TW202030565A - Abnormality monitoring device, abnormality monitoring method, and abnormality monitoring program - Google Patents

Abstract

Provided is an abnormality monitoring technique which enables early detection of an abnormality in a pipe of a boiler relating to structural damage, crystalline changes, leaks, or bursting, as well as an objective determination of the occurrence of such abnormality. Provided is an abnormality monitoring device for boiler piping serving to detect the occurrence of an abnormality relating to structural damage, crystalline changes, leaks, or bursting in the boiler piping, said device comprising: a process data display part for displaying time-series data comprising process data relating to the condition of the boiler piping; and a sensor data display part for displaying time-series data based on outputs from a plurality of AE sensors disposed in the boiler piping.

Description

Abnormal monitoring device, abnormal monitoring method and abnormal monitoring program

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

There is a system that detects abnormalities such as high-pressure steam or high-pressure water leakage caused by structural destruction, crystal change, leakage, or bursting of various pipes of the boiler by collecting sounds emitted from boiler pipes. In this system, a plurality of radio devices capable of collecting audible frequency bands and ultrasonic frequency bands are installed in the radio target, and the sound pressure levels of each frequency band are compared from the obtained sound data to determine the occurrence of abnormalities such as leakage. (Prior technical literature) (Patent Document) Patent Document 1: International Publication No. WO2013/136472

(Problems to be solved by the present invention) In the case of abnormality detection by the sound emitted from the boiler piping, the sound generated by structural destruction, crystal change, leakage or bursting from the piping is detected. Therefore, in the state before these phenomena occur, since no sound is produced, it is difficult to detect the phenomena early. In addition, in the case of sound detection, in addition to the sound of the phenomenon, noise is mixed, so it is difficult to objectively judge the occurrence of abnormality. One of the illustrative purposes of one aspect of the present invention is to provide an abnormality monitoring technology that can early detect and objectively determine abnormalities caused by structural failure, crystal change, leakage or burst in various piping of the boiler. (Means to solve the problem) In order to solve the above-mentioned problems, an abnormality monitoring device of one aspect of the present invention is an abnormality monitoring device for boiler piping that detects the occurrence of abnormalities caused by structural destruction, crystal change, leakage or bursting of boiler piping. The device is configured to be able to compare: the process data display part displays time series data related to the state of the boiler piping; the sensor data display part displays the sensing based on a plurality of AE sensors installed in the boiler piping Timing data of the sensor data; the aforementioned process data; and the aforementioned sensor data. In this aspect, a plurality of AE (Acoustic Emission) sensors are installed in the boiler piping, and the data detected by the AE sensors are displayed, so it is possible to detect physical destruction sounds that are not in the audible area. Therefore, it is possible to detect abnormalities including burst signs early. Moreover, in addition to the data based on the AE sensor, it also displays the data about the piping status of the boiler, so it can provide multiple judgment materials of the information obtained by the AE sensor and the information obtained from the data about the piping status of the boiler in real time. Therefore, the operator can judge the occurrence of abnormality early and objectively. In addition, the "boiler piping" referred to here includes the part that performs heat exchange such as a heat exchanger, or all the piping connecting the part. In addition, as the data about the piping status of the boiler, the process data is the first, and it also includes on-site records, action records, alarm history and other data. Another aspect of the present invention is an abnormality monitoring method. This method is an abnormal monitoring method for boiler piping that detects abnormal occurrences of boiler piping caused by structural failure, crystal change, leakage or bursting. The abnormal monitoring method includes: a process data display step to display the process related to the aforementioned boiler piping status The timing data of the data; and the sensor data display step, displaying the timing data based on the sensor data of the plurality of AE sensors installed in the boiler piping, and can compare the process data with the sensor data . In addition, any combination of the above constitutional requirements, constitutional requirements and expressions of the present invention, alternatives among methods, devices, systems, computer programs, data structures, and recording media, are also effective as aspects of the present invention. (Effect of Invention) According to the present invention, an abnormality caused by structural breakdown, crystal change, leakage or burst in various piping of the boiler can be detected early, and the occurrence of abnormality can be judged objectively.

Hereinafter, the present invention will be described based on a preferred embodiment (hereinafter referred to as "this embodiment") with reference to the drawings. The same or equivalent constituent elements, components, and processes shown in the various drawings are labeled with the same symbols, and repeated descriptions are appropriately omitted. In addition, this embodiment is an illustration, and is not intended to limit the inventor, and all the features or combinations of the features described in this embodiment are not necessarily the essence of the invention. 1 and 2, the abnormality monitoring device 200 of this embodiment and the boiler 100 that is the monitoring target of the abnormality monitoring device 200 will be described. Fig. 1 is an overall configuration diagram of an abnormality monitoring device 200 and a boiler 100 as a monitoring target. Here, the boiler 100 described is a circulating fluidized bed boiler, which is a device that circulates solid particles (circulating material, silica sand, etc.) flowing at a high temperature while burning fuel to generate steam. In the boiler 100, as a fuel, for example, non-fossil fuels (woody biomass, waste tires, waste plastics, sludge, etc.) can be used. The steam generated in the boiler 100 is used, for example, to drive a power generating turbine. In addition, in the present embodiment, the circulating fluidized bed boiler is described as the best embodiment, and the present invention is not limited to this, and it can be applied to other boilers. The boiler 100 includes a furnace 101, a cyclone 102, a circulating material recovery pipe 103, an exhaust gas flow path 104, a steam drum 105, a super heater 106 and an economizer 107. That is, in the boiler 100, fuel is burned in the furnace 101, and the circulating material is separated from the exhaust gas by the cyclone 102, so that the separated solid particles return to the furnace 101 and circulate. The separated circulating material is sent back to the lower part of the furnace 101 through the circulating material recovery pipe 103 connected to the lower part of 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 downstream of the cyclone 102 and is subjected to a predetermined treatment by an exhaust gas treatment device (not shown) before being discharged from the chimney. When the exhaust gas passes through the exhaust gas flow path 104, the heat is recovered and cooled by the superheater 106 that generates superheated steam and the economizer 107 that preheats the boiler water supply. The furnace 101 is a combustion furnace that burns fuel. It is provided with a fuel input port 101a, a fan 101b for supplying combustion air into the furnace 101, and exhaust gas generated by combustion to the cyclone 102. Discharge port 101c. In addition, the furnace wall of the furnace 101 is composed of a furnace wall pipe 111 for heating the boiler water supply, and the furnace wall pipe 111 is connected to the steam drum 105. The steam drum 105 is connected with a downpipe 112 which is connected to the furnace wall pipe 111. The boiler water supply in the steam drum 105 descends in the downfall pipe 112 and is introduced into the furnace wall pipe 111 at the lower side of the furnace 101. The boiler feed water in the furnace wall tube 111 is heated by the combustion of the furnace 101 and evaporates in the steam drum 105 to become steam. The steam drum 105 is connected to a steam pipe 113 for discharging internal steam. The steam pipe 113 connects the steam drum 105 and the superheater 106. The steam in the steam drum 105 passes through the steam piping 113 and is supplied to the superheater 106. The superheater 106 uses the heat of the exhaust gas to heat the steam to generate superheated steam. The superheated steam passes through the inside of 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 transfers the heat of the exhaust gas to the boiler water supply to preheat the boiler water supply. The economizer 107 is connected to the steam drum 105 through a water supply pipe 115. The economizer 107 raises the temperature of the steam to approximately 300° C., and the steam supplied by the heated boiler is supplied to the steam drum 105 through the water supply pipe 115. The boiler 100 includes a deaerator 108 for removing dissolved oxygen in the boiler water supply, and a boiler water supply pump 109 for supplying the boiler water supply in the deaerator 108 to the economizer 107. The deaerator 108 is connected to the economizer 107 through the boiler water supply pipe 116. A boiler water supply pump 109 is connected to the boiler water supply pipe 116, and the boiler water supply stored in the deaerator 108 is sent by the boiler water supply pump 109 and flows through the boiler water supply pipe 116 to be supplied to the economizer 107. The liquid piping such as the furnace wall pipe 111 and the superheater 106 of the furnace 101 of the boiler 100 is provided with a plurality of (11 places in the drawing) AE (acoustic emission) sensors 120. The AE sensor 120 is a sensor capable of detecting elastic waves (AE waves) generated in various pipes of the boiler 100. The AE wave has high-frequency components in the ultrasonic region, and by using the AE sensor 120, the deformation or destruction of the material around the installation site can be detected in real time. More specifically, AE waves include not only sound waves or elastic waves passing through solids such as pipes, but also sound waves or elastic waves passing through fluids such as water or steam. When the material deforms or cracks in the material, the material releases and accumulates in Internal strain energy. The AE sensor 120 is a sensor that detects the elastic wave with a transducer placed on the surface of the material and evaluates the destruction process of the material by signal processing. The AE sensor will not damage pressure vessels, dams, buildings, roads, airplanes, automobiles and other structures, equipment, such as cracks or friction and wear, so it can be evaluated. Like this embodiment, leakage in piping, etc. An AE wave is also generated in the phenomenon, and the leak of the pipe can be evaluated by detecting the AE wave by the AE sensor. The abnormality monitoring device 200 is connected to the boiler 100 via a network, and monitors the time series data about the value of the operating state of the entire plant including the boiler 100, that is, process data, and the sensor data obtained from the AE sensor 120 . The process data includes, for example, the pressure, temperature, or flow rate of fluids in various pipes of the boiler 100. Fig. 2 is a functional block diagram of the abnormality monitoring device 200 of this embodiment. 2, the details of the abnormality monitoring device 200 will be described. 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 result display unit 206. 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 acquires from a sensor or measuring device installed in a process system such as a chemical plant or a power plant, that is, a pressure gauge, thermometer, or flow meter installed in the boiler 100 in this embodiment A plurality of process data are used as time series data. The process data selection unit 202 selects the process data used for specific abnormality detection from the plurality of process data acquired by the process data acquisition unit 201. For example, in order to detect the occurrence of a burst in the piping of the boiler 100, the operation data of the water supply amount and the drainage amount to the piping of the boiler 100 are selected. More specifically, the amount of water supplied to the steam drum 105 through the water supply pipe 115 and the discharge amount of the steam discharged from the discharge pipe 114 are selected. In this way, the difference between the supply value and the discharge value of the fluid to the boiler 100 is calculated to determine whether the fluid in the boiler piping leaks. If it is the process data selected by the process data selection unit 202, such as the above-mentioned water supply and water discharge data of the boiler 100, the process data display unit 203 will display the equivalent amount as numerical data in a predetermined unit, and, The real-time data of the water supply volume and the water discharge volume are displayed on the screen as a graph, and further, the temporal changes in the water supply volume and the water discharge volume are displayed on the screen as a graph. The above screen display is performed by displaying it on a display device not shown in a state visible by the operator performing the operation of the abnormality monitoring device 200. The statistical processing unit 204 performs statistical processing on each process data selected by the process data selection unit 202, and supplies each process data after statistical processing to the abnormality determination unit 205. For example, the difference between the water supply volume and the water discharge volume calculated from the data of the water supply volume and the water discharge volume of the boiler 100 is compared with the average of the past differences to calculate the deviation, or the difference is compared with the relevant preset difference The increase or decrease between the thresholds. 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. Evaluate how the timing pattern of each process data is different from the normal pattern, and comprehensively evaluate and judge by adding weighted evaluation values that indicate the degree of abnormality of each process data. For example, when the difference between the calculated water supply amount and the drainage amount of the boiler 100 exceeds the average value of the past difference, it is judged as an abnormal state, and when the difference exceeds the threshold value for the preset difference, it is judged as abnormal. status. In this abnormality determination, for example, the standard is set step by step according to the difference in advance, and the abnormality degree is set to 1 to 5 or A to E in a distributed manner. It can also be set to 5 or E, which means high abnormality. . The determination result display unit 206 displays the determination result determined by the abnormality determination unit 205 on the screen. For example, it is set to blue or green in the case of a normal state, and it is set to red in the case of an abnormal state. It can also be set to display "normal" on the monitor screen installed in the abnormality monitoring device 200 The character is displayed in blue, or, conversely, in the case of an abnormal state, the character "abnormality occurred" is displayed on the monitor screen and the character is displayed in red. Furthermore, it is possible to set a light or the like only on the monitor screen or a location visible by the operator around it, and display it in blue or red. Also, as described above, when the degree of abnormality is gradually set, the low abnormality level 1 or A stage is set to blue, and when the abnormality degree is high 5 or E stage, it can gradually become red. Set the color and display it. The sensor data acquisition unit 211 acquires time series data of elastic waves from a plurality of AE sensors 120. The sensor data display part 212 displays the time series data of the elastic waves detected by the AE sensor 120 acquired by the sensor data acquisition part 211. For example, as shown in Figure 3 (A) to (B), the horizontal axis is set to time and the vertical axis is set to decibels to display waveform data. The abnormality detection unit 213 judges from the data of the AE sensor 120 acquired by the sensor data acquisition unit 211, including signs such as deformation or breakage of various pipes of the boiler, as well as from the piping. Whether there is leakage of high-pressure steam or high-pressure water, etc. Fig. 3 is a graph showing the time series data of elastic waves obtained from a plurality of AE sensors 120a to 120c, and Fig. 3(A) shows the change of elastic wave detected in any of the three AE sensors In this case, Fig. 3(B) shows the situation where the elastic wave is detected in one AE sensor. For example, as shown in FIG. 3, in the case where three AE sensors 120a to 120c are respectively arranged near the superheater 106 and are arranged so that the detectable range of the sensor overlaps, as shown in FIG. 3(A) It is shown that when the change of the elastic wave is detected in any of the three AE sensors, the abnormality detection unit 213 detects the abnormality that a burst has occurred. Also, at this time, since the elastic waves in the three sensors have different intensities, the sensor can estimate the location where the burst has occurred. Also, as shown in FIG. 3(B), when only the sensor 120a detects the elastic wave change, and the other two sensors 120b and the sensor 120c do not detect the elastic wave change The judgment is made as follows: no burst or noise occurs within the detectable range of the sensor 120b and the sensor 120c. Also, in this abnormality detection, similar to the abnormality determination unit 205, for example, a reference is gradually set based on the intensity of the detected elastic wave, and the degree of abnormality is set to 1 to 5 or A to E in a distributed manner, etc. Can be set to 5 or E for high abnormality. 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 burst from a pipe is detected, as shown in FIG. 3(A), a message indicating an abnormality such as "A burst occurred" is displayed in the waveform data. In addition, as shown in Figure 3(B), when one sensor 120a detects elastic waves, and the other sensors 120b or 120c do not detect elastic waves, no special display is performed, or only for the sensor The 120a waveform is displayed in red to show the alarm status. Moreover, it is not limited to the display on the screen as described above. For example, like an alarm sound, an abnormality can also be notified by sound. Also, as described above, when the degree of abnormality is gradually set, the low abnormality level 1 or A stage is set to blue, and when the abnormality degree is high 5 or E stage, it can gradually become red. Set the color and display it. 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, the abnormality detection unit 213 obtains the information about the difference between the boiler water supply and the drainage volume obtained from the process data, and the abnormality determination unit 205 obtains the relevant presence or absence detection obtained from the sensor data by the AE sensor 120 elastic wave data obtained. Also, as another aspect, the comparison data acquisition unit 221 acquires: the data on the presence or absence of detection of bursts obtained from the process data and the data on the presence or absence of detection of the elastic waves obtained by the AE sensor 120 obtained from the sensor data. data. The comparison unit 222 judges the abnormality based on the process data and sensor data acquired in the comparison data acquisition unit 221. For example, as the process data, when the difference between the water supply and the water discharge in the boiler 100 is "Yes", and the detection of the elastic wave obtained by the AE sensor 120 is "Yes", it is determined that the boiler In the case of 100 leaks caused by bursts, all of them are "None", the occurrence of bursts is judged as "None", and if one of them is "Yes" and the other is "None", it is judged to be present The possibility of leakage caused by a burst. Also, as another aspect, as the process data, when the detection of the occurrence of a burst is "Yes" and the elastic wave obtained by the AE sensor 120 is "Yes", the comparison unit 222 determines that the occurrence of the boiler If the leak caused by the burst in the piping of the boiler 100 is "None", it is judged that the occurrence of the burst in the piping of the boiler 100 is "None", and the case is "Yes" on one side and "None" on the other. Next, it is determined that there is a possibility of explosion. Furthermore, the output results of the process data and sensor data are weighted by setting, so that, for example, even if one of the process data and sensor data is "Yes" and the other is "None" Next, we also attach importance to the determination result in the AE sensor 120. As the process data, when the detection of burst occurrence is "Yes" and the elastic wave obtained by the AE sensor 120 is "No", the boiler 100 is determined The occurrence of burst in the piping is "None". On the other hand, when the detection of burst occurrence as process data is "None" and the elastic wave obtained by the AE sensor 120 is "Yes", it can The occurrence of a leak caused by a burst in the boiler 100 is determined as "possible." Since it is possible to set the weight of such output results, it is possible to make more accurate abnormal judgments. In addition, in the abnormality determination unit 205 and the abnormality detection unit 213, when the abnormality degree is gradually determined or detected as 1 to 5, etc., the abnormality degree of the process data and the sensor data are combined to make the abnormality degree "8". "In the above cases, etc., if it exceeds the preset reference value, it can be determined that there is a possibility of explosion. In this way, the possibility of bursting can be comprehensively judged from the process data and sensor data and based on certain benchmarks. In addition, it is possible to set a difference in the degree of abnormality that determines the possibility of bursting based on the process data and the sensor data. For example, when the abnormality degree in the process data is "4" or higher, and the abnormality degree in the sensor data is "3" or higher, it is determined that there is a possibility of explosion Sex. Since it is possible to set the reference value of the probability of occurrence of bursting determined by the abnormality degree to be different in the process data and the sensor data, a more accurate abnormality judgment can be made. The comparison result display unit 223 displays the determination result determined by the comparison unit 222 on the screen so that the operator can see it. For example, when it is determined that a burst has not occurred, the text "No burst has occurred" can be displayed in blue, and when it is estimated that a burst has occurred, the text "A burst has occurred" can be displayed in red. In addition, when it is determined that there is a possibility of bursting, the text "There is a possibility of bursting" etc. can be displayed in orange. Furthermore, a lamp or the like is provided only in a position visible to the operator on the monitor screen or its periphery, and the display is displayed in blue, red, or orange. In this case, it is not limited to the display on the screen as described above. For example, like an alarm sound, it is also possible to notify the occurrence of a burst by sound. In the abnormality monitoring device 200 configured as above, the process data display unit 203 displays the process data on a screen, and the sensor data display unit 212 displays time series data of elastic waves detected by a plurality of AE sensors 120. At this time, the process data display unit 203 can display a plurality of process data, among which the process data used in the detection of pipe burst occurrence, may also display a plurality of process data. In the case of displaying only the process data used in the detection of the occurrence of piping bursts, it is possible to emphasize the display of information on the occurrence of bursts, and to reliably inform the operator of the occurrence of bursts, and by displaying multiple occurrences except for the occurrence of piping In the burst detection process data other than the user, the operator can also confirm other information at the same time, which is beneficial to any situation. In this way, the operator can obtain information about the presence or absence of leakage in the piping obtained from the plurality of AE sensors 120, and information about the leakage of the piping in the process data. That is, regarding the abnormality caused by the occurrence of bursts of various pipes of the boiler 100, the operator obtains a plurality of judgment materials. In this way, the operator can judge the occurrence of the explosion from the multiple judgment materials of the process data and the data detected by the sensor, and therefore can objectively judge the occurrence of the explosion. That is, in the case where either of the process data and the data detected by the sensor is based on the false alarm, the operator can compare a plurality of data, and therefore can make a more accurate judgment. In addition, the operator can detect the physical destruction sound not in the audible area by the AE sensor 120, and can detect the elastic wave of various pipes of the boiler 100. With this, the operator can detect the strain energy of the pipe such as the occurrence of pipe deformation or cracking as a change over time, and therefore can grasp the signs of the occurrence of bursts, and therefore can detect the occurrence of bursts early. In addition, a plurality of AE sensors 120 are arranged near the boiler piping, and the detectable ranges of the sensors are arranged in such a way that the detection ranges of the sensors overlap with each other. Therefore, the detection result of the elastic wave of the plurality of sensors is integrated to determine the burst Therefore, the detection accuracy can be improved. The comparison part 222 judges the abnormality based on the process data and the sensor data, and the comparison result display part 223 displays the judgment result determined by the comparison part 222 on the screen, so the operator can obtain the two data from the process data and the sensor data. Information about the occurrence of a burst from the data. At this time, the comparison unit 222 compares the information about the difference between the water supply amount and the water discharge amount in the various pipes of the boiler 100 as the process data, and the information about whether the elastic wave acquired by the AE sensor 120 has been detected. That is, the operator can obtain information about the occurrence of bursts from two indicators of the amount of water in various pipes of the boiler 100 and the occurrence of physical damage sound related to leakage from the pipes. The comparison unit 222 also compares the information related to the detection of the occurrence of bursts as process data and the information related to the elastic waves from various pipes of the boiler 100 acquired by the AE sensor 120. In this case, information about the occurrence of burst can also be obtained from the two indicators of process data and sensor data. In this way, the operator can obtain highly reliable information about the occurrence of a burst. In addition, the comparison result display unit 223 displays the determination result determined by the comparison unit 222 on the screen, so the operator can visually grasp the occurrence of the burst. FIG. 4 is a flowchart showing an abnormality monitoring processing sequence based on the abnormality monitoring device 200. 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 a screen (S12). The comparison data acquisition part 221 compares the process data and the sensor data (S13). The comparison part 222 judges the comparison result based on the process data and the sensor data compared in the comparison data acquisition part 221 (S14). In the case that there are process data related to the presence or absence of detection of bursting and data related to the presence or absence of detection of the elastic wave obtained by the AE sensor, the comparison unit 222 determines that the burst has occurred ("Yes" in S15), and the determination result display unit 215 The detection result detected by the abnormality detection unit 213 is displayed 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 suspected abnormality (S16). When there is a suspected abnormality (Yes in S16), the judgment result display unit 215 displays the judgment 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 the present embodiment described above, a plurality of AE (Acoustic Emission) sensors are installed in the boiler piping, and the data detected by the AE sensors are displayed, so it can detect physical objects that are not in the audible area. The sound of destruction. Therefore, it is possible to early detect abnormalities including signs of bursts. In addition, in addition to the data based on the AE sensor, the process data is also displayed, so it can provide multiple judgment materials, the information obtained by the AE sensor and the information obtained from the process data. Therefore, it can be expected that the operator can objectively judge the occurrence of an abnormality. In addition, the above embodiments are for easy understanding of the inventors, and are not for limiting the interpretation of the inventors. The requirements of the embodiment, its arrangement, materials, conditions, shapes, dimensions, etc. should not be limited to the exemplified ones, and can be changed as appropriate. In addition, it is possible to partially replace or combine the configurations shown in the different embodiments. For example, in the above embodiment, the occurrence of an abnormality caused by the burst of the boiler piping has been described, but the present invention is not limited to this, and can be used to detect abnormalities caused by structural destruction, crystal change, or leakage happened. In addition, as the data on the piping status of the boiler, the process data is used as an example for description, but it is not limited to the process data, and data such as field records, action records, and alarm history can be used. For example, the sensor data display unit 212 and the process data display unit 203 can be configured to form a display screen separately, or can be displayed on one screen, and furthermore, can also be configured to overlap and display these modes. When the sensor data display portion 212 and the process data display portion 203 are displayed separately, the operator can easily grasp the output of each display system using two different indicators: the process data and the sensor data. On the other hand, in the case where the sensor data display portion 212 and the process data display portion 203 are displayed on one screen, and in the case of overlapping and displaying these, the operator can easily compare the two data. Also, similar to the above, the abnormality determination unit 205 and the abnormality detection unit 213 may be configured as the same processing mechanism on the same terminal, or may be configured as another processing mechanism on another terminal. A simplified data processing structure can be realized by configuring the same processing mechanism on the same terminal. On the other hand, by configuring another processing mechanism on another terminal, the data processing terminal can be separately constructed. Furthermore, as described above, the detection result display unit 214 and the determination result display unit 206 may be configured as the same processing mechanism on the same terminal, or may be configured as another processing mechanism on another terminal. In this case, it can be expected Same effect as above.

100: boiler 101: Furnace 102: Cyclone 103: recycling material recovery pipe 104: Exhaust gas flow path 105: Steam drum 106: Superheater 107: Economizer 108: Degasser 109: Boiler water supply pump 111: Furnace wall tube 112: precipitation pipe 113: Steam piping 114: Discharge piping 115: Water supply piping 116: Boiler water supply piping 120: Sensor 200: Abnormal monitoring device 201: Process Data Acquisition Department 202: Process Data Selection Department 203: Process data display unit 204: Statistical Processing Department 205: Abnormality Judgment Department 206: Judgment result display unit 211: Sensor Data Acquisition Department 212: Sensor data display section 213: Anomaly Detection Department 214: Test result display unit 215: Judgment result display unit 221: Comparative Information Acquisition Department 222: Comparison Department 223: Comparison result display unit

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

100: boiler

101a: input port

101b: Fan

101c: discharge outlet

101: Furnace

102: Cyclone

103: recycling material recovery pipe

104: Exhaust gas flow path

105: Steam drum

106: Superheater

107: Economizer

108: Degasser

109: Boiler water supply pump

111: Furnace wall tube

112: precipitation pipe

113: Steam piping

114: Discharge piping

115: Water supply piping

116: Boiler water supply piping

120: Sensor

200: Abnormal monitoring device

Claims (7)

An abnormality monitoring device for detecting abnormal occurrences of boiler piping caused by structural destruction, crystal change, leakage, or bursting, which is provided with: The process data display part displays the time series data of the process data related to the aforementioned boiler piping status; and The sensor data display part displays the time series data of the sensor data obtained by at least one AE sensor installed in the boiler piping, And it is configured to be able to compare the aforementioned process data with the aforementioned sensor data. Such as the abnormal monitoring device described in item 1 of the scope of patent application, which has: The comparison part compares the information about the presence or absence of structural failure, crystal change, leakage or burst of the boiler piping detected by the aforementioned process data and the aforementioned sensor data; and The comparison result display unit displays the comparison result of the aforementioned data. The abnormal monitoring device described in item 2 of the scope of patent application, wherein If the abnormal state is determined based on the comparison result, the comparison result display unit displays its content. The abnormal monitoring device described in item 1 of the scope of patent application, wherein The aforementioned process data includes data on whether the aforementioned boiler piping has burst or not, The comparison unit compares the presence or absence of the burst of the boiler pipe and the presence or absence of detection of the elastic wave detected by the AE sensor. The abnormal monitoring device described in item 1 of the scope of patent application, wherein The aforementioned process data includes information about the amount of water supplied to the aforementioned boiler piping and the amount of water discharged from the aforementioned boiler piping. The comparison unit compares the presence or absence of the difference between the water supply amount and the drainage amount and the presence or absence of detection of the elastic wave detected by the AE sensor. An abnormality monitoring method for detecting abnormal occurrence of boiler piping caused by structural destruction, crystal change, leakage or bursting, including: The process data display step is to display the sequence data of the process data related to the aforementioned boiler piping status; and The sensor data display step is to display the time sequence data of the sensor data obtained by the plurality of AE sensors installed in the aforementioned boiler piping. And can compare the aforementioned process data with the aforementioned sensor data. An abnormality monitoring program that detects abnormal occurrences of boiler piping caused by structural failure, crystal change, leakage or bursting. The aforementioned program makes the computer execute: The process data display step is to display the sequence data of the process data related to the aforementioned boiler piping status; and The sensor data display step is to display the time sequence data of the sensor data obtained by the plurality of AE sensors installed in the aforementioned boiler piping. And can compare the aforementioned process data with the aforementioned sensor data.

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