Conveyance apparatus monitoring system and method

Abstract

According to an embodiment, a coal transport facility monitoring system is provided, comprising: a communication unit that collects field data including acoustic data and temperature data from a power plant coal transport facility; a data processing unit that classifies the field data according to abnormal vibration types of a conveyor belt to generate learning data; and a learning model unit that combines feature values ​​of the learning data and calculates alarm range values ​​for each vibration data signal band classification through learning.

Description

Technology Field

[0001] One embodiment of the present invention relates to a coal transport facility monitoring system and method. Background Technology

[0003] Generally, thermal power generation systems include thermal power generation systems that produce electricity using coal as a raw material and combined cycle thermal power generation systems that produce electricity using natural gas as a raw material.

[0004] A thermal power generation system is a method in which energy obtained by burning coal is used to boil water in a boiler to create steam, and that steam is used to rotate a turbine to generate rotational force, which is then used to produce electricity in a generator connected to the turbine.

[0005] In addition, the above combined cycle power generation system is a method of producing electricity in the first stage by directly rotating a gas turbine with high-temperature combustion gas produced by burning natural gas, and then sending the high-temperature exhaust gas emitted at this time to a boiler to generate steam, and then rotating a turbine with the power of the steam to produce electricity in the second stage.

[0006] Here, the coal-fired power generation system is equipped with a coal transport facility that unloads coal brought by ships or other means of transport, transports it to a low-carbon facility, and then crushes the coal into small pieces in a pulverizer so that it can be burned well in a boiler; a boiler facility that produces high-temperature, high-pressure steam using the heat generated when burning the coal; a turbine facility that rotates at high speed by the steam produced in the boiler facility and drives a generator; a water treatment facility that produces industrial water into pure water through various treatments; a feedwater facility that produces pure water through the water treatment facility, condenses it in a condenser, and supplies it to the boiler; a circulating water facility that supplies seawater to drive the turbine facility and cool the high-temperature steam in the condenser; a ventilation facility that supplies air necessary for combustion in the combustion chamber of the boiler facility, removes dust from the combusted gas in an electrostatic precipitator, removes sulfur components in a desulfurization facility, and discharges it through a chimney; and an electrical facility that steps up the electricity produced by the generator rotating by the turbine facility through a transformer and transmits it outside the power plant via transmission tower lines.

[0007] In such a coal-fired power generation system, the coal transport facility serves as a coal conveying system and is equipped with a tripper that feeds raw coal into pulverizer storage silos according to type. This tripper is installed on one side of a conveyor positioned on the silo side and functions to distribute and feed the coal transported via the conveyor to each respective silo.

[0008] Existing coal-fired thermal power generation systems operate by installing a single Distributed Temperature Sensing (DTS), multiple vibration sensors, or a single Distributed Acoustic Sensing (DAS) unit to detect simple fault locations, which presents a problem in that it is difficult to accurately diagnose the condition of the conveyor. The problem to be solved

[0010] The technical problem that the present invention aims to solve is to provide a coal handling facility monitoring system and method capable of determining when a failure occurs in a power plant coal handling facility and analyzing the cause of the failure. means of solving the problem

[0012] According to an embodiment, a coal transport facility monitoring system is provided, comprising: a communication unit that collects field data including acoustic data and temperature data from a power plant coal transport facility; a data processing unit that classifies the field data according to abnormal vibration types of a conveyor belt to generate learning data; and a learning model unit that combines feature values ​​of the learning data and calculates alarm range values ​​for each vibration data signal band classification through learning.

[0013] It may further include a judgment unit that determines whether the coal transport facility is faulty by comparing the above-mentioned field data with the alarm range value for each vibration data signal band classification.

[0014] The above data processing unit can generate the training data by classifying the field data by conveyor belt operation status, by vibration data signal band, and by major failure type.

[0015] The above classification by conveyor belt operation state may include any one of the following: conveyor belt stopped state, empty conveyor belt operation state for trial run, coal transporting state, state going to stop operation, and conveyor belt stopped state.

[0016] The above classification of vibration data signal bands may include any one of the following: High state, Low state, Mid state, Variant state, and other states.

[0017] The above classification by major failure type may include any one of the following: belt aging and breakage, idler support defect, or bearing defect.

[0018] According to an embodiment, a method for monitoring a coal transport facility is provided, comprising: a step in which a communication unit collects field data including acoustic data and temperature data from a power plant coal transport facility; a step in which a processing unit classifies the field data according to the type of abnormal vibration of a conveyor belt to generate learning data; and a step in which a learning model unit combines feature values ​​of the learning data and then calculates alarm range values ​​for each vibration data signal band classification through learning.

[0019] The judgment unit may further include a step of determining whether the coal transport facility is faulty by comparing the above-mentioned field data with the alarm range value for each vibration data signal band classification.

[0020] The step of generating the above training data can generate the training data by classifying the field data by conveyor belt operation status, by vibration data signal band, and by major failure type.

[0021] The above classification by conveyor belt operation state may include any one of the following: conveyor belt stopped state, empty conveyor belt operation state for trial run, coal transporting state, state going to stop operation, and conveyor belt stopped state.

[0022] The above classification of vibration data signal bands may include any one of the following: High state, Low state, Mid state, Variant state, and other states.

[0023] The above classification by major failure type may include any one of the following: belt aging and breakage, idler support defect, or bearing defect.

[0024] According to an embodiment, a computer-readable recording medium is provided on which a program for executing the above-described method is recorded. Effects of the invention

[0026] The coal transport facility monitoring system and method according to the embodiment can improve the accuracy of determining whether a failure has occurred in the power plant coal transport facility.

[0027] In addition, it can be automatically classified according to the type of failure. Brief explanation of the drawing

[0029] FIG. 1 is a block diagram of a coal transport facility monitoring system according to an embodiment. Figure 2 is a conceptual diagram of a coal conveyor belt. FIG. 3 is a flowchart of a method for monitoring a coal transport facility according to an embodiment. Specific details for implementing the invention

[0030] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the attached drawings.

[0031] However, the technical concept of the present invention is not limited to some of the described embodiments but can be implemented in various different forms, and within the scope of the technical concept of the present invention, one or more of the components among the embodiments may be selectively combined or substituted.

[0032] In addition, terms used in the embodiments of the present invention (including technical and scientific terms) may be interpreted in a meaning that is generally understood by those skilled in the art to which the present invention belongs, unless explicitly and specifically defined otherwise. Terms that are commonly used, such as terms defined in advance, may be interpreted in consideration of their meaning in the context of the relevant technology.

[0033] Furthermore, the terms used in the embodiments of the present invention are for the purpose of describing the embodiments and are not intended to limit the present invention.

[0034] In this specification, the singular form may include the plural form unless specifically stated otherwise in the text, and when described as "at least one of A and B and C (or more than one)," it may include one or more of all combinations that can be formed from A, B, and C.

[0035] In addition, terms such as first, second, A, B, (a), (b), etc. may be used when describing the components of the embodiments of the present invention.

[0036] These terms are intended merely to distinguish a component from other components and are not limited by the essence, order, sequence, etc. of the component.

[0037] And, where it is stated that a component is 'connected', 'combined', or 'joined' to another component, this may include not only cases where the component is directly connected, combined, or joined to the other component, but also cases where it is 'connected', 'combined', or 'joined' due to another component located between the component and the other component.

[0038] Furthermore, when described as being formed or placed "above or below" each component, "above" or "below" includes not only cases where two components are in direct contact with each other, but also cases where one or more other components are formed or placed between the two components. Additionally, when expressed as "above or below," it may include the meaning of a downward direction as well as an upward direction relative to a single component.

[0039] Hereinafter, embodiments will be described in detail with reference to the attached drawings, provided that identical or corresponding components are given the same reference number regardless of the drawing symbols, and redundant descriptions thereof will be omitted.

[0041] FIG. 1 is a block diagram of a coal transport facility monitoring system according to an embodiment. Referring to FIG. 1, the coal transport facility monitoring system (100) according to an embodiment may include a communication unit (110), a database (120), a data processing unit (130), a learning model unit (140), and a judgment unit (150). In the embodiment, the communication unit (110), the database (120), the data processing unit (130), and the judgment unit (150) may be installed in a DATS device, and the learning model unit (140) may be installed in an upper server. The learning model unit (140) may receive unclassified learning data received from a DATS (Distributed Acoustic Temperature Sensing) device, perform deep learning, output the result of the deep learning, and transmit it to the DATS device. The DATS device may update the received deep learning result and use it to perform monitoring of the coal transport facility.

[0042] The communication unit (110) can collect field data including acoustic data and temperature data from the power plant coal handling facility. The communication unit (110) can collect acoustic data and temperature data from the acoustic measurement module and the temperature measurement module installed in the DATS equipment, respectively. The communication unit (110) can collect acoustic data and temperature data according to a preset cycle.

[0043] For example, the communication unit (110) can perform data communication using long-distance communication technologies such as Wireless LAN (WLAN), Wi-Fi, Wireless Broadband (Wibro), World Interoperability for Microwave Access (Wimax), High Speed ​​Downlink Packet Access (HSDPA), IEEE 802.16, Long Term Evolution (LTE), and Wireless Mobile Broadband Service (WMBS).

[0044] Alternatively, the communication unit (110) may include Bluetooth, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), Ultra Wideband (UWB), Zigbee, Near Field Communication (NFC), etc. Additionally, as a wired communication technology, data communication can be performed using short-range communication technologies such as USB communication, Ethernet, serial communication, optical / coaxial cable, and power line communication.

[0045] The database (120) can store field data including collected acoustic data and temperature data. Additionally, the database (120) can store training data classified and generated through the data processing unit (130). Furthermore, the database (120) can receive and store training results received from the training model unit (140), and can update and store them whenever training results are received.

[0046] The database (120) may include at least one storage medium among Flash Memory Type, Hard Disk Type, Multimedia Card Micro Type, Card Type Memory (e.g., SD or XD memory), Magnetic Memory, Magnetic Disk, Optical Disk, RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), and PROM (Programmable Read-Only Memory). Additionally, the coal transport facility monitoring system may operate a web storage that performs the storage function of the database (120) on the internet, or may operate in relation to the web storage.

[0047] In addition, the database (120) can store data and programs necessary for the operation of the coal transport facility monitoring system.

[0048] Additionally, the database (120) (190) can store various user interfaces (UI) or graphic user interfaces (GUI).

[0049] The data processing unit (130) can generate training data by classifying field data according to the type of abnormal vibration of the conveyor belt.

[0050] The data processing unit (130) can generate the training data by classifying field data by conveyor belt operation status, by vibration data signal band, and by major failure type.

[0051] In the embodiment, the classification by conveyor belt operation state may include any one of the following: a conveyor belt stop state, an empty conveyor belt operation state for trial run, a state in transit, a state going to stop operation, and a conveyor belt stop state.

[0052] In addition, the classification of vibration data signals by band may include any one of the following: High state, Low state, Mid state, Variant state, and other states.

[0053] In addition, the classification by major failure type may include any one of the following: belt aging and breakage, idler support defects, or bearing defects.

[0054] The data processing unit (130) can classify abnormal vibration types according to collected frequency characteristics through classification by conveyor belt operation status, classification by vibration data signal band, and classification by major failure type, and generate the classified data as training data.

[0055] For example, the data processing unit (130) can classify field data according to 60 abnormal vibration types. That is, the data processing unit (130) can classify field data into learning data according to 60 abnormal vibration types by combining 4 classifications by conveyor belt operating status, 5 classifications by vibration data signal band, and 3 classifications by major failure types.

[0056] At this time, the data processing unit (130) can filter out unclassified data by comparing the training data with the training data already stored in the database (120). Unclassified data may refer to a type of data that has not previously been generated as training data. The unclassified data can be transmitted to the training model unit (140) to perform machine learning.

[0057] The learning model unit (140) can calculate alarm range values ​​for each vibration data signal band classification after combining feature values ​​of the learning data through learning.

[0058] For example, the learning model unit (140) can automatically calculate alarm range values ​​for each vibration data signal band classification by combining two or more feature values ​​through learning.

[0059] The learning model unit (140) may include a neural network that learns the correlation between alarm range values ​​by vibration data signal band classification using the learning data generated through the data processing unit (130) as an input layer, and learns the alarm range values ​​by vibration data signal band classification as an output layer.

[0060] The learning model unit (140) may include a computer-readable program. The program may be stored in a recording medium or storage device that can be executed by a computer. An artificial intelligence processor within the computer reads the program stored in the recording medium or storage device, executes the program, i.e., the learned model, to perform calculations on the input information, and outputs the calculation result.

[0061] The judgment unit (150) can determine whether the coal transport facility is faulty by comparing the field data with the alarm range value for each vibration data signal band classification.

[0062] The judgment unit (150) can determine which alarm range the field data is included in by comparing the field data measured by the acoustic measurement module and the temperature measurement module installed in the DATS equipment with the alarm range value for each vibration data signal band classification output from the learning model unit (140). Through this, the judgment unit (150) can classify the type of failure of the coal handling equipment according to the conveyor belt operation status and the vibration data signal band.

[0063] The judgment unit (150) can process the location where vibration occurred and the field data into information in the form of animation based on a 3D Digital Twin and provide it to the user.

[0064] Additionally, the judgment unit (150) can perform the function of verifying the safety of workers working at the power plant through video data. Since workers are not allowed to enter during conveyor operation and coal handling in accordance with safety regulations for the safety of workers, the judgment unit (150) can detect this through video data and transmit a warning message to an upper server according to the type of failure of the coal handling equipment.

[0065] FIG. 2 is a conceptual diagram of a coal conveyor belt. Referring to FIG. 2, the coal conveyor belt is made of rubber material and may be mechanically damaged if foreign substances (iron structures, fittings, etc.) that may be contained in the coal fall on it. The idler support (1), belt bearing (3), and belt (4) may be the main parts of damage. The coal conveyor equipment monitoring system according to the embodiment can determine whether a failure has occurred in the coal conveyor belt and the location of the failure based on field data measured at the site, using abnormal vibration types and a learning model.

[0066] The upper server may be a server including a personal computer (PC), tablet PC, mobile terminal, etc.

[0067] The upper server may include a learning model unit (140). The learning model unit (140) of the upper server is identical to the aforementioned learning model unit (140), and a separate machine learning server may be formed for processing a large amount of data through machine learning. The upper server may generate a learning model unit (140) using learning data received from the DATS equipment and transmit the learning result or the learning model to the DATS equipment. Through this, the DATS equipment can determine whether a failure has occurred in the coal transport equipment by continuously updating the learning result or the learning model generated by the upper server without performing a large amount of computation to generate the learning model unit (140).

[0068] FIG. 3 is a flowchart of a method for monitoring a coal transport facility according to an embodiment.

[0069] Referring to FIG. 3, first, the communication unit collects field data including acoustic data and temperature data from the power plant coal handling facility (S301).

[0070] Next, the processing unit classifies field data according to the type of abnormal vibration of the conveyor belt to generate training data (S302).

[0071] Next, the processing unit compares the generated training data with the training data already stored in the database (S303~304).

[0072] Next, if the training data is unclassified data, the processing unit transmits the training data to the upper server (S305~306).

[0073] At this time, if the training data is not unclassified data, the training data is saved to the database (S307).

[0074] Next, the learning model unit of the upper server combines the feature values ​​of the learning data and calculates alarm range values ​​for each vibration data signal band classification through learning. At this time, the administrator can determine whether learning is necessary and decide whether to learn the corresponding learning data. Through this, the accuracy of the learning model unit can be improved (S308~311).

[0075] Next, the upper server transmits the training result or training model to the decision unit (S312~S313).

[0076] Next, the judgment unit compares the field data measured through the DATS equipment with the alarm range values ​​for each vibration data signal band classification of the learning result or learning model received from the upper server to determine whether the coal handling equipment is faulty, and then provides the judgment result to the user or manager (S314~315).

[0077] The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable recording medium. In this case, the medium may continuously store a program executable by a computer, or temporarily store it for execution or download. Additionally, the medium may be various recording or storage means in the form of a single or several hardware combined, and is not limited to a medium directly connected to a computer system, but may exist distributed over a network. Examples of media may include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and media configured to store program instructions, including ROM, RAM, and flash memory. Additionally, other examples of media may include recording or storage media managed by app stores that distribute applications or sites and servers that supply or distribute various other software.

[0079] The term "part" as used in this embodiment refers to a software or hardware component, such as a field-programmable gate array (FPGA) or an ASIC, and the "part" performs certain roles. However, the meaning of "part" is not limited to software or hardware. The "part" may be configured to reside in an addressable storage medium or configured to run one or more processors. Thus, as an example, the "part" includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and "parts" may be combined into a smaller number of components and "parts" or further separated into additional components and "parts." In addition, the components and '~parts' may be implemented to play one or more CPUs within the device or secure multimedia card.

[0080] Although the present invention has been described above with reference to preferred embodiments, those skilled in the art will understand that various modifications and changes can be made to the invention without departing from the spirit and scope of the invention as described in the following claims. Explanation of the symbols

[0082] 100: Coal Transport Facility Monitoring System 110: Communications Department 120: Database 130: Data processing unit 140: Learning Model Section 150: Judgment Department

Claims

Claim 1 A coal handling facility monitoring system comprising: a communication unit for collecting field data including acoustic data and temperature data from a power plant coal handling facility; a data processing unit for classifying abnormal vibration types according to collected frequency characteristics through classification by conveyor belt operation status, classification by vibration data signal band, and classification by major failure type of the field data, and generating the classified data as training data; and a learning model unit for combining feature values ​​of the training data and calculating alarm range values ​​by vibration data signal band classification through learning. Claim 2 A coal transport facility monitoring system according to claim 1, further comprising a judgment unit that determines whether the coal transport facility is faulty by comparing the field data with the alarm range value according to the vibration data signal band classification. Claim 3 delete Claim 4 In claim 1, the classification by conveyor belt operation state includes any one of the following classifications: conveyor belt stop state, empty conveyor belt operation state for trial run, coal transport state, state going to stop operation, and conveyor belt stop state, in a coal transport facility monitoring system. Claim 5 In claim 1, the classification by vibration data signal band includes any one of the following: High state, Low state, Mid state, Variant state, and other states in a coal transport facility monitoring system. Claim 6 In paragraph 1, the classification by major failure type includes a coal transport facility monitoring system comprising any one of the following classifications: belt aging and breakage condition, idler support defect condition, and bearing defect condition. Claim 7 A method for monitoring a coal handling facility comprising: a communication unit collecting field data including acoustic data and temperature data from a power plant coal handling facility; a processing unit classifying abnormal vibration types according to collected frequency characteristics by classifying the field data by conveyor belt operation status, by vibration data signal band, and by major fault type, and generating the classified data as training data; and a learning model unit combining feature values ​​of the training data and then calculating alarm range values ​​by vibration data signal band classification through learning. Claim 8 A method for monitoring a coal transport facility according to claim 7, further comprising the step of a judgment unit determining whether the coal transport facility is faulty by comparing the field data with the alarm range value for each vibration data signal band classification. Claim 9 delete Claim 10 In claim 7, the above classification by conveyor belt operation state includes any one of the following classifications: conveyor belt stop state, empty conveyor belt operation state for trial run, coal transport state, state going to stop operation, and conveyor belt stop state. Claim 11 A method for monitoring a coal transport facility according to claim 7, wherein the classification by vibration data signal band includes any one of the following: High state, Low state, Mid state, Variant state, and other states. Claim 12 In claim 7, the above classification by major failure type includes a classification of any one of belt aging and breakage condition, idler support defect condition, and bearing defect condition. Claim 13 A computer-readable recording medium having a program recorded thereon for executing the method of any one of paragraphs 7, 8, 10 through 12 on a computer.

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