WO2023273378A1 - Procédé et appareil de diagnostic de défaillance de boulon - Google Patents

Procédé et appareil de diagnostic de défaillance de boulon Download PDF

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Publication number
WO2023273378A1
WO2023273378A1 PCT/CN2022/078811 CN2022078811W WO2023273378A1 WO 2023273378 A1 WO2023273378 A1 WO 2023273378A1 CN 2022078811 W CN2022078811 W CN 2022078811W WO 2023273378 A1 WO2023273378 A1 WO 2023273378A1
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Prior art keywords
bolt
fault
fault diagnosis
simulation
monitoring
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PCT/CN2022/078811
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English (en)
Chinese (zh)
Inventor
张猛
周军长
陈旭
郭靖
铎林
刘云平
宋敏
唐磊
赵政雷
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东方电气集团东方电机有限公司
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Publication of WO2023273378A1 publication Critical patent/WO2023273378A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B5/00—Measuring arrangements characterised by the use of mechanical techniques
    • G01B5/02—Measuring arrangements characterised by the use of mechanical techniques for measuring length, width or thickness
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
    • G01L5/00—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes
    • G01L5/16—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes for measuring several components of force
    • G01L5/173—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes for measuring several components of force using acoustic means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/17—Mechanical parametric or variational design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/20—Hydro energy

Definitions

  • the invention relates to the technical field of bolt fault diagnosis, in particular to a bolt fault diagnosis method and device.
  • the top cover is one of the key components of the turbine, and its main function is to fix the water guide mechanism and bear the axial load of the unit.
  • the top cover and the seat ring are connected by bolts.
  • the vibration of the unit caused by uncertain factors will cause the bolts to bear variable amplitude loads, which will have a huge impact on the mechanical strength and fatigue life of the bolts.
  • the Russian Sayan Hydropower Station abroad caused a major accident due to the fatigue failure of the top cover-seat ring bolts.
  • the top cover-seat ring bolts of a pumped storage power station in China were fatigued and fractured when the turbine of a certain unit was shut down in an emergency, and the top cover was lifted.
  • causes major safety accidents (such as flooding of factory buildings, etc.).
  • the bolt is a typical multi-notch part, and its fatigue performance is affected by various factors such as the structure, material, manufacturing process, dynamic stress level and environmental factors of the bolt.
  • the strength and fatigue checks are carried out under the preset working conditions, but the influence of the actual installation value of the bolts of the real machine, the actual operating state of the real machine and the real operation history on the stress state of the actual bolts cannot be accurately considered.
  • the failure probability of important bolts of hydraulic turbines is extremely low, but once it happens, the damage will be huge, ranging from equipment damage to flooding of workshops and major personnel safety accidents.
  • the present invention provides a method and device for bolt fault diagnosis, which solves the problem in the prior art that all bolts cannot be effectively diagnosed through limited measuring points, thereby solving the problems of existing equipment bolt group monitoring and Troubleshooting problems.
  • a bolt fault diagnosis method comprising the following steps:
  • the present invention solves the problems of limited number of bolt fault monitoring points, limited arrangement of bolt fault monitoring points, and rapid diagnosis of fault points through bolt fault simulation tests, fault simulation, artificial intelligence modeling, and the like.
  • the virtual fault case library is established, and the automatic diagnosis of the bolt faults of the real machine can be realized, which is convenient for the diagnosis of all bolts.
  • the following settings can be adopted: if a bolt without a measuring point is loosened or broken, according to the finite element simulation analysis, the axial force of the bolts near the measuring point will change to varying degrees, and the combination of the changing values can be As the corresponding fault characteristics, the looseness and fracture of the bolts without measuring points can be reversed, so as to realize the location of the fault bolts and quantitative fault diagnosis analysis, and complete the intelligent diagnosis of this fault through artificial neural network technology.
  • the bolt failure simulation test in step S1 includes a bolt loosening failure simulation test and/or a bolt breaking failure simulation test.
  • the monitoring data of the bolt fault monitoring point in step S1 includes the bolt axial force value and/or the bolt elongation value of the bolt fault monitoring point.
  • the bolt axial force value and bolt elongation value are used as numerical indicators of bolt failure, which is convenient for statistical analysis and modeling.
  • step S2 also includes the following steps: analyzing simulation errors, and verifying the bolt axial force monitoring instrument.
  • the present invention can verify the performance of the bolt axial force monitoring instrument before construction, verify the fault diagnosis method, and improve the self-adaptive ability, fault tolerance and robustness of diagnosing the flange bolt fault of the real machine .
  • step S2 the real machine working condition data, the real machine operating state data, and the initial preload installation value of the unit are used as boundary conditions for simulation input.
  • step S3 further includes the following step: enabling the bolt fault model of the real machine to have an online self-learning function.
  • the online self-learning function further improves the error-correcting ability of diagnosis and makes it more intelligent.
  • a bolt fault diagnosis device applied to the bolt fault diagnosis method includes a bolt, a bolt axial force monitoring instrument connected to the bolt, and a load simulation device connected to the bolt.
  • the load simulation device provides simulated load for bolt failure, and the bolt axial force monitoring instrument monitors the bolt axial force in the process of bolt failure in real time, so as to facilitate the diagnosis of bolt failure.
  • the load simulation device is a jack.
  • the jack applies loads well and is easy to install.
  • the bolt axial force monitoring instrument is an ultrasonic load cell.
  • Ultrasonic force sensor has high force measurement accuracy, non-destructive monitoring, relatively sensitive and easy to install.
  • a bolt length monitoring instrument is also included.
  • the bolt length monitoring instrument can choose a dial indicator, which is convenient for installation.
  • the present invention has the following beneficial effects:
  • the present invention solves the problems of limited number of bolt fault monitoring points, limited location of bolt fault monitoring points, and rapid diagnosis of fault points through bolt fault simulation tests, fault simulation, artificial intelligence modeling, etc.; through finite element simulation, Establish a virtual fault case library, and realize automatic diagnosis of bolt faults on real machines, which facilitates the diagnosis of all bolts; the invention can reverse the loosening and fracture of bolts without measuring points, so as to realize the positioning and quantitative fault diagnosis of faulty bolts Analysis, complete the intelligent diagnosis of this fault through artificial neural network technology;
  • the bolt fault simulation test described in step S1 of the present invention comprises a bolt loosening fault simulation test and/or a bolt fracture fault simulation test; bolt loosening and bolt fracture are common bolt faults, and by the above simulation tests, the scope of application of the present invention is wider wide;
  • the monitoring data of the bolt fault monitoring point described in step S1 of the present invention includes the bolt axial force value and/or the bolt elongation value of the bolt fault monitoring point; the bolt axial force value and the bolt elongation value are used as the numerical index of the bolt fault, which is convenient Convenient statistical analysis and modeling;
  • Step S2 of the present invention also includes the following steps: analyze the simulation error, and verify the bolt axial force monitoring instrument; through the above steps, the present invention can verify the performance of the bolt axial force monitoring instrument before construction, and diagnose the fault The method is verified, which improves the adaptive ability, fault tolerance and robustness of the diagnosis of the flange bolt fault of the real machine;
  • the boundary condition of simulation input is taken as the boundary condition of simulation input with real machine working condition data, real machine operating state data, unit initial preload installation value; It is beneficial to improve the quality of diagnosis;
  • step S3 of the present invention also includes the following steps: make the bolt failure model of the real machine possess an online self-learning function, and the online self-learning function further improves the error correction ability of diagnosis and is more intelligent;
  • the load simulation device of the present invention provides simulated loads for bolt failures, and the bolt axial force monitoring instrument monitors the bolt axial force in the process of bolt failure in real time, thereby facilitating the diagnosis of bolt failures;
  • the load simulation device of the present invention is a jack, which has a good effect of applying load and is easy to install;
  • the bolt axial force monitoring instrument of the present invention is an ultrasonic load cell, which has high force measurement accuracy, non-destructive monitoring, is more sensitive, and is easy to install;
  • the present invention also includes a bolt length monitoring instrument; this is convenient for monitoring the length change in the process of bolt failure, thereby further improving the effect of simulation and diagnosis.
  • Fig. 1 is the fault diagnosis modeling flow chart of the bolt fault simulation test
  • Fig. 2 is the fault diagnosis modeling flow chart of the real machine bolt fault
  • Fig. 3 is the structural representation of the bolt fault diagnosis device of the present invention.
  • Fig. 4 is the P direction view of Fig. 3;
  • Fig. 5 is a cross-sectional view along A-A plane of Fig. 3 .
  • a bolt fault diagnosis method includes the following steps:
  • the present invention solves the problems of limited number of bolt fault monitoring points, limited arrangement of bolt fault monitoring points, and rapid diagnosis of fault points through bolt fault simulation tests, fault simulation, artificial intelligence modeling, and the like.
  • the virtual fault case library is established, and the automatic diagnosis of the bolt faults of the real machine can be realized, which is convenient for the diagnosis of all bolts.
  • the following settings can be adopted: if a bolt without a measuring point is loosened or broken, according to the finite element simulation analysis, the axial force of the bolts near the measuring point will change to varying degrees, and the combination of the changing values can be As the corresponding fault characteristics, the looseness and fracture of the bolts without measuring points can be reversed, so as to realize the location of the fault bolts and quantitative fault diagnosis analysis, and complete the intelligent diagnosis of this fault through artificial neural network technology.
  • the bolt failure simulation test in step S1 includes a bolt loosening failure simulation test and/or a bolt breaking failure simulation test.
  • the monitoring data of the bolt fault monitoring point in step S1 includes the bolt axial force value and/or the bolt elongation value of the bolt fault monitoring point.
  • the bolt axial force value and bolt elongation value are used as numerical indicators of bolt failure, which is convenient for statistical analysis and modeling.
  • step S2 also includes the following steps: analyzing simulation errors, and verifying the bolt axial force monitoring instrument.
  • the present invention can verify the performance of the bolt axial force monitoring instrument before construction, verify the fault diagnosis method, and improve the self-adaptive ability, fault tolerance and robustness of diagnosing the flange bolt fault of the real machine .
  • step S2 the real machine working condition data, the real machine operating state data, and the initial preload installation value of the unit are used as boundary conditions for simulation input.
  • step S3 further includes the following step: enabling the bolt fault model of the real machine to have an online self-learning function.
  • the online self-learning function further improves the error-correcting ability of diagnosis and makes it more intelligent.
  • this embodiment includes all the technical features of Embodiment 1. In addition, this embodiment also includes the following technical features:
  • a bolt fault diagnosis device applied to the bolt fault diagnosis method described above includes a bolt 1 , a bolt axial force monitoring instrument 2 connected to the bolt 1 , and a load simulation device 3 connected to the bolt 1 .
  • the load simulation device 3 provides simulated load for the failure of the bolt 1, and the bolt axial force monitoring instrument 2 monitors the bolt axial force in the process of the failure of the bolt 1 in real time, so as to facilitate the diagnosis of the failure of the bolt 1.
  • the load simulation device 3 is a jack.
  • the jack applies loads well and is easy to install.
  • the bolt axial force monitoring instrument 2 is an ultrasonic load cell.
  • Ultrasonic force sensor has high force measurement accuracy, non-destructive monitoring, relatively sensitive and easy to install.
  • a bolt length monitoring instrument 4 is also included.
  • the bolt length monitoring instrument 4 can choose a dial indicator, which is convenient for installation.
  • this embodiment provides a more detailed implementation manner.
  • the fault simulation device consists of base 5, base fixing bolt 6, flange 7, test bolt (i.e. bolt 1), test nut 8, jack (i.e. load simulation device 3), jack support plate 9, dial indicator (i.e. Bolt length monitoring instrument 4), bolt force sensor (ie bolt axial force monitoring instrument 2), force measuring washer 10, see Figure 1 for details.
  • the base 5 When in use, the base 5 is fixed on the test bench by the base fixing bolt 6; the flange 7 is connected to the base 5 according to the set preload by the test bolt and the test nut; the external load of the work is accurately simulated by the jack .
  • the bolt force sensor is consistent with the real machine, and the ultrasonic force sensor is preferred.
  • the elongation value of the test bolt is monitored by a dial gauge.
  • test bolts of this device are 8 M30-8.8-O bolts, and the bolt axial force measuring points are arranged on 2#, 4#, 6#, 8# bolts; the jack range is 100 tons, and the simulated axial working load is set to 80 tons .
  • pre-tighten 8 bolts to the specified load such as 180kN
  • loosen the specified bolts to a certain or several proportions of the pre-tightening value, such as loosening to 50% of the pre-tightening force, while loosening the bolts record each The reading of the bolt measuring point changes.
  • the measured data of the fault sample can be obtained; based on the finite element simulation, the corresponding virtual simulation calculation value can be obtained, and compared and corrected with the measured data of the simulation device. Then, a complete fault sample library is further established through artificial intelligence technology, and the intelligent diagnosis of all bolt faults of the flange 7 based on the limited bolt axial force monitoring data is realized.
  • the fault diagnosis modeling process is shown in Figure 1 for details. Table 1 is a schematic diagram of fault feature extraction based on finite element calculation.
  • Table 1 Schematic diagram of fault feature extraction based on finite element calculation
  • the invention provides a fault simulation device and an intelligent diagnosis method for the loosening and fracture of top cover-seat ring bolts.
  • the method is based on the operating condition data of the unit, the operating state data of the unit, the bolt pre-tightening installation data, and the measuring points of some bolts.
  • On-line monitoring data finite element calculation and artificial neural network technology can automatically judge and automatically on-line early warning the possible loosening and fracture of all bolts in the flange 7, which can reduce the implementation cost and maintenance cost of bolt monitoring equipment.
  • the performance verification of the force sensor in the factory (before the construction of the power station) provides a reliable guarantee for the safe operation and condition maintenance of the power station.
  • the present invention can be simultaneously applied to bolt health status evaluation and intelligent fault diagnosis of flange 7 connection structures such as draft pipe entry doors, volute casing entry doors, and ball valves. It has the following advantages and functions:
  • the present invention can verify the performance of the bolt force sensor in the factory (before the construction of the power station), and perform health status evaluation and fault diagnosis on all the bolts of the flange of the real machine, which can reduce the implementation cost and maintenance cost of the bolt monitoring equipment, and provide a better solution for the power station. Safe operation and state-of-the-art maintenance provide a reliable basis.
  • the fault can be directly judged according to the data of the bolt axial force measuring points. If only some bolts of all flange bolts are equipped with bolt sensor measuring points, the health status assessment and fault diagnosis of all flange bolts can be carried out through limited measuring points.
  • the present invention can be preferably carried out.

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  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
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  • Computer Hardware Design (AREA)
  • General Engineering & Computer Science (AREA)
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Abstract

La présente invention divulgue un procédé et un appareil de diagnostic de défaillance de boulon. Le procédé comprend les étapes suivantes consistant : S1, à réaliser un contrôle de simulation de défaillance de boulon et à enregistrer des données de surveillance d'un point de surveillance de défaillance de boulon; S2, à simuler une défaillance de boulon au moyen d'une méthode des éléments finis et établir une bibliothèque de cas virtuels de défaillance; et S3, à extraire une caractéristique de défaillance de la bibliothèque de cas virtuels de défaillance et à réaliser une modélisation par intelligence artificielle sur une défaillance réelle de boulon de machine. Au moyen de la présente invention, le problème que représente dans l'état de la technique l'impossibilité d'effectuer un diagnostic efficace sur tous les boulons au moyen d'un nombre limité de points de contrôle est résolu, ce qui permet de résoudre le problème que représente la réalisation d'une surveillance et d'un diagnostic de défaillance d'un groupe de boulons de dispositifs existants.
PCT/CN2022/078811 2021-06-29 2022-03-02 Procédé et appareil de diagnostic de défaillance de boulon WO2023273378A1 (fr)

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CN117705424A (zh) * 2023-10-27 2024-03-15 国网新源集团有限公司 一种水轮机组的顶盖螺栓试验方法

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CN113656989A (zh) * 2021-06-29 2021-11-16 东方电气集团东方电机有限公司 一种螺栓故障诊断方法及装置

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CN105378574A (zh) * 2013-06-19 2016-03-02 沃尔沃卡车集团 用于车辆的方法
CN107862052A (zh) * 2017-11-08 2018-03-30 国网电力科学研究院武汉南瑞有限责任公司 一种故障案例库、故障树及故障谱构建方法
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