KR20210042928A - Abnormality monitoring device, abnormality monitoring method and abnormality monitoring program - Google Patents

Abstract

Provides abnormality monitoring technology that enables early detection of abnormalities due to structural destruction, decisive change, leakage or branching in various pipes of boilers, and objective judgment of occurrence of abnormalities. As a boiler piping abnormality monitoring device to detect the occurrence of abnormalities due to structural destruction, decisive change, leakage or branching of the boiler piping, it is a process data display unit that displays time series data of process data related to the status of the boiler piping, and on the boiler piping. And a sensor data display unit that displays time series data by a plurality of installed AE sensors.

Description

Abnormality monitoring device, abnormality monitoring method and abnormality monitoring program

The present invention relates to an abnormality monitoring device, an abnormality monitoring method, and an abnormality monitoring program.

Detects abnormalities such as structural destruction, decisive change, leakage or leakage of high-pressure steam or high-pressure water due to branching, by collecting the sound emitted from the boiler piping. There is a system to do it. In this system, a plurality of sound collecting devices capable of collecting an audible band and an ultrasonic band are installed on a collection target, and the sound pressure levels for each frequency band are compared from the obtained plurality of sound data to determine the occurrence of abnormalities such as leakage.

Patent Document 1: International Publication No. WO2013/136472

When abnormality is detected by sound emitted from a boiler pipe, the sound of structural destruction, decisive change, leakage or branching from the pipe is detected. Therefore, it is difficult to detect events early because there is no sound in the state before their events occur. Further, in the case of detection by sound, in addition to the sound of the event, it may be mixed as noise, making it difficult to objectively determine the occurrence of an abnormality.

One of the exemplary objects of an aspect of the present invention is to provide an abnormality monitoring technology that enables early detection and objective judgment of abnormalities due to structural destruction, decisive change, leakage or branching in various pipes of a boiler.

In order to solve the above problem, the abnormality monitoring device of one aspect of the present invention is an abnormality monitoring device of a boiler pipe for detecting an abnormality due to structural destruction, decisive change, leakage or branching of the boiler pipe. A data display unit that displays time series data related to a state, a sensor data display unit that displays time series data by a plurality of AE sensors installed in the boiler pipe, and the process data and the sensor data can be compared.

In this embodiment, since a plurality of AE (acoustic emission) sensors are installed in the boiler pipe and the data detected by the AE sensor is displayed, it is possible to detect a physical breakdown sound rather than an audible area. For this reason, it becomes possible to detect abnormalities in an early stage, including signs of branching. In addition, since data related to the condition of the boiler piping is displayed in addition to the data from the AE sensor, it is possible to provide in real time a plurality of judgment materials of the information obtained by the AE sensor and the information obtained from the data related to the condition of the boiler piping. I can. Therefore, it becomes possible to judge the occurrence of an abnormality early and objectively by the operator. However, the boiler piping here includes both a heat exchanger such as a heat exchanger and a pipe connecting the heat exchanger. In addition, the data related to the state of the boiler piping includes process data, as well as data such as field records, operation records, and alarm history.

Another aspect of the present invention is an abnormality monitoring method. This method is a method for monitoring an abnormality in a boiler pipe that detects occurrence of an abnormality due to structural destruction, decisive change, leakage or branching of the boiler pipe, comprising: a data display step displaying time series data related to the condition of the boiler pipe; and the boiler And a sensor data display step for displaying time series data by a plurality of AE sensors installed in the pipe, and the process data and the sensor data can be compared.

However, it is also effective as an aspect of the present invention to substitute any combination of the above constituent elements or constituent elements or expressions of the present invention among methods, apparatuses, systems, computer programs, data structures, recording media, etc. .

Advantageous Effects of Invention According to the present invention, early detection of abnormalities due to structural destruction, decisive changes, leakage or branching in various pipings of a boiler, and objective determination of occurrence of abnormalities becomes possible.

1 is an overall configuration diagram of an abnormality monitoring device and a boiler to be monitored according to the present embodiment.
Fig. 2 is a functional block diagram of the abnormality monitoring device of the present embodiment.
3 is an image diagram of data obtained from an AE sensor in the abnormality monitoring device of FIG. 1.
4 is a flowchart showing the procedure of the abnormality monitoring process of the abnormality monitoring device of FIG. 2.

Hereinafter, the present invention will be described with reference to the drawings on the basis of preferred embodiments (hereinafter referred to as "the present embodiment"). The same or equivalent constituent elements, members, and processes shown in each drawing are denoted by the same reference numerals, and appropriately redundant descriptions are omitted. In addition, the present embodiment is not intended to limit the invention, but is an illustration, and all features and combinations thereof described in the present embodiment are not necessarily limited to being essential of the invention.

1 and 2, the abnormality monitoring device 200 of the present embodiment and the boiler 100 as a monitoring target of the abnormality monitoring device 200 will be described.

1 is an overall configuration diagram of an abnormality monitoring device 200 and a boiler 100 to be monitored. The boiler 100 described herein is a circulating fluidized bed boiler, and is an apparatus for generating steam by burning fuel while circulating solid particles (circulating material, silica, etc.) flowing at a high temperature. In the boiler 100, a non-fossil fuel (wood biomass, waste tire, waste plastic, sludge, etc.) can be used as fuel. The steam generated by the boiler 100 is used, for example, to drive a power generation turbine. However, in this embodiment, a circulating fluidized bed boiler is described as an optimal example, and the present invention is not limited to this, and can be applied to other boilers.

The boiler 100 includes a furnace 101, a cyclone 102, a circulation recycling pipe 103, an exhaust gas passage 104, a steam drum 105, a superheater (superheater) 106, and , An economizer (economizer) 107 is provided. That is, in the boiler 100, the fuel is burned in the furnace 101, the circulating material is separated from the exhaust gas by the cyclone 102, and the separated solid particles are returned to the furnace 101 to be circulated. The separated circulating material is conveyed to the lower part of the furnace 101 via the circulation re-recovery pipe 103 connected below the cyclone 102. The exhaust gas from which the solid particles have been removed by the cyclone 102 passes through the exhaust gas flow path 104 connected to the downstream of the cyclone 102, and after a predetermined treatment is performed in an exhaust gas treatment device (not shown). , Discharged from the chimney. Further, when the exhaust gas passes through the exhaust gas flow path 104, heat is recovered and cooled by a superheater 106 that generates superheated steam and an economizer 107 that preheats the boiler feed water.

The furnace 101 is a combustion furnace for burning fuel, and an inlet 101a for inputting fuel, a fan 101b for supplying combustion air into the furnace 101, and exhaust gas generated by combustion are cyclone. A discharge port 101c for discharging to 102 is provided. Further, the furnace wall of the furnace 101 is constituted by a furnace wall pipe 111 for heating boiler feed water, and the furnace wall pipe 111 is connected to the steam drum 105.

The steam drum 105 is connected to a precipitation pipe 112, and the precipitation pipe 112 is connected to a furnace wall pipe 111. The boiler feed water in the steam drum 105 descends inside the precipitation pipe 112 and is introduced into the furnace wall pipe 111 from the lower side of the furnace 101. The boiler feed water in the furnace wall pipe 111 is heated by combustion of the furnace 101, and evaporates in the steam drum 105 to become steam.

A steam pipe 113 for discharging the steam inside is connected to the steam drum 105. The steam pipe 113 connects the steam drum 105 and the superheater 106. The steam in the steam drum 105 passes through the steam pipe 113 and is supplied to the superheater 106.

The superheater 106 generates superheated steam by heating the steam using the heat of the exhaust gas. The superheated steam passes through the discharge pipe 114 and is discharged to the outside of the boiler 100. The superheated steam is supplied to the power generation turbine and used for power generation.

The economizer 107 preheats the boiler feed water by transferring heat from the exhaust gas to the boiler feed water. The economizer 107 is connected to the steam drum 105 by a water supply pipe 115. The economizer 107 raises the steam temperature to about 300°C, and the steam of the heated boiler feed water passes through the feed water pipe 115 and is supplied to the steam drum 105.

The boiler 100 includes a deaerator 108 for removing dissolved oxygen from boiler feed water, and a boiler feed water supply pump 109 for supplying boiler feed water in the deaerator 108 to the economizer 107. ).

The deaerator 108 is connected to the economizer 107 through a boiler feed water supply pipe 116. The boiler feed water supply pump 109 is connected to the boiler feed water supply pipe 116, and the boiler feed water stored in the deaerator 108 is fed by the boiler feed water supply pump 109, and the boiler feed water supply pipe ( 116) It flows inside and is supplied to the economizer 107.

In the furnace wall pipe 111 of the furnace 101 of the boiler 100 and a liquid pipe such as the superheater 106, a plurality of AE (acoustic emission) sensors 120 (11 locations on the drawing) are provided. The AE sensor 120 is a sensor capable of detecting an acoustic wave (AE wave) generated in various pipings of the boiler 100. This AE wave has a high frequency component in the ultrasonic region, and by using the AE sensor 120, it is possible to detect in real time the deformation or destruction of the material around the installation location. More specifically, AE waves include not only sound waves or acoustic waves through solids such as pipes, but also sound waves or acoustic waves through fluids such as water or steam. It releases the strain energy that was accumulated inside. The AE sensor 120 is a sensor that detects this acoustic wave with a transducer installed on the surface of the material, and evaluates the destruction process of the material by performing signal processing. The AE sensor can evaluate without destroying the progress of cracks or frictional wear in various structures and facilities such as pressure vessels, dams, buildings, roads, airplanes, automobiles, etc., and, as in this embodiment, leakage of pipes, etc. Also in the present situation, an AE wave is generated, and the leakage of the pipe can be evaluated by detecting this by the AE sensor.

The abnormality monitoring device 200 is connected to the boiler 100 through a network, and process data that is time-series data of values related to the operating state of the entire plant including the boiler 100 and sensor data obtained from the AE sensor 120 Monitoring. The process data includes, for example, the pressure, temperature, or flow rate of the fluid in various pipings of the boiler 100.

2 is a functional block diagram of the abnormality monitoring device 200 of the present embodiment. The details of the abnormality monitoring device 200 will be described with reference to FIG. 2. The abnormality monitoring device 200 includes a process data acquisition unit 201, a process data selection unit 202, a process data display unit 203, a statistical processing unit 204, an abnormality determination unit 205, and a determination. A result display unit 206 is provided. The abnormality monitoring device 200 further includes a sensor data acquisition unit 211, a sensor data display unit 212, an abnormality detection unit 213, and a detection result display unit 214. The abnormality monitoring device 200 further includes a comparison data acquisition unit 221, a comparison unit 222, and a comparison result display unit 223.

The process data acquisition unit 201 includes a plurality of processes from a sensor or measuring device provided in a process system system such as a chemical plant or a power plant, that is, a pressure gauge, a thermometer or a flow meter provided in the boiler 100 in the present embodiment. Data is acquired as time series data. The process data selection unit 202 selects process data used for detecting a specific abnormality from among a plurality of process data acquired by the process data acquisition unit 201. For example, in order to detect the occurrence of a branch wave in the pipe of the boiler 100, operation data of the amount of water supply and the amount of discharge for the pipe of the boiler 100 are selected. More specifically, the amount of water supplied to the drum 105 through the water supply pipe 115 and the amount of water discharged as the discharge amount of the steam discharged from the discharge pipe 114 are selected. Thereby, the difference between the supply value of the fluid and the value discharged to the boiler 100 is calculated to determine the presence or absence of leakage of the fluid in the boiler piping.

The process data display unit 203 displays the process data selected by the process data selection unit 202, for example, data of the amount of water supply and discharge of the boiler 100 described above, as numerical data in a predetermined unit. Alternatively, real-time data of the amount of water supply and the amount of discharge are displayed on a screen as a graph, or the change over time in the amount of water supply and the amount of discharge is displayed as a graph. The above screen display is performed by displaying on a display device (not shown) in a state in which the operator who operates the abnormality monitoring device 200 can visually recognize.

The statistics processing unit 204 performs statistical processing on each of the process data selected by the process data selection unit 202, and supplies each process data after the statistical processing to the abnormality determination unit 205. For example, with respect to the difference between the water supply amount and the discharge amount calculated from the data of the water supply amount and the discharge amount of the pipe of the boiler 100, the difference is calculated by comparing with the average value of the difference in the past, or Compare the increase or decrease of

The abnormality determination unit 205 comprehensively evaluates each process data after statistical processing to determine whether the system is in a normal state or an abnormal state. It evaluates to what extent the time series pattern of each process data differs from the pattern at the normal time, and evaluates by adding a weighted value to the evaluation value indicating the degree of abnormality of each process data. For example, if the difference between the calculated water supply amount and the discharge amount of the boiler 100 exceeds the average value of the difference in the past, it is determined as an abnormal condition, or if the difference exceeds a preset threshold value for the difference, abnormality It is judged as the state. For this abnormality determination, for example, the abnormality is set to be assigned step by step according to the difference in advance, such as 1 to 5 or A to E, and if it is 5 or E, the abnormality may be increased. .

The determination result display unit 206 displays the determination result determined by the abnormality determination unit 205 on the screen. For example, in the case of a normal state, it is blue or green, and in the case of an abnormal state, it is colored red, and this character is displayed in blue along with a character such as "normal" on the monitor screen provided in the abnormality monitoring device 200. Alternatively, in the case of an abnormal condition, the corresponding character may be displayed in red along with the character "Abnormal Occurrence" on the monitor screen. Further, a lamp or the like may be provided at a position that can be easily recognized by the monitor screen or the operator around the monitor screen and displayed in blue or red. In addition, as described above, when the abnormality is set step by step, the level of 1 or A with low abnormality is set to blue, and toward the level of 5 or E with high abnormality, the color is set step by step so that it becomes red. You can also display it.

The sensor data acquisition unit 211 acquires time series data of acoustic waves from the plurality of AE sensors 120. The sensor data display unit 212 displays time series data of acoustic waves detected by the AE sensor 120 acquired by the sensor data acquisition unit 211. For example, as shown in Figs. 3A to 3B, waveform data is displayed with the horizontal axis as time and the vertical axis as decibels.

The abnormality detection unit 213 includes signs of deformation or destruction, etc., which cause branching in various pipings of the boiler from the data of the AE sensor 120 acquired by the sensor data acquisition unit 211, from the piping. Whether high-pressure steam or high-pressure water is leaked or not is judged.

Fig. 3 is a graph showing time series data of elastic waves obtained from a plurality of AE sensors 120a to 120c, and Fig. 3A shows a case where a change in elastic wave is detected in any of the three AE sensors, and Fig. Fig. 3(B) shows a case where a change in elastic wave is detected by one AE sensor. The abnormality detection unit 213 is, for example, as shown in FIG. 3, three AE sensors 120a to 120c are disposed in the vicinity of the superheater 106, respectively, and are disposed so that the ranges detectable by the sensors overlap with each other. In a case, as shown in Fig. 3A, when a change in elastic wave is detected in any of the three AE sensors, an abnormality is detected assuming that a branching wave has occurred. In addition, at this time, it is possible to estimate the location where the branching wave has occurred from the sensor from the difference in the intensity of the acoustic wave in the three sensors. In addition, as shown in Fig. 3B, when the change of the elastic wave is detected only by the sensor 120a, and the change of the elastic wave is not detected by the other two sensors 120b and the sensor 120c, In the range in which the sensor 120b and the sensor 120c can detect, the branching is not generated or it is determined that it is noise. In addition, the abnormality detection is set to be assigned by preparing and assigning a standard step by step according to the intensity of the detected acoustic wave, such as 1 to 5 or A to E, for example, as in the abnormality determination unit 205, and 5 or If it is E, the ideal degree can be increased.

The detection result display unit 214 displays the result detected by the abnormality detection unit 213 on the screen. For example, when the occurrence of a branch wave from a pipe is detected, as shown in Fig. 3A, a message informing of an abnormality such as "offset occurrence" is displayed in the waveform data. In addition, in the case of detection by one sensor 120a as shown in FIG. 3B, and when the other sensor 120b or 10c does not detect the acoustic wave, no special display is performed or the sensor 120a Only the waveform of is displayed in red to indicate the alarm status. In addition, it is not limited to the above-described display on the screen, and it is also possible to notify an abnormality by sound, such as an alarm sound. In addition, when the abnormality is set step by step as above, the level of 1 or A with low abnormality is set to blue, and when the level of 5 or E with high abnormality is set to red, the color is set and displayed in stages. You may.

The comparison data acquisition unit 221 acquires process data and sensor data for comparison from the abnormality detection unit 213 and the abnormality determination unit 205. For example, data on the presence or absence of the difference between the water supply amount and the discharge amount of the boiler obtained from the process data from the abnormality detection unit 213, and the AE sensor 120 obtained from the sensor data from the abnormality determination unit 205 Acquire data on whether or not an acoustic wave is detected. In another aspect, the comparison data acquisition unit 221 includes data relating to the presence or absence of detection of the occurrence of branching obtained from the process data, and data relating to the presence or absence of detection of an acoustic wave obtained by the AE sensor 120 obtained from the sensor data. Acquire.

The comparison unit 222 makes the above determination from the process data and sensor data acquired by the comparison data acquisition unit 221. For example, when the difference between the water supply amount and the water discharge amount in the boiler 100 as process data is "Yes", and the presence or absence of detection of the acoustic wave acquired by the AE sensor 120 is "Yes", the boiler 100 In the case of "no", the occurrence of the branch is determined as "no", and if one is "yes" and the other is "no", it is determined that leakage due to the branch is occurring. It is determined that there is a possibility of leakage.

In another aspect, the comparison unit 222, when the detection of the occurrence of the branch wave as the process data is "Yes" and the acoustic wave acquired by the AE sensor 120 is "Yes", the pipe of the boiler 100 is Occurrence of leakage due to branching is determined, and if all are "no", the occurrence of branching in the piping of the boiler 100 is determined as "no", and one is "yes" and the other is "no". In the case of, it is determined that there is a possibility of occurrence of a branching.

In addition, by adding a weight value to the output result of the process data and sensor data according to the setting, for example, even when one of the process data and the sensor data is "Yes" and the other is "None", the AE sensor 120 With regard to the determination result of, when the detection of branch wave generation as process data is “Yes” and the elastic wave acquired by the AE sensor 120 is “No”, the generation of branch wave in the piping of the boiler 100 is If it is determined as "no", and the detection of branch wave generation as process data is "no" and the acoustic wave acquired by the AE sensor 120 is "yes", leakage due to the branch wave in the boiler 100 Occurrence of may be determined as "possible". By being able to set the weighting value to the output result as described above, it is possible to make a more accurate ideal judgment.

In addition, in the abnormality determination unit 205 and the abnormality detection unit 213, when the abnormality is determined or detected in stages such as 1 to 5, the abnormality is equal to or greater than "8" by adding the abnormality of the process data and sensor data. It may be determined that there is a possibility of occurrence of branching when the preset reference value is exceeded, such as a case. Accordingly, it is possible to judge the possibility of occurrence of a branching in a complex manner based on a certain standard from process data and sensor data. In addition, a difference may be provided in the degree of abnormality for determining the possibility of occurrence of branching using the process data and the sensor data. For example, if the process data has an abnormality of "4" or "D" or more, and the sensor data is determined that there is a possibility of occurrence of a branching when the abnormality is "3" or "C" or more. In this way, it is possible to make a more accurate abnormality determination by making the reference value of the probability of occurrence of the branching determined from the abnormality different from the process data and the sensor data.

The comparison result display unit 223 displays the judgment result determined by the comparison unit 222 on the screen so that the operator can visually recognize it. For example, when it is determined that there is no occurrence of a branch, the text “No branch occurs” is displayed in blue, and when it is estimated that there is a branch, the character “Offset occurs” is displayed in red. can do. In addition, in the case of a determination that there is a possibility of occurrence of a branch, the character "Possibility of occurrence of a branch" can be displayed in orange. In addition, a lamp or the like is simply provided on the monitor screen or at a position where the operator around it can visually recognize and display it in blue, red, or orange colors. In this case as well, it is not limited to the above-described display on the screen, and it is also possible to notify the occurrence of a branch wave by sound, such as an alarm sound.

In the abnormality monitoring device 200 having the above configuration, the process data display unit 203 displays the process data on the screen, and the sensor data display unit 212 displays time series data of the acoustic waves detected by the plurality of AE sensors 120. Indicate. At this time, the process data display unit 203 may display, among a plurality of process data, process data used for detection of branching of the pipe or a plurality of them. In the case of displaying only the process data used to detect the occurrence of branching of a pipe, information on the presence or absence of branching can be highlighted and displayed, allowing the operator to reliably inform the presence or absence of a branching occurrence, and to detect the occurrence of branching in the pipe. By displaying a plurality of process data other than to be used, the operator can check other information at the same time, and there is a merit in any case.

Thereby, the operator can obtain data on the presence or absence of leakage in the piping obtained by the plurality of AE sensors 120, and information on the leakage of piping in the process data and the like. That is, the operator can obtain a plurality of judgment materials for abnormalities caused by the occurrence of branching of various pipings of the boiler 100. In this way, since the operator can judge the occurrence of the branching from the plurality of judgment materials of the process data and the data detected by the sensor, it is possible to objectively determine the occurrence of the branching. That is, in the case that any one of the process data and the data detected by the sensor is due to an error, the operator can compare a plurality of data, thereby enabling a more accurate judgment.

In addition, the AE sensor 120 enables the operator to detect a physical breakdown sound rather than an audible region, and thus detects elastic waves of various pipings of the boiler 100. Thereby, since the operator can detect the deformation energy of the pipe, such as the occurrence of deformation or cracking of the pipe, as a change over time, it is possible to grasp the sign of the occurrence of the branching, so that early detection of the occurrence of the branching is possible. In addition, since the AE sensor 120 is arranged so as to overlap with each other, the AE sensor 120 is in the vicinity of the boiler piping, and the detection result of the plurality of sensors is combined to determine the occurrence of a branching wave. Therefore, the detection accuracy can be improved.

Since the comparison unit 222 performs abnormal determination from the process data and sensor data, and the comparison result display unit 223 displays the determination result determined by the comparison unit 222 on the screen, the operator It is possible to obtain information on the occurrence of the branch obtained from the two data of.

At this time, the comparison unit 222 compares, as process data, information about the difference between the amount of water supply and the amount of discharge in various pipes of the boiler 100 and information about whether or not the acoustic wave acquired by the AE sensor 120 is detected. do. That is, the operator can obtain information on the occurrence of branching from two indicators: the amount of water in the various pipings of the boiler 100 and the occurrence of a physical breakdown sound related to leakage from the piping. The comparison unit 222 also compares, as process data, information on the detection of branch wave generation and information on elastic waves from various pipings of the boiler 100 acquired by the AE sensor 120. Also in this case, the operator can obtain information on the occurrence of a branch from two indexes of process data and sensor data. In this way, the operator can obtain highly reliable information regarding the occurrence of the branching.

Further, since the comparison result display unit 223 displays the determination result determined by the comparison unit 222 on the screen, the operator can visually grasp the occurrence of the branch.

4 is a flowchart showing the procedure of an abnormality monitoring process by the abnormality monitoring device 200. As shown in FIG.

The process data display unit 203 displays the process data selected by the process data selection unit 202 on a screen (S11). The sensor data display unit 212 displays the time series data of the AE sensor acquired by the sensor data acquisition unit 211 on the screen (S12). The comparison data acquisition unit 221 compares the process data and the sensor data (S13).

The comparison unit 222 determines a comparison result from the process data and sensor data compared in the comparison data acquisition unit 221 (S14). When the comparison unit 222 determines the occurrence of the branch wave when both the process data regarding the presence or absence of the detection of the branch wave occurrence and the data regarding the presence or absence of the elastic wave acquired by the AE sensor are present (S15) YES), the determination result display unit 215 displays the detection result detected by the abnormality detection unit 213 on the screen (S17). If the comparison unit 222 determines that there is no abnormality (NO in S15), then the comparison unit 222 determines whether there is a risk of abnormality (S16). When there is a concern about the above (YES in S16), the determination result display unit 215 displays the determination result on the screen (S17). If it is determined that there is no abnormality (NO in S16), the process returns to S11 and the subsequent processing is repeated.

According to the abnormality monitoring device 200 of this embodiment described above, since a plurality of AE (acoustic emission) sensors are installed in the boiler pipe and the data detected by the AE sensor is displayed, physical destruction rather than an audible area Negative detection becomes possible. For this reason, it becomes possible to detect an abnormality at an early stage, including a sign of occurrence of branching. Further, since process data is displayed in addition to the data by the AE sensor, it is possible to provide a plurality of judgment materials of the information obtained by the AE sensor and the information obtained by the process data. Therefore, it is possible to expect an objective judgment of occurrence of an abnormality by the operator.

However, the above embodiments are intended to facilitate understanding of the present invention, and are not intended to limit and interpret the present invention. Each element included in the embodiment and its arrangement, material, condition, shape, size, and the like are not limited to those illustrated and can be appropriately changed. Moreover, it is possible to partially substitute or combine the structures shown in another embodiment. For example, in the above embodiment, the occurrence of abnormalities due to branching of a boiler pipe has been described, but the present invention is not limited to this, and can be used for detection of occurrence of abnormalities due to structural destruction, decisive changes, or leakage. . Further, as data related to the state of the boiler piping, process data has been described as an example, but data such as field records, operation records, and alarm history may be used, not limited to process data.

For example, the sensor data display unit 212 and the process data display unit 203 may be configured by dividing the display screen, or may be displayed on a single screen, and may be displayed by overlapping them. When the sensor data display unit 212 and the process data display unit 203 are separately displayed, it is easy for the operator to grasp that each display is output with two different indicators of process data and sensor data. On the other hand, when the sensor data display unit 212 and the process data display unit 203 are displayed on one screen, or when they are superimposed and displayed, the operator can easily compare both data.

In the same manner as described above, the abnormality determination unit 205 and the abnormality detection unit 213 may be configured as the same processing means on the same terminal or as different processing means on different terminals. By configuring it as the same processing means on the same terminal, it is possible to simplify the data processing configuration, while configuring it as another processing means on another terminal makes it possible to configure the terminal that performs data processing separately. In the same manner as above, the detection result display unit 214 and the determination result display unit 206 may be configured as the same processing means on the same terminal or as different processing means on different terminals. The same effect can be expected.

100 boiler
101 brazier
102 cyclone
103 Circulation Recollection Hall
104 exhaust gas flow path
105 Steam Drum
106 Superheater
107 Economizer
108 Deaeration
109 Boiler feed water supply pump
111 Furnace Hall
112 River Water Pipe
113 Steam piping
114 discharge piping
115 Water supply pipe
116 Boiler water supply piping
120 sensors
Over 200 monitoring devices
201 Process Data Acquisition Department
202 Process data selection unit
203 Process data display
204 Statistics processing unit
205 or above
206 Judgment result display section
211 Sensor data acquisition unit
212 Sensor data display
213 Abnormality detection unit
214 detection result display
215 Judgment result display section
221 Comparison data acquisition unit
222 Comparison
223 Comparison result display

Claims (7)

As an abnormality monitoring device for boiler pipes to detect the occurrence of abnormalities due to structural destruction, decisive change, leakage or branching of boiler pipes,
A process data display unit that displays time series data of process data related to the state of the boiler piping;
A sensor data display unit for displaying time series data by at least one AE sensor installed in the boiler pipe,
An abnormality monitoring device configured to be able to compare the process data and the sensor data. The method of claim 1,
A comparison unit for comparing data on the presence or absence of structural destruction, decisive change, leakage or branching of the boiler pipe detected from the process data and the sensor data;
An abnormality monitoring device comprising a comparison result display unit for displaying a comparison result of the data regarding the presence or absence of the data. The method of claim 2,
The comparison result display unit, when determining an abnormal state from the comparison result, displays the result. The method of claim 1,
The process data includes data on whether the boiler pipe is branched or not,
The comparison unit is an abnormality monitoring device for comparing the presence or absence of branching of the boiler pipe and the detection of an elastic wave detected by the AE sensor. The method of claim 1,
The process data includes data on an amount of water supply to the boiler pipe and a discharge amount from the boiler pipe,
The comparison unit is an abnormality monitoring device that compares the presence or absence of a difference between the water supply amount and the drainage amount and the detection of an acoustic wave detected by the AE sensor. As a method for monitoring abnormality in boiler pipes that detects the occurrence of abnormalities due to structural destruction, decisive change, leakage or branching of boiler pipes,
A process data display step for displaying time series data of process data related to the state of the boiler piping;
A sensor data display step for displaying time series data by a plurality of AE sensors installed in the boiler pipe,
An abnormality monitoring method capable of comparing the process data and the sensor data. As a boiler pipe abnormality monitoring program that detects the occurrence of abnormalities due to structural destruction, decisive change, leakage or branching of boiler pipes,
The above program is on the computer,
A process data display step for displaying time series data of process data related to the state of the boiler piping;
A sensor data display step for displaying time series data by a plurality of AE sensors installed in the boiler pipe is executed,
An abnormality monitoring program in which the process data and the sensor data can be compared.

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