WO2014091956A1 - Dispositif de surveillance, procédé de surveillance et programme - Google Patents

Dispositif de surveillance, procédé de surveillance et programme Download PDF

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Publication number
WO2014091956A1
WO2014091956A1 PCT/JP2013/082307 JP2013082307W WO2014091956A1 WO 2014091956 A1 WO2014091956 A1 WO 2014091956A1 JP 2013082307 W JP2013082307 W JP 2013082307W WO 2014091956 A1 WO2014091956 A1 WO 2014091956A1
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WO
WIPO (PCT)
Prior art keywords
vibration sensors
turbine
relationship
vibration
sensor
Prior art date
Application number
PCT/JP2013/082307
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English (en)
Japanese (ja)
Inventor
哲 寺澤
加藤　真也
敬之 山本
敬喜 朝倉
林　司
山本　秀夫
睦男 生田
安達　勝
将弘 崎部
健三 宮
知也 相馬
真弓 高城
大石　敏之
Original Assignee
日本電気株式会社
中国電力株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 日本電気株式会社, 中国電力株式会社 filed Critical 日本電気株式会社
Priority to JP2014551983A priority Critical patent/JP6308591B2/ja
Publication of WO2014091956A1 publication Critical patent/WO2014091956A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H1/00—Measuring characteristics of vibrations in solids by using direct conduction to the detector
    • G01H1/003—Measuring characteristics of vibrations in solids by using direct conduction to the detector of rotating machines
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model

Definitions

  • the present invention relates to a monitoring device, method, and program for monitoring abnormality, deterioration, etc. of a turbine shaft, and more particularly, to a monitoring device, method, and program for a generator turbine shaft.
  • generator turbines are used in nuclear power plants, thermal power plants (hereinafter referred to as power plants), etc., and there is a risk of serious accidents if the rotating shaft of the turbine is abnormal or deteriorated. There is. For this reason, it is extremely important to monitor abnormalities and deterioration of the rotating shaft of the turbine.
  • the rotating shafts (turbine shafts) of a plurality of generator turbines from the high pressure turbine to the low pressure turbine are connected to each other and connected to the rotating shaft of the generator.
  • attempts have been made to detect abnormalities, deterioration, etc. of the turbine shaft by detecting abnormal vibrations in the bearing with a vibration sensor. For this reason, in order to monitor abnormality, deterioration, etc.
  • Patent Document 1 discloses a shaft vibration monitoring system and a shaft vibration monitoring device for a turbine generator.
  • Patent Document 1 when a vibration sensor that detects vibration of the turbine shaft is attached to the turbine shaft and the output of the vibration sensor exceeds a predetermined trip value, The shaft vibration monitoring system and the shaft vibration monitoring device that are judged to be abnormal are clarified. Further, in Patent Document 1, since the output of the vibration sensor is easily affected by noise from electrical and mechanical equipment provided around the turbine generator, the influence of noise is obtained by averaging the output from the vibration sensor. It has been proposed to mitigate.
  • Patent Document 1 the output of a vibration sensor provided in a turbine generator is individually processed, that is, individually compared with a trip value or a threshold value and averaged.
  • Patent Literature 1 does not disclose monitoring the outputs of a plurality of vibration sensors at all.
  • the subject of this invention is providing the monitoring apparatus and monitoring method which can detect abnormality, deterioration, etc. of a turbine shaft during a driving
  • a plurality of vibration sensors a relationship storage database that models correlations between sensor values obtained from the plurality of vibration sensors, and stores them as a relationship model;
  • a relationship verification module that is detected by a vibration sensor and compares a detection result representing a relationship between the plurality of vibration sensors with the relationship model, verifies the relationship between the plurality of vibration sensors, and outputs a verification result; It is possible to obtain a monitoring device characterized by having
  • a plurality of vibration sensors are provided in a turbine having a rotating shaft, a correlation between vibration data in the plurality of vibration sensors is constructed as a relationship model, and the power generation
  • a monitoring method comprising: comparing measurement data obtained from the plurality of vibration sensors when driving a mechanical turbine and the relationship model to verify disturbance of the measurement data from the relationship model.
  • a computer-readable program that receives sensor data from a plurality of vibration sensors and diagnoses a state of an attachment position of the vibration sensor, and A step of constructing a correlation between sensor data as a relationship model, a step of comparing measurement data from the plurality of vibration sensors with the relationship model, and verification of disturbance of the measurement data from the relationship model
  • a program characterized by causing the computer to perform the step of performing is obtained.
  • abnormality or deterioration of the turbine shaft can be detected during operation of the turbine without stopping or disassembling the turbine. For this reason, when this invention is applied to a power plant, the operation rate of a power plant can be raised and the period of a periodic inspection can be lengthened.
  • the present invention makes it possible to detect turbine shaft rigidity deterioration (cracking), bearing wear, and the like.
  • FIG. 1 is a block diagram illustrating an example of a system according to an embodiment of the present invention.
  • FIG. 2 is a flowchart for explaining a model generation mode executed in the system shown in FIG.
  • FIG. 3 is a table showing the relationship between the fit value indicating the strength of the correlation between the vibration sensors and the physical position of the vibration sensor.
  • FIG. 4 is a flowchart for explaining a monitoring mode executed in an embodiment of the present invention.
  • FIG. 1 an example in which the monitoring device according to the present invention is applied to a system (here, a power plant system) is shown.
  • the illustrated power plant system includes a generator 11 and a plurality of turbines.
  • FIG. 1 exemplarily shows a case where three turbines (herein, referred to as first, second, and third turbines 131, 132, and 133) are provided.
  • the third turbine 133 is a high-pressure turbine to which high-pressure steam is supplied, and the other second and first turbines 131 and 132 are low-pressure turbines to which low-pressure steam is supplied rather than steam supplied to the high-pressure turbine 133.
  • the present invention is not limited to a system including three turbines, and can be applied to a system including four or more turbines or a system including a single turbine, for example.
  • one end of the turbine shaft of the first turbine 131 is connected to the rotating shaft of the generator 11, while the other end of the turbine shaft of the first turbine 131 is the turbine of the second turbine 132. It is connected to one end of the shaft.
  • the other end of the turbine shaft of the second turbine 132 is connected to one end of the turbine shaft of the third turbine 133 that is a high-pressure turbine.
  • Bearing portions are provided at both ends of the turbine shafts of the first to third turbines 131 to 133.
  • first and second vibration sensors S1 and S2 are attached to both ends of the rotating shaft of the generator 11, and further a rotation speed sensor R1 for detecting the rotation speed of the generator 11 is also attached.
  • third and fourth vibration sensors S3 and S4 are attached to both ends of the turbine shaft of the first turbine 131
  • fifth and sixth vibration sensors S5 are attached to both ends of the turbine shaft of the second turbine 132.
  • S6 is attached to both ends of the turbine shaft of the third turbine 133, respectively.
  • An identification number (ID) is assigned to each of the vibration sensors S1 to S8.
  • the vibration sensors S1 to S8 described above constitute a part of the monitoring device according to the present invention.
  • the sensor detection values (sensor data) output from the first to eighth vibration sensors S1 to S8 are given to the monitoring module 15 together with the output value from the rotation speed sensor R1.
  • the monitoring module 15 stores sensor detection values from the vibration sensors S1 to S8 as sensor information in the sensor value storage database 17 together with IDs, times, physical positions, measurement target information, and the like of the vibration sensors S1 to S8. .
  • the sensor information stored and accumulated in the sensor value storage database 17 is input to the preprocessing module 19, and the preprocessing module 19 performs averaging processing for removing noise and the like, extraction processing for waveform feature points, and the like.
  • the preprocessed sensor data is stored in the sensor accompanying information storage database 21.
  • the pre-processing module 19 When used in a situation where noise is not a problem, the pre-processing module 19 is unnecessary, and in this case, the sensor-associated information storage database 21 is also unnecessary.
  • sensor data preprocessed by the preprocessing module 19 is input to the relationship verification module 23.
  • the relationship verification module 23 verifies the relationship between sensor data of a plurality of vibration sensors (here, the first to eighth vibration sensors S1 to S8).
  • the relationship verification module 23 includes a relationship storage database 25.
  • the relationship storage database 25 accumulates relationships between past sensors that represent the correlation between the sensor data of the first to eighth vibration sensors S1 to S8. As the relationship between past sensors, it is preferable to use the output data of each sensor when the system is operating normally.
  • the verification result in the relationship verification module 23 is given to the diagnosis module 27 and compared with the abnormal pattern stored in advance in the abnormal pattern storage database 29. If an abnormality is detected as a result of the comparison, an alarm or a display device is notified.
  • the monitoring module 15, the preprocessing module 19, the relationship verification module 23, and the diagnostic module 27 are illustrated as hardware, but these modules are actually executed by a computer. Consists of possible programs.
  • the monitoring module 15, the preprocessing module 19, the relationship verification module 23, and the diagnostic module 27 include a memory that stores programs for executing operations in these modules, and a computer (CPU) that executes these programs. Can be realized.
  • the correlation between sensor data output from a plurality of vibration sensors (here, eight vibration sensors) by the relationship verification module 23 and the relationship storage database 25 is used.
  • the correlation between the normality and the correlation in the normal state and detecting the disturbance of the correlation of the sensor data from the correlation in the normal state, the abnormality or deterioration of the turbine shaft is detected. Yes.
  • FIGS. 1 the generation mode (phase) of the relationship model stored in the relationship storage database 25 is shown.
  • the relationship model generation mode is described as being executed by a program that can be executed by a computer, but it can also be realized by using a hardware circuit.
  • a sensor data collection period is defined, and a collection period is set (step F1-2).
  • sensor data is acquired from the plurality of vibration sensors S1 to S8 during the collection period.
  • the acquired sensor data is stored in a memory (not shown) included in the relationship verification module 23.
  • Step F1-4 under the control of the verification program that realizes the operation of the relationship verification module 23, a relationship model that represents the correlation between the sensor data from the plurality of vibration sensors S1 to S8 is constructed. Is done.
  • the constructed relationship model is stored in the relationship storage model 25 in step F1-5.
  • the relationship verification module 23 generates a fit value indicating the degree of correlation of sensor data from a plurality of vibration sensors for each vibration sensor. Further, the relationship verification module 23 creates a table in which vibration sensors are arranged in order of high correlation for each vibration sensor, stores the table in the relationship storage database 25, and ends the creation of the relationship model.
  • FIG. 3 an example of the relationship model created by the process shown in FIG. 2 is shown. Here, an example in which ten vibration sensors are provided is shown. In the uppermost column of FIG. 3, the fit values are shown in order from the highest fit value (first) to the lowest fit value (9th). In the leftmost column of FIG. 3, numbers 1 to 10 of the first to tenth vibration sensors are shown.
  • the vibration sensor having the highest correlation with the vibration sensor 1 is the fourth vibration sensor shown in the column of the fit value 1, and hereinafter, the fifth, ninth, sixth, third, eighth, The correlation becomes weaker in the order of 7, 2, and 10 vibration sensors.
  • the vibration sensor 6 having the highest correlation with the second vibration sensor 2 is the sixth vibration sensor 6, and is referred to as 6, 3, 5, 9, 7, 4, 1, 8, 10 below. In this order, the correlation with the second vibration sensor 2 becomes weaker.
  • the sensor data of the vibration sensors at the physical positions closest to each other are not necessarily the most correlated, and the sensor data of the vibration sensors at the separated positions are not necessarily the highest. It can be seen that there is a case where the correlation between them is the highest.
  • a table for normal operation is created and stored in the relationship storage database 25, and this table is compared with the detection data obtained by the vibration sensor, so that there is an abnormality in the turbine shaft. Whether the system has deteriorated, worn, or the like can be detected during system operation. For example, in FIG. 3, if the first “4” and the second “5” of the correlation with respect to the vibration sensor 1 are replaced with “5” and “4”, there is some abnormality in the system. You can detect what happened. However, in this state, the location where the abnormality has occurred cannot be specified. In order to specify the location where the abnormality has occurred, the relationship between the correlation disorder and the abnormal pattern is stored in advance in the abnormal pattern storage database 29. With reference to FIG.
  • the monitoring mode shown in FIG. 4 includes the monitoring module 15, the sensor value storage database 17, the relationship verification module 23, the relationship storage database 25, the diagnostic module 27, and the abnormal pattern storage database 29 shown in FIG. Done with. Actually, the monitoring mode is executed by a program that realizes an operation corresponding to the operation performed by the above-described module, but can also be realized by using a hardware circuit. Note that the preprocessing module 19 and the sensor-associated information storage database can be used as necessary.
  • the monitoring mode shown in FIG. 4 is started by acquiring a sensor value (detection data) from each vibration sensor in the monitoring module 15 (step F2-1), and the correlation between the vibration sensors is step F2-. 2 is confirmed.
  • step F ⁇ b> 2-3 the relationship verification module 23 checks whether there is any disturbance in the correlation between sensor data (that is, measurement data) from each sensor. That is, whether or not the table shown in FIG. 3 and the measurement data from each vibration sensor have the same correlation is determined for each vibration sensor in step F2-3.
  • step F2-3: No the process returns to step F2-1, and measurement data from other vibration sensors is acquired.
  • step F2-3: Yes the relationship verification module 23 determines that an abnormality has occurred in the turbine shaft, and proceeds to step F2-4. To do.
  • step F2-4 a fit value indicating the correlation of measurement data from each vibration sensor is generated, and a pattern as shown in FIG. Stored in the database 25.
  • step F2-5 the diagnostic module 27 is activated, the abnormal pattern stored in advance in the abnormal pattern storage database 29 is compared with the pattern created for the detection data, and the difference between the two is extracted.
  • the diagnostic module 27 detects the abnormal pattern, and indicates the location of the turbine shaft deflection or the like, thereby terminating the process.
  • a plurality of vibration sensors a database that models correlations between sensor values obtained from the plurality of vibration sensors, and stored as a relationship model; and the plurality of vibration sensors detected by the plurality of vibration sensors
  • a monitoring apparatus comprising: a module that compares a detection result representing a relationship between sensors with the relationship model, verifies a relationship between the plurality of vibration sensors, and outputs a verification result.
  • the monitoring apparatus according to supplementary note 1, further comprising a diagnostic module for performing a diagnosis based on the verification result.
  • a diagnostic module for performing a diagnosis based on the verification result.
  • the monitoring device according to supplementary note 1 or 2, wherein the plurality of vibration sensors are attached to different positions of the turbine.
  • the plurality of vibration sensors are attached to bearings of the turbine.
  • the said turbine is a turbine for generators, The monitoring apparatus of Additional remark 3 or 4 characterized by the above-mentioned.
  • a plurality of vibration sensors are provided in a turbine having a rotating shaft, and a correlation between vibration data in the plurality of vibration sensors is constructed as a relationship model, and when the generator turbine is driven
  • a monitoring method comprising: comparing measured vibration data obtained from the plurality of vibration sensors and the relationship model to verify disturbance of the measured vibration data from the relationship model.
  • the monitoring method according to supplementary note 6 wherein after the disturbance of the measured vibration data from the relationship model is verified, the presence or absence of deterioration of the rotating shaft of the turbine is diagnosed.
  • the measurement vibration data is stored in a storage database, and the stored measurement vibration data is preprocessed and then compared with the relationship model.
  • Monitoring method described in. The monitoring method characterized by averaging the said measurement vibration data and extracting a wavelength feature point in the said pre-processing.
  • a computer-readable program that receives sensor data from a plurality of vibration sensors and diagnoses the state of the mounting position of the vibration sensors, and the correlation between the sensor data in the plurality of vibration sensors Building a relationship as a relationship model; A program for causing the computer to perform a step of comparing measurement data from the plurality of vibration sensors with the relationship model and a step of verifying disturbance of the measurement data from the relationship model.

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  • General Physics & Mathematics (AREA)
  • Testing And Monitoring For Control Systems (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Abstract

La présente invention porte sur un dispositif de surveillance, un procédé et un programme qui sont aptes à détecter des erreurs, qui peuvent être obtenus par : construction, en tant que modèle de relation, de la corrélation entre des données de capteur provenant d'une pluralité de capteurs de vibration disposés au niveau de différentes positions sur un axe de turbine ; et comparaison des données de mesure pour chaque capteur de vibration au modèle de relation.
PCT/JP2013/082307 2012-12-14 2013-11-25 Dispositif de surveillance, procédé de surveillance et programme WO2014091956A1 (fr)

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JP2012273448 2012-12-14

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2017021702A (ja) * 2015-07-14 2017-01-26 中国電力株式会社 故障予兆監視方法
KR101858121B1 (ko) * 2016-06-13 2018-05-15 두산중공업 주식회사 발전소 상태 판단 장치 및 방법
CN109643112A (zh) * 2016-08-31 2019-04-16 通用电气技术有限公司 用于阀和致动器监测系统的高级启动计数器模块
EP3489641A1 (fr) * 2017-11-27 2019-05-29 Goodrich Actuation Systems Limited Système amélioré de détection d'un défaut mécanique dans un arbre rotatif
JP2022129722A (ja) * 2021-02-25 2022-09-06 株式会社ミヤワキ 測定診断装置

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2021082025A (ja) * 2019-11-19 2021-05-27 旭化成株式会社 診断装置、診断方法及び診断プログラム
KR102450030B1 (ko) * 2020-11-27 2022-10-05 한국과학기술연구원 발전설비의 고장을 감지하는 시스템 및 방법

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JPH04134597A (ja) * 1990-09-27 1992-05-08 Toshiba Corp プラントの異常監視装置
JPH05256741A (ja) * 1992-03-11 1993-10-05 Toshiba Corp プラント信号監視方法およびその装置
JPH07110708A (ja) * 1993-10-13 1995-04-25 Hitachi Ltd 故障診断装置および方法
JPH07168619A (ja) * 1993-10-20 1995-07-04 Hitachi Ltd 機器／設備診断方法およびシステム
JPH10333742A (ja) * 1997-05-28 1998-12-18 Toshiba Eng Co Ltd 回転体振動診断装置
JP2005149137A (ja) * 2003-11-14 2005-06-09 Tokyo Gas Co Ltd 遠隔監視システム、遠隔監視方法、及び遠隔監視プログラム

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH04134597A (ja) * 1990-09-27 1992-05-08 Toshiba Corp プラントの異常監視装置
JPH05256741A (ja) * 1992-03-11 1993-10-05 Toshiba Corp プラント信号監視方法およびその装置
JPH07110708A (ja) * 1993-10-13 1995-04-25 Hitachi Ltd 故障診断装置および方法
JPH07168619A (ja) * 1993-10-20 1995-07-04 Hitachi Ltd 機器／設備診断方法およびシステム
JPH10333742A (ja) * 1997-05-28 1998-12-18 Toshiba Eng Co Ltd 回転体振動診断装置
JP2005149137A (ja) * 2003-11-14 2005-06-09 Tokyo Gas Co Ltd 遠隔監視システム、遠隔監視方法、及び遠隔監視プログラム

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2017021702A (ja) * 2015-07-14 2017-01-26 中国電力株式会社 故障予兆監視方法
KR101858121B1 (ko) * 2016-06-13 2018-05-15 두산중공업 주식회사 발전소 상태 판단 장치 및 방법
CN109643112A (zh) * 2016-08-31 2019-04-16 通用电气技术有限公司 用于阀和致动器监测系统的高级启动计数器模块
CN109643112B (zh) * 2016-08-31 2021-08-03 通用电气技术有限公司 用于阀和致动器监测系统的高级启动计数器模块
EP3489641A1 (fr) * 2017-11-27 2019-05-29 Goodrich Actuation Systems Limited Système amélioré de détection d'un défaut mécanique dans un arbre rotatif
US11067496B2 (en) 2017-11-27 2021-07-20 Goodrich Actuation Systems Limited System for detecting a mechanical fault in a rotating shaft
JP2022129722A (ja) * 2021-02-25 2022-09-06 株式会社ミヤワキ 測定診断装置
JP7257060B2 (ja) 2021-02-25 2023-04-13 株式会社ミヤワキ 測定診断装置

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