CN114091368A - Method for identifying cavitation state of axial flow turbine - Google Patents

Method for identifying cavitation state of axial flow turbine Download PDF

Info

Publication number
CN114091368A
CN114091368A CN202111265390.1A CN202111265390A CN114091368A CN 114091368 A CN114091368 A CN 114091368A CN 202111265390 A CN202111265390 A CN 202111265390A CN 114091368 A CN114091368 A CN 114091368A
Authority
CN
China
Prior art keywords
cavitation
axial flow
identifying
water turbine
linearity
Prior art date
Legal status (The legal status 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 status listed.)
Granted
Application number
CN202111265390.1A
Other languages
Chinese (zh)
Other versions
CN114091368B (en
Inventor
冯建军
门羿
朱国俊
罗兴锜
吴广宽
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xian University of Technology
Original Assignee
Xian University of Technology
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 Xian University of Technology filed Critical Xian University of Technology
Priority to CN202111265390.1A priority Critical patent/CN114091368B/en
Priority to NL2031029A priority patent/NL2031029A/en
Publication of CN114091368A publication Critical patent/CN114091368A/en
Application granted granted Critical
Publication of CN114091368B publication Critical patent/CN114091368B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03B—MACHINES OR ENGINES FOR LIQUIDS
    • F03B11/00—Parts or details not provided for in, or of interest apart from, the preceding groups, e.g. wear-protection couplings, between turbine and generator
    • F03B11/008—Measuring or testing arrangements
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03B—MACHINES OR ENGINES FOR LIQUIDS
    • F03B15/00—Controlling
    • 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
    • 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
    • G01H1/12—Measuring characteristics of vibrations in solids by using direct conduction to the detector of longitudinal or not specified vibrations
    • G01H1/16—Amplitude
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M7/00—Vibration-testing of structures; Shock-testing of structures
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03B—MACHINES OR ENGINES FOR LIQUIDS
    • F03B3/00—Machines or engines of reaction type; Parts or details peculiar thereto
    • F03B3/04—Machines or engines of reaction type; Parts or details peculiar thereto with substantially axial flow throughout rotors, e.g. propeller turbines
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2260/00—Function
    • F05B2260/80—Diagnostics
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/80—Devices generating input signals, e.g. transducers, sensors, cameras or strain gauges
    • F05B2270/807—Accelerometers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/08—Fluids
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Analysis (AREA)
  • Fluid Mechanics (AREA)
  • Computing Systems (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Physics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • Algebra (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
  • Hydraulic Turbines (AREA)

Abstract

The invention discloses a method for identifying the cavitation state of an axial flow water turbine, which is a method for acquiring vibration signals of the water turbine under different working conditions, obtaining a characteristic parameter Hurst index by analyzing a vibration signal MF-DFA of a draft tube of the axial flow water turbine, and identifying the cavitation state based on the linearity of the Hurst index.

Description

Method for identifying cavitation state of axial flow turbine
Technical Field
The invention belongs to the technical field of fluid machinery, and relates to a method for identifying the cavitation state of an axial flow type water turbine.
Background
Water energy is very important for realizing the double-carbon target in China. The water turbine is core mechanical equipment for water energy development, and cavitation has obvious influence on the performance of the water turbine. Cavitation can cause cavitation erosion which can lead to flaking or even perforation of metallic material on localized surfaces of the runner and certain flow components. When cavitation and cavitation erosion are serious to the extent of destroying normal water flow, the energy loss of the water turbine can be increased rapidly, and the efficiency and the output are greatly reduced. Therefore, the determination of the cavitation state of the water turbine in the operation process is very important. Because the internal flow state of the prototype water turbine is invisible, the cavitation judgment method of the prototype water turbine can judge until the cavitation is serious, the efficiency is rapidly reduced, the large-shaft throw is increased and the noise is increased, and the judgment of the cavitation state at the moment is meaningless. A prototype water turbine cavitation state determination method capable of determining a real-time cavitation state must be provided.
Disclosure of Invention
The invention aims to provide a method for identifying the cavitation state of an axial flow water turbine, which accurately judges the cavitation state of the water turbine by analyzing a vibration signal MF-DFA of a draft tube of the axial flow water turbine.
The invention adopts the technical scheme that the method for identifying the cavitation state of the axial flow turbine specifically comprises the following steps:
step 1, collecting a vibration acceleration signal of a water turbine by adopting a vibration acceleration sensor to obtain a time sequence x after sampling the vibration acceleration signal of the water turbinek；
Step 2, utilizing the vibration signal sequence x of the water turbine collected in the step 1kConstructing a dispersion sequence Y (i) of vibration signals, i is 1,2,3, … …, N;
step 3, dividing the dispersion series Y (i) obtained in the step 2 into non-overlapping N with equal length ssA section for dividing the sequence Y (i) into non-overlapping N sections with equal length s from the tailsAn interval; finally obtaining 2NsA plurality of equal-length intervals;
step 4, fitting the mean square error F of each interval obtained in the step 3 by adopting a least square method2(s，v)；
Step 5, trending off F2(s, v) taking the average to obtain a q-wave function: f (q, S) -Sh(q)；
Step 6, performing linear fitting on the change relation of the q-order Hurst index along with the order q;
step 7, calculating linearity of the Hurst index and the order q of the draft tube vibration signal of the axial flow water turbine under different cavitation coefficients based on the fitting result of the step 6, taking the linearity of 0.501 as a standard for judging whether cavitation occurs, and if the linearity is more than 0.501, proving that cavitation occurs, otherwise, cavitation does not occur;
and 8, identifying the cavitation state of the water turbine based on the step 7.
The invention is also characterized in that:
the specific process of the step 2 is as follows:
constructing a dispersion sequence Y (i) of a vibration signal by using an MF-DFA method, wherein i is 1,2,3, … …, N;
Figure BDA0003326811650000031
Figure BDA0003326811650000032
the specific process of the step 4 is as follows:
and fitting the mean square error of each interval of the vibration signal by adopting a least square method:
F2(s，v)，v＝1，2，...，2m；
when v is 1,2, …, m:
Figure BDA0003326811650000033
when v is m +1, m +2, …,2 m:
Figure BDA0003326811650000034
in the formula, yv(i) Is the fitted polynomial of order r of the v-th segment.
In step 5, the function F (q, s) satisfies the following formula:
Figure BDA0003326811650000035
in step 6, the mean value F (q, s) of the q-order fluctuation function and the interval length s have the following relationship:
F(q，s)～Sh(q)；
wherein h (q) is the Hurst index.
The invention has the beneficial effects that: the invention provides a method for identifying a cavitation state based on linearity of a Hurst index for the first time by collecting vibration signals of a water turbine under different working conditions. When the water turbine does not generate cavitation, the vibration signal of the water turbine is a nonlinear non-stationary time sequence, the linearity of the Hurst index is low, the linearity is high, and the linearity of the Hurst index is basically kept unchanged when the cavitation does not occur; when the tail water pipe is subjected to cavitation inception, the linearity of the Hurst index starts to increase, the linear relation starts to deteriorate, and when the cavitation coefficient is reduced again, the cavitation degree increases, the linearity is higher and higher, and the linearity is lower and lower, so that the linearity of the Hurst index and the order q and the cavitation coefficient have a certain rule, and the cavitation state can be identified based on the linearity of the Hurst index, and a cavitation inception point can be found.
Drawings
FIG. 1 is a diagram of vibration acceleration signals collected in the method for identifying cavitation state of an axial flow turbine according to the present invention;
FIG. 2 is a graph showing the relationship between the mean value F (q, s) of the q-order fluctuation function and the interval length s in the method for identifying the cavitation state of the axial flow turbine according to the present invention;
FIG. 3 is a graph showing the linearity of the Hurst index along with the change of the cavitation degree in the method for identifying the cavitation state of the axial flow water turbine.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention relates to a method for identifying the cavitation state of an axial flow turbine, which specifically comprises the following steps,
step 1, collecting a vibration acceleration signal of a water turbine by adopting a vibration acceleration sensor to obtain a time sequence x after sampling the vibration acceleration signal of the water turbinek(ii) a Fig. 1 shows a diagram of a vibration acceleration signal acquired by the present invention.
Step 2, utilizing the collected water turbine vibration signal type nonlinear non-stationary time sequence xk(k ═ 1,2,3 …, N), constructing a dispersion sequence y (i) of the vibration signals, i ═ 1,2,3, … …, N;
Figure BDA0003326811650000051
Figure BDA0003326811650000052
step 3, dividing the vibration signal Y (i) in the step 2 into non-overlapping N with equal length ssAnd each interval contains s data. If N cannot divide s exactly during the calculation, Y (i) will have a part of the data remaining, and the sequence is divided into non-overlapping N with equal length s for the accuracy and completeness of the calculated datasInterval, finally obtaining 2NsAnd a section with equal length contains all the data.
Step 4, fitting the mean square error of each interval of the vibration signal by adopting a least square method
F2(s，v)，v＝1，2，...，2m；
When v is 1,2, …, m:
Figure BDA0003326811650000053
when v is m +1, m +2, …,2 m:
Figure BDA0003326811650000054
in the formula, yv(i) Is the fitted polynomial of order r of the v-th segment.
Step 5, trending off F2(s, v) taking the average, we get the q-wave function: f (q, S) -Sh(q)Then, fitting the logarithm of the function F (q, s) with s, in the process, taking 6 intervals with the interval Scale being 16-1026 equal ratio, determining that the detrending polynomial order r is 2, and the q-order value of the weighted local change is [ -5, 5]And taking the order q as-5, 0 and 5 respectively as analysis display.
Figure BDA0003326811650000061
The mean value F (q, s) of the q-order fluctuation function has the following relationship with the interval length s:
F(q，s)～Sh(q)；
wherein h (q) is the Hurst index.
As shown in fig. 2, it is a graph of the mean F (q, s) of the q-order fluctuation function of the present invention and the section length s.
Step 6, performing linear fitting on the change relation of the q-order Hurst index along with the order q;
and 7, calculating the linearity of the Hurst index and the order q of the draft tube vibration signal of the axial flow water turbine under different cavitation coefficients based on the fitting result of the step 6, and as shown in fig. 3, the linearity is a graph of the change of the linearity of the Hurst index along with the cavitation degree.
Linearity is a characteristic value of the degree of coincidence of a calibration curve approaching a prescribed straight line, and the formula of linearity is as follows:
Figure BDA0003326811650000062
Δ Ymax represents the maximum deviation of the calibration curve from the fitted line, and Y represents the percentage of full scale output.
And 8, identifying the cavitation state of the water turbine based on the step 7, wherein when the linearity is between 0.50 and 0.501, cavitation does not occur, when the linearity is more than 0.501, cavitation starts to occur, and when the cavitation degree increases again, the linearity is correspondingly and continuously increased.

Claims (5)

1. A method for identifying the cavitation state of an axial flow turbine is characterized by comprising the following steps: the method specifically comprises the following steps:
step 1, collecting a vibration acceleration signal of a water turbine by adopting a vibration acceleration sensor to obtain a time sequence x after sampling the vibration acceleration signal of the water turbinek；
Step 2, utilizing the vibration signal sequence x of the water turbine collected in the step 1kConstructing a dispersion sequence Y (i) of vibration signals, i is 1,2,3, … …, N;
step 3, dividing the dispersion series Y (i) obtained in the step 2 into non-overlapping N with equal length ssA section for dividing the sequence Y (i) into non-overlapping N sections with equal length s from the tailsAn interval; finally obtaining 2NsA plurality of equal-length intervals;
step 4, fitting the mean square error F of each interval obtained in the step 3 by adopting a least square method2(s，v)；
Step 5, trending off F2(s, v) taking the average to obtain a q-wave function: f (q, S) -Sh(q)；
Step 6, performing linear fitting on the change relation of the q-order Hurst index along with the order q;
step 7, calculating linearity of the Hurst index and the order q of the draft tube vibration signal of the axial flow water turbine under different cavitation coefficients based on the fitting result of the step 6, taking the linearity of 0.501 as a standard for judging whether cavitation occurs, and if the linearity is more than 0.501, proving that cavitation occurs, otherwise, cavitation does not occur;
and 8, identifying the cavitation state of the water turbine based on the step 7.
2. The method for identifying the cavitation state of the axial flow turbine as claimed in claim 1, wherein: the specific process of the step 2 is as follows:
constructing a dispersion sequence Y (i) of a vibration signal by using an MF-DFA method, wherein i is 1,2,3, … …, N;
Figure FDA0003326811640000021
Figure FDA0003326811640000022
3. the method for identifying the cavitation state of the axial flow turbine as claimed in claim 2, wherein: the specific process of the step 4 is as follows:
fitting the mean square error of each interval of the vibration signal by using a least square method
F2(s，v)，v＝1，2，...，2m；
When v is 1,2, …, m:
Figure FDA0003326811640000023
when v is m +1, m +2, …,2 m:
Figure FDA0003326811640000024
in the formula, yv(i) Is the fitted polynomial of order r of the v-th segment.
4. The method for identifying the cavitation state of the axial flow turbine as claimed in claim 3, wherein: in step 5, the function F (q, s) satisfies the following formula:
Figure FDA0003326811640000025
5. the method for identifying the cavitation state of the axial flow turbine as claimed in claim 4, wherein: in step 6, the mean value F (q, s) of the q-order fluctuation function and the interval length s have the following relationship:
F(q，s)～Sh(q)；
wherein h (q) is the Hurst index.
CN202111265390.1A 2021-10-28 2021-10-28 Identification method for cavitation state of axial flow turbine Active CN114091368B (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CN202111265390.1A CN114091368B (en) 2021-10-28 2021-10-28 Identification method for cavitation state of axial flow turbine
NL2031029A NL2031029A (en) 2021-10-28 2022-02-22 Method for identifying cavitation state of axial flow hydraulic turbine

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111265390.1A CN114091368B (en) 2021-10-28 2021-10-28 Identification method for cavitation state of axial flow turbine

Publications (2)

Publication Number Publication Date
CN114091368A true CN114091368A (en) 2022-02-25
CN114091368B CN114091368B (en) 2024-06-21

Family

ID=80298061

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111265390.1A Active CN114091368B (en) 2021-10-28 2021-10-28 Identification method for cavitation state of axial flow turbine

Country Status (2)

Country Link
CN (1) CN114091368B (en)
NL (1) NL2031029A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114718793A (en) * 2022-04-22 2022-07-08 西安理工大学 Method for identifying cavitation state of through-flow turbine
CN115048746A (en) * 2022-07-05 2022-09-13 西安理工大学 Method for calculating vibration probability density curve of rotating wheel of full-through-flow turbine

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114091368B (en) * 2021-10-28 2024-06-21 西安理工大学 Identification method for cavitation state of axial flow turbine

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20100027087A (en) * 2009-11-30 2010-03-10 이우성 For the ocean a generating plant in global village zero calmity tidal power generator used with in it's a level pipes seesow type
JP2011157894A (en) * 2010-02-02 2011-08-18 Hitachi Plant Technologies Ltd Method and device for predicting cavitation erosion quantity
CN112065629A (en) * 2020-08-06 2020-12-11 西安理工大学 Method for detecting clearance cavitation primary of through-flow turbine
CN114718793A (en) * 2022-04-22 2022-07-08 西安理工大学 Method for identifying cavitation state of through-flow turbine
NL2031029A (en) * 2021-10-28 2023-05-23 Univ Xian Technology Method for identifying cavitation state of axial flow hydraulic turbine

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101813568B (en) * 2009-12-07 2011-07-27 哈尔滨电机厂有限责任公司 Judging method for determining cavitation inception
CN113155266B (en) * 2021-03-08 2022-11-01 西安理工大学 Water turbine cavitation initiation determination method integrating vibration test and pressure pulsation test

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20100027087A (en) * 2009-11-30 2010-03-10 이우성 For the ocean a generating plant in global village zero calmity tidal power generator used with in it's a level pipes seesow type
JP2011157894A (en) * 2010-02-02 2011-08-18 Hitachi Plant Technologies Ltd Method and device for predicting cavitation erosion quantity
CN112065629A (en) * 2020-08-06 2020-12-11 西安理工大学 Method for detecting clearance cavitation primary of through-flow turbine
NL2031029A (en) * 2021-10-28 2023-05-23 Univ Xian Technology Method for identifying cavitation state of axial flow hydraulic turbine
CN114718793A (en) * 2022-04-22 2022-07-08 西安理工大学 Method for identifying cavitation state of through-flow turbine

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
JIANJUN FENG 等: "Cavitation detection in a Kaplan turbine based on multifractal detrended fluctuation analysis of vibration signals", OCEAN ENGINEERING 263 (2022) 112232, 9 September 2022 (2022-09-09), pages 1 - 8 *
曹玉良;明廷锋;贺国;苏永生;: "基于深度学习的离心泵空化状态识别", 西安交通大学学报, no. 11, 31 December 2017 (2017-12-31), pages 171 - 178 *
门羿: "基于振动信号的轴流式水轮机空化状态识别", 中国优秀硕士学位论文全文数据库 工程科技II辑, 15 February 2024 (2024-02-15), pages 037 - 147 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114718793A (en) * 2022-04-22 2022-07-08 西安理工大学 Method for identifying cavitation state of through-flow turbine
CN114718793B (en) * 2022-04-22 2024-02-06 西安理工大学 Through-flow turbine cavitation state identification method
CN115048746A (en) * 2022-07-05 2022-09-13 西安理工大学 Method for calculating vibration probability density curve of rotating wheel of full-through-flow turbine

Also Published As

Publication number Publication date
CN114091368B (en) 2024-06-21
NL2031029A (en) 2023-05-23

Similar Documents

Publication Publication Date Title
CN114091368A (en) Method for identifying cavitation state of axial flow turbine
CN101813568B (en) Judging method for determining cavitation inception
CN101900789B (en) Tolerance analog circuit fault diagnosing method based on wavelet transform and fractal dimension
CN107860548B (en) Approximate online flutter boundary prediction method
CN108981796B (en) Five-in-one hydraulic mechanical fault diagnosis method
CN116510223B (en) Self-identification fire pump electrical parameter inspection monitoring system based on Internet of things
CN102022348B (en) Water pump cavitation measuring method
CN111415070A (en) Wind turbine generator gearbox oil temperature over-temperature fault early warning method based on SCADA data
CN116956047B (en) Wind turbine generator system performance evaluation system based on wind power generation data
CN112065629B (en) Method for detecting clearance cavitation primary of through-flow turbine
CN110879927A (en) Sea clutter amplitude statistical distribution field modeling method for sea target detection
CN112267972A (en) Intelligent judgment method for abnormity of power curve of wind turbine generator
CN113901379A (en) Dynamic online rapid processing method for real-time data of edge terminal
CN118072994A (en) Nuclear island equipment cooling water heat exchanger health state monitoring and fault diagnosis method
CN102539823A (en) Method for forecasting wind speed distribution of WTG (wind turbine generator)
CN114718793B (en) Through-flow turbine cavitation state identification method
CN110298767A (en) A kind of thermal power plant time series variable method for monitoring abnormality and system
CN115095535A (en) Industrial pump operation multi-parameter detector
CN109520643B (en) Sensor for measuring weak stress-strain signal
CN117189720B (en) Fault diagnosis method for hydraulic system of aircraft
CN115048746B (en) Method for calculating vibration probability density curve of runner of full-through-flow turbine
CN117520983B (en) Structural damage identification robust method based on electromechanical impedance and CNN
CN111539096A (en) Electric drive system reliability evaluation method based on real-time test parameters
CN111735528B (en) Method for accurately positioning disturbance point based on KNN voting
CN117195152B (en) Under-dam tributary jacking condition analysis system based on deep learning

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant