) can receive the collected field data from the data collection device (110).

Next, in step (530), the edge gateway (120) can convert the received field data to be suitable for the communication network to which the cloud server (130) is connected.

Next, in step (540), the edge gateway (120) can transmit the converted field data to the cloud server (130) through the communication network.

Next, in step (550), the cloud server (130) can receive the converted field data from the edge gateway (120) and store and manage it in the database.

The method according to the embodiment may be implemented in the form of program commands that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program commands, data files, data structures, etc., alone or in combination. The program commands recorded on the medium may be those specially designed and configured for the embodiment or may be those known to and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CDROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specially configured to store and execute program commands such as ROMs, RAMs, flash memories, etc. Examples of the program commands include not only machine language codes generated by a compiler but also high-level language codes that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiment, and vice versa.

Although the embodiments have been described above by way of limited examples and drawings, those skilled in the art can make various modifications and variations from the above description. For example, appropriate results can be achieved even if the described techniques are performed in a different order than the described method, and/or components of the described system, structure, device, circuit, etc. are combined or combined in a different form than the described method, or are replaced or substituted by other components or equivalents.

Therefore, other implementations, other embodiments, and equivalents to the claims are also included within the scope of the claims.

100: OPC-UA based integrated data management system
110: Data collection device
120: Edge Gateway
130: Cloud Server
310: Monitoring Department
320: Expenses Billing Department
330: Diagnostic Prescription
340: Fault Determination Unit
350: Remote Processing Unit
360: Control Unit

Claims (8)

A data collection device that is connected to multiple operating equipment and collects field data in real time from each operating equipment through a standard communication protocol for integrating and connecting different communication methods of each manufacturer among multiple operating equipment operating in a smart factory;
An edge gateway that receives the collected field data from the data collection device, converts it to be suitable for the communication network to which the cloud server is connected, and then transmits it to the cloud server through the communication network; and
Includes a cloud server that receives the converted field data from the edge gateway and stores and manages it in a database;
The above cloud server
A cost charging unit that generates cost charging amount information for a user of an operator terminal based on a cost charging policy according to at least one of the computational amount, data usage, and data communication amount of the cloud server, and provides the generated cost charging amount information to the operator terminal; and
A diagnostic prescription unit is included that analyzes sensing data received through sensors installed in each of the above-mentioned plurality of operating devices, diagnoses the operating status of each of the above-mentioned operating devices or the communication status between the above-mentioned operating devices and the cloud server, and provides the diagnostic results to the operator terminal.
The above diagnostic prescription unit trains a deep learning model for fault diagnosis using big data consisting of field data collected according to the operating time of the above-mentioned operating equipment through time series analysis as learning data, and performs a fault diagnosis on the above-mentioned operating equipment by analyzing the field data stored in the above-mentioned database based on the deep learning model for fault diagnosis.
Based on the above operating time, life estimation data is produced that can estimate the life of the above operating equipment.
By applying the above life estimation data and the failure and maintenance history data for the above operating equipment to the above fault diagnosis deep learning model, data on the arrival of the life of the above operating equipment and the risk of wear and failure are produced.
Using data on the arrival of the lifespan of the above operating equipment and the risk of wear and tear failure, fault diagnosis information for the above operating equipment is searched for from the above database, prescription information matching the searched fault diagnosis information is extracted from the above database, and the extracted prescription information is provided to the operator terminal.
An OPC-UA-based integrated data management system, characterized in that the above-mentioned cost charging unit generates cost charging amount information for a user of an operator terminal based on a first cost charging policy for a time period in which the failure did not occur when at least one of the operation state and the communication state does not fail, and generates cost charging amount information for a user of the operator terminal based on a second cost charging policy different from the first cost charging policy for a time period in which the failure occurs when at least one of the operation state and the communication state fails.
In the first paragraph,
The above standard communication protocol is
An OPC-UA based integrated data management system characterized by including OPC-UA (Open Platform Communications Unified Architecture).
In the first paragraph,
The above cloud server
An OPC-UA-based integrated data management system characterized in that it analyzes field data stored in the above database using AI deep learning to perform monitoring on each of the above operating equipment and provides the results of the monitoring to the operator terminal.
delete delete delete In the first paragraph,
The above cloud server
An OPC-UA-based integrated data management system characterized in that when a partial discharge signal of an ultra-harmonic band generated due to the operating state of each of the above operating equipment is detected through three or more sensors installed in each of the above operating equipment, a fault location is estimated using a distance measurement method or a trilateration measurement method using the time difference between the signals detected by the sensors, and the estimated fault location is matched using the location data of the operating equipment stored in advance to determine whether the operating equipment in which the partial discharge occurred is faulty. delete

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