CN111413404A - On-line measurement method of blade crack based on blade tip timing and support vector machine principle - Google Patents
On-line measurement method of blade crack based on blade tip timing and support vector machine principle Download PDFInfo
- Publication number
- CN111413404A CN111413404A CN202010231636.2A CN202010231636A CN111413404A CN 111413404 A CN111413404 A CN 111413404A CN 202010231636 A CN202010231636 A CN 202010231636A CN 111413404 A CN111413404 A CN 111413404A
- Authority
- CN
- China
- Prior art keywords
- blade
- vibration
- tip timing
- support vector
- crack
- 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.)
- Pending
Links
- 238000012706 support-vector machine Methods 0.000 title claims abstract description 31
- 238000000691 measurement method Methods 0.000 title claims abstract description 9
- 238000012549 training Methods 0.000 claims abstract description 23
- 238000013145 classification model Methods 0.000 claims abstract description 19
- 238000006073 displacement reaction Methods 0.000 claims abstract description 19
- 238000005259 measurement Methods 0.000 claims description 17
- 238000000034 method Methods 0.000 claims description 13
- 238000010606 normalization Methods 0.000 claims description 12
- 238000004422 calculation algorithm Methods 0.000 claims description 11
- 238000012544 monitoring process Methods 0.000 claims description 11
- 238000012360 testing method Methods 0.000 claims description 7
- 238000009434 installation Methods 0.000 claims description 6
- 238000004088 simulation Methods 0.000 claims description 6
- 238000002474 experimental method Methods 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 claims 1
- 238000012545 processing Methods 0.000 claims 1
- 238000005070 sampling Methods 0.000 claims 1
- 230000006870 function Effects 0.000 description 4
- 238000001514 detection method Methods 0.000 description 3
- 206010011376 Crepitations Diseases 0.000 description 2
- 208000037656 Respiratory Sounds Diseases 0.000 description 2
- 239000008358 core component Substances 0.000 description 2
- 230000007812 deficiency Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007635 classification algorithm Methods 0.000 description 1
- 238000002790 cross-validation Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000003628 erosive effect Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000004044 response Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/04—Analysing solids
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/04—Analysing solids
- G01N29/12—Analysing solids by measuring frequency or resonance of acoustic waves
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
- G01N29/4409—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison
- G01N29/4418—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison with a model, e.g. best-fit, regression analysis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/01—Indexing codes associated with the measuring variable
- G01N2291/014—Resonance or resonant frequency
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/02—Indexing codes associated with the analysed material
- G01N2291/028—Material parameters
- G01N2291/0289—Internal structure, e.g. defects, grain size, texture
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/26—Scanned objects
- G01N2291/269—Various geometry objects
- G01N2291/2693—Rotor or turbine parts
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/26—Scanned objects
- G01N2291/269—Various geometry objects
- G01N2291/2698—Other discrete objects, e.g. bricks
Landscapes
- Physics & Mathematics (AREA)
- Biochemistry (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Acoustics & Sound (AREA)
- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
本发明涉及一种基于叶尖定时和支持向量机原理的叶片裂纹在线测量方法,在旋转机械的机匣不同位置设置多支叶尖定时传感器,在模拟工况下,通过叶尖定时传感器测量叶片到达每支叶尖定时传感器的到达时间,测得叶片的振动位移、振幅和振动频率叶片振动数据,包括无裂纹叶片和有裂纹叶片的振动数据,将无裂纹叶片的振动数据标记为正类样本,有裂纹叶片的振动数据标记为负类样本,送入支持向量机进行分类训练并建立分类模型,利用训练好的分类模型实现运行工况下旋转机械叶片裂纹的在线测量。
The invention relates to an on-line measurement method for blade cracks based on blade tip timing and support vector machine principles. Multiple blade tip timing sensors are arranged at different positions of a casing of a rotating machine, and under simulated working conditions, blades are measured through the blade tip timing sensors. The arrival time of the timing sensor at the tip of each blade is measured, and the vibration displacement, amplitude and vibration frequency of the blade are measured. The vibration data of the blade, including the vibration data of the blade without cracks and the blades with cracks, are marked as positive samples. , the vibration data of cracked blades are marked as negative samples, which are sent to the support vector machine for classification training and a classification model is established.
Description
技术领域technical field
本发明属于旋转机械状态监测领域,特别是基于叶尖定时和支持向量机原理的叶片裂纹在线测量方法。The invention belongs to the field of rotating machinery state monitoring, in particular to an on-line measurement method for blade cracks based on blade tip timing and support vector machine principles.
技术背景technical background
大型旋转机械包括航空发动机和汽轮机等大型设备,是航空航天领域中的各类军用、商用航空器以及工业领域的发电机组和蒸汽机组等关键设备的核心部件。尤其是叶片作为旋转机械做功的核心元件,其工作状态直接影响这些关键设备的工作效率和安全稳定运行。旋转机械叶片的工作环境非常恶劣,长时间处于高应力、高低温或高冲刷等严酷条件。这些外界条件对大型旋转机械的叶片会产生复杂的周期性或非周期变化应力,当应力超过叶片材料的屈服强度极限时会导致叶片产生裂纹,进而可能发生叶片断裂。叶片裂纹是导致大型旋转机械故障的主要原因之一,因此准确测量叶片的振动参数并在线测量叶片是否产生裂纹,可以对叶片故障进行实时预警,对航空发动机和汽轮机等重大旋转机械的研发测试、状态监测和故障诊断等方面具有非常重要的实际意义。Large-scale rotating machinery includes large-scale equipment such as aero-engines and steam turbines, and is the core component of various military and commercial aircraft in the aerospace field, as well as key equipment such as generator sets and steam generators in the industrial field. In particular, blades are the core components of rotating machinery, and their working states directly affect the work efficiency and safe and stable operation of these key equipment. The working environment of rotating machinery blades is very harsh, and they are exposed to severe conditions such as high stress, high and low temperature or high erosion for a long time. These external conditions will produce complex periodic or non-periodic stress on the blades of large rotating machinery. When the stress exceeds the yield strength limit of the blade material, cracks will occur in the blade, and then the blade may break. Blade cracks are one of the main causes of large-scale rotating machinery failures. Therefore, accurate measurement of the vibration parameters of the blades and online measurement of whether the blades have cracks can provide real-time early warning of blade failures. Condition monitoring and fault diagnosis have very important practical significance.
基于叶尖定时原理的旋转叶片振动测量技术[1-3]是典型的非接触式测量方法,基本原理是将一定数量的传感器设置在旋转机械的机匣上,测量每支叶片旋转经过传感器时的到达时间,利用相关数学算法实现振动位移、振幅和振动频率等叶片振动参数的在线测量。与传统的离线式叶片状态检测方法和应变片法、频率调制法和声响法等在线检测方法相比,叶尖定时技术具有非接触、实时在线和可测量全部叶片等优点,具有很好的工程实用性。支持向量机是一种基于统计学理论的机器学习方法,可用于线性和非线性数据的分类问题。支持向量机最初即设计用于解决二分类问题,其主要思想是寻找一个最优分类超平面,使训练集中的正负类样本距离最优分类超平面的距离最大,并利用最优分类超平面对被测样本进行分类。因此,通过叶尖定时方法获得正常叶片和有裂纹叶片的振动数据后,再利用支持向量机算法进行训练并建立分类模型,进而实现旋转机械实际运行工况下叶片裂纹的在线测量。The rotating blade vibration measurement technology based on the blade tip timing principle [1-3 ] is a typical non-contact measurement method. The on-line measurement of blade vibration parameters such as vibration displacement, amplitude and vibration frequency is realized by using relevant mathematical algorithms. Compared with traditional off-line blade condition detection methods and online detection methods such as strain gage method, frequency modulation method and acoustic method, blade tip timing technology has the advantages of non-contact, real-time online measurement of all blades, etc. practicality. Support Vector Machine is a machine learning method based on statistical theory, which can be used for classification problems of linear and nonlinear data. The support vector machine was originally designed to solve the binary classification problem. Classify the tested samples. Therefore, after the vibration data of normal blades and cracked blades are obtained by the blade tip timing method, the support vector machine algorithm is used to train and establish a classification model, so as to realize the online measurement of blade cracks under the actual operating conditions of rotating machinery.
目前,对于旋转机械叶片的裂纹测量均依靠离线检测技术,无法满足大型旋转机械在线测量叶片裂纹的实际需求。At present, the crack measurement of rotating machinery blades relies on offline detection technology, which cannot meet the actual needs of online measurement of blade cracks in large rotating machinery.
[1]欧阳涛.基于叶尖定时的旋转叶片振动检测及参数辨识技术[D].天津大学,2011.[1] Ouyang Tao. Rotating blade vibration detection and parameter identification technology based on blade tip timing [D]. Tianjin University, 2011.
[2]赵行明,滕光蓉等.叶尖定时旋转叶片振动测量新技术[J].测控技术,2006(03):17-19.[2] Zhao Xingming, Teng Guangrong, et al. New technology of blade tip timing rotating blade vibration measurement [J]. Measurement and Control Technology, 2006(03):17-19.
[3]王萍.叶尖定时方法在国外航空发动机叶片振动测量中的应用综述[J].航空科学技术,2013(06):9-13.[3] Wang Ping. Review of the application of blade tip timing method in vibration measurement of foreign aero-engine blades [J]. Aeronautical Science and Technology, 2013(06):9-13.
发明内容SUMMARY OF THE INVENTION
本发明的目的是针对上述现有技术存在的不足,提供一种基于叶尖定时和支持向量机原理的叶片裂纹在线测量方法,通过叶尖定时方法获得无裂纹叶片和有裂纹叶片的振动数据,利用支持向量机算法进行有、无裂纹叶片的分类训练并建立支持向量机分类模型,进而实现工作状态下旋转机械叶片裂纹的在线测量。The purpose of the present invention is to aim at the deficiencies of the above-mentioned prior art, provide a kind of blade crack on-line measurement method based on blade tip timing and support vector machine principle, obtain the vibration data of the crackless blade and the cracked blade by the blade tip timing method, The support vector machine algorithm is used to classify and train the blades with and without cracks, and a support vector machine classification model is established to realize the online measurement of cracks in rotating machinery blades under working conditions.
本发明的技术方案如下:The technical scheme of the present invention is as follows:
一种基于叶尖定时和支持向量机原理的叶片裂纹在线测量方法,在旋转机械的机匣不同位置设置多支叶尖定时传感器,在模拟工况下,通过叶尖定时传感器测量叶片到达每支叶尖定时传感器的到达时间,测得叶片的振动位移、振幅和振动频率叶片振动数据,包括无裂纹叶片和有裂纹叶片的振动数据,将无裂纹叶片的振动数据标记为正类样本,有裂纹叶片的振动数据标记为负类样本,送入支持向量机进行分类训练并建立分类模型,利用训练好的分类模型实现运行工况下旋转机械叶片裂纹的在线测量。包括下列步骤:An online measurement method for blade cracks based on blade tip timing and support vector machine principles. Multiple blade tip timing sensors are set at different positions of the casing of the rotating machine. Under simulated working conditions, the blade tip timing sensor measures the arrival of each blade The arrival time of the blade tip timing sensor, the vibration displacement, amplitude and vibration frequency of the blade are measured. The blade vibration data includes the vibration data of the non-cracked blade and the cracked blade. The vibration data of the non-cracked blade is marked as a positive sample with cracks. The vibration data of the blade are marked as negative samples, which are sent to the support vector machine for classification training and a classification model is established. Include the following steps:
(1)在旋转机械机匣的不同位置设置多支叶尖定时传感器,用于测量不同转速下叶片到达每支叶尖定时传感器的时间;(1) Set up multiple blade tip timing sensors at different positions of the rotating machinery casing to measure the time when the blade reaches each blade tip timing sensor at different speeds;
(2)在叶片完好没有裂纹时进行模拟工况试验,将叶尖定时传感器测量的每支叶片到达时间信号送入叶片状态监测系统,结合多支叶尖定时传感器的安装位置、旋转机械的不同转速和叶尖定时算法,计算得到无裂纹叶片在不同转速下的振动位移、振幅、振动频率、初始相位、振动常偏量和共振倍频数叶片振动数据;(2) Carry out a simulated working condition test when the blade is intact and without cracks, and send the arrival time signal of each blade measured by the blade tip timing sensor to the blade condition monitoring system. Combined with the installation positions of multiple blade tip timing sensors and the difference in rotating machinery Rotation speed and blade tip timing algorithms are used to calculate the vibration displacement, amplitude, vibration frequency, initial phase, vibration constant deviation and resonance multiplier of the blade without cracks at different speeds;
(3)对于有裂纹叶片,进行模拟工况实验,获得有裂纹叶片在不同转速下的振动位移、振幅、振动频率、初始相位、振动常偏量和共振倍频数叶片振动数据;(3) For the cracked blade, carry out a simulated working condition experiment to obtain the vibration displacement, amplitude, vibration frequency, initial phase, vibration constant deviation and resonance frequency multiplier of the cracked blade at different speeds;
(4)将无裂纹叶片的振动数据设置为正类样本,数据标签设置为0;将有裂纹叶片的振动数据设置为负类样本,数据标签设置为1;(4) Set the vibration data of the blade without cracks as a positive sample, and set the data label to 0; set the vibration data of the blade with cracks to be a negative sample, and set the data label to 1;
(5)以负类样本总数为参考,利用等间隔下采样方法对正类样本进行采样以实现正负样本均衡,均衡后的正类样本和负类样本组合成为训练集;(5) Taking the total number of negative samples as a reference, the positive samples are sampled by the equal interval downsampling method to achieve the balance of positive and negative samples, and the balanced positive samples and negative samples are combined into a training set;
(6)利用最小最大归一化算法,对训练集中的数据进行归一化化处理,消除量纲;(6) Use the min-max normalization algorithm to normalize the data in the training set to eliminate the dimension;
(7)根据消除量纲后的训练集,利用支持向量机算法进行分类训练,建立支持向量机分类模型;(7) According to the training set after eliminating the dimension, use the support vector machine algorithm to perform classification training, and establish a support vector machine classification model;
(8)当旋转机械在工作状态运行时,根据叶尖定时传感器实测获得的叶片到达时间,利用叶片状态监测系统计算得到叶片在工作状态下不同转速的振动位移、振幅、振动频率、初始相位、振动常偏量和共振倍频数叶片振动数据;(8) When the rotating machine is running in the working state, according to the arrival time of the blade obtained by the blade tip timing sensor, the blade state monitoring system is used to calculate the vibration displacement, amplitude, vibration frequency, initial phase, Vibration constant and resonance frequency multiplier blade vibration data;
(9)将工作状态测得的叶片振动数据作为待测样本,经过归一化处理后,送入叶片状态监测系统中训练好的支持向量机分类模型,通过支持向量机分类模型计算得到的待测样本标签是0或1,(9) The blade vibration data measured in the working state is used as the sample to be tested, and after normalization, it is sent to the SVM classification model trained in the blade state monitoring system, and the pending SVM classification model is calculated by the SVM classification model. The test sample label is 0 or 1,
实现旋转机械叶片裂纹的在线测量。Realize online measurement of cracks in rotating machinery blades.
本发明的有益效果及优点如下:The beneficial effects and advantages of the present invention are as follows:
本发明的方法克服了现有旋转机械叶片裂纹测量技术的不足,提供一种基于叶尖定时和支持向量机原理的叶片裂纹在线测量方法,通过叶尖定时方法在模拟工况下测得无裂纹和有裂纹叶片的振动数据,结合支持向量机算法建立无裂纹叶片和有裂纹叶片的分类模型,利用训练好的分类模型实现旋转机械叶片裂纹的在线测量。The method of the invention overcomes the shortcomings of the existing rotating machinery blade crack measurement technology, and provides an online blade crack measurement method based on the blade tip timing and the support vector machine principle. And the vibration data of the cracked blade, combined with the support vector machine algorithm to establish the classification model of the crackless blade and the cracked blade, and use the trained classification model to realize the online measurement of the crack of the rotating machinery blade.
附图说明Description of drawings
以下附图描述了本发明所选择的实施例,均为示例性附图而非穷举或限制性,其中:Selected embodiments of the present invention are described in the following figures, which are illustrative and not exhaustive or limiting, in which:
图1示出基于叶尖定时和支持向量机原理的叶片裂纹在线测量结构图Figure 1 shows the structure diagram of blade crack online measurement based on blade tip timing and support vector machine principle
图2示出支持向量机分类算法原理图Figure 2 shows the schematic diagram of the support vector machine classification algorithm
图中标号说明:Description of the labels in the figure:
图1中:1为叶尖定时传感器A;2为叶尖定时传感器B;3为叶尖定时传感器C;4为为叶尖定时传感器D;5为旋转机械机匣;6为叶片;7为叶片状态监测系统;In Figure 1: 1 is the tip timing sensor A; 2 is the tip timing sensor B; 3 is the tip timing sensor C; 4 is the tip timing sensor D; 5 is the rotating machine casing; 6 is the blade; 7 is the blade Blade condition monitoring system;
图2中:8为最优分类超平面;9为正类支持向量;10为负类支持向量。In Figure 2: 8 is the optimal classification hyperplane; 9 is the positive class support vector; 10 is the negative class support vector.
具体实施方式Detailed ways
以下详细描述本发明的步骤,旨在作为本发明的实施例描述,并非是可被制造或利用的唯一形式,对其他可实现相同功能的实施例也应包括在本发明的范围内。The steps of the present invention are described in detail below, and are intended to be described as embodiments of the present invention, not the only form that can be manufactured or utilized, and other embodiments that can achieve the same function should also be included within the scope of the present invention.
下面结合说明书附图详细说明本发明的优选实施例。The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
本发明的目的是克服现有旋转机械叶片裂纹无法实现在线测量这一不足,提出一种基于叶尖定时和支持向量机原理的叶片裂纹在线测量方法;The purpose of the present invention is to overcome the deficiency that the existing rotating machinery blade cracks cannot be measured online, and proposes an online blade crack measurement method based on the blade tip timing and the support vector machine principle;
(一).基于叶尖定时和支持向量机原理的叶片裂纹在线测量结构如图1所示,在旋转机械机匣5的任意不同位置设置四支叶尖定时传感器,包括叶尖定时传感器A1,叶尖定时传感器B2,叶尖定时传感器C3和叶尖定时传感器D4,四支叶尖定时传感器都可以测量每支叶片在同一圈内到达叶尖定时传感器的时间;(1) The online measurement structure of blade crack based on blade tip timing and support vector machine principle is shown in Figure 1. Four blade tip timing sensors are installed at any different positions of the rotating machinery casing 5, including the blade tip timing sensor A1, Tip timing sensor B2, tip timing sensor C3 and tip timing sensor D4, four tip timing sensors can measure the time when each blade reaches the tip timing sensor in the same circle;
(二).在叶片无裂纹情况下,将旋转机械放入旋转机械工况模拟试验台,模拟旋转机械的运行情况,假设旋转机械顺时针旋转,当叶片6受激发生同步振动时,四支叶尖定时传感器测得叶片6的到达时间信号被送入叶片状态监测系统7,根据四支叶尖定时传感器的安装位置、旋转机械不同转速和叶片受激振动响应方程,同一圈内叶片6先后经过四支叶尖定时传感器的振动位移方程组为:(2) When the blade has no cracks, put the rotating machine into the rotating machine condition simulation test bench to simulate the operation of the rotating machine. Assuming that the rotating machine rotates clockwise, when the blade 6 is stimulated to vibrate synchronously, the four The arrival time signal of the blade 6 measured by the blade tip timing sensor is sent to the blade
式中,y0为叶片6经过叶尖定时传感器A1时的振动位移,y1为叶片6经过叶尖定时传感器B2时的振动位移,y2为叶片6经过叶尖定时传感器C3时的振动位移,y3为叶片6经过叶尖定时传感器D4时的振动位移,A是叶片6的振幅,为叶片6的初始相位,C为叶片6的振动常偏量,N为叶片6的共振倍频数,α1为叶尖定时传感器B2相对于叶尖定时传感器A1的安装弧度角,α2为叶尖定时传感器C3相对于叶尖定时传感器A1的安装弧度角,α3为叶尖定时传感器D4相对于叶尖定时传感器A1的安装弧度角,利用遍历算法将N所有的可能取值带入式(1),结合最小二乘法可以求解叶片6无裂纹时的振动频率ω、初始相位振幅A和振动常偏量C;In the formula, y 0 is the vibration displacement of the blade 6 when it passes the tip timing sensor A1, y 1 is the vibration displacement of the blade 6 when it passes the tip timing sensor B2, and y 2 is the vibration displacement of the blade 6 when it passes the tip timing sensor C3. , y 3 is the vibration displacement of the blade 6 when it passes through the tip timing sensor D4, A is the amplitude of the blade 6, is the initial phase of the blade 6, C is the vibration constant deviation of the blade 6, N is the resonance multiplier of the blade 6, α1 is the installation radian angle of the blade tip timing sensor B2 relative to the blade tip timing sensor A1, α2 is the blade The installation radian angle of the tip timing sensor C3 relative to the tip timing sensor A1, α3 is the installation radian angle of the tip timing sensor D4 relative to the blade tip timing sensor A1, and the traversal algorithm is used to bring all possible values of N into the formula ( 1), combined with the least squares method, the vibration frequency ω and initial phase of the blade 6 without cracks can be solved Amplitude A and vibration constant C;
将叶片6人工制造所需裂纹后装入旋转机械,再次利用旋转机械工况模拟试验台重复前述模拟运行过程,可以获得相同转速下有裂纹叶片6经过叶尖定时传感器A1时的振动位移y'0、经过叶尖定时传感器B2时的振动位移y1'、经过叶尖定时传感器C3时的振动位移y'2和经过叶尖定时传感器D4时的振动位移y'3,并求得有裂纹叶片6的振动频率ω'、初始相位振幅A'、振动常偏量C'和共振倍频数N';The blade 6 is artificially manufactured with the required cracks and then loaded into the rotating machine, and the above-mentioned simulation operation process is repeated by using the rotating machine working condition simulation test bench again, and the vibration displacement y' of the cracked blade 6 passing through the tip timing sensor A1 at the same rotational speed can be obtained. 0. The vibration displacement y 1 ' when passing the tip timing sensor B2, the vibration displacement y' 2 when passing the tip timing sensor C3 and the vibration displacement y' 3 when passing the tip timing sensor D4, and obtain the cracked blade 6 vibration frequency ω', initial phase Amplitude A', vibration constant C' and resonance frequency N';
(三).将无裂纹叶片6在不同转速下测得的叶片振动数据y0、y1、y2、y3、A、C和N设为正类样本、数据标签设置为0;将有裂纹叶片6在不同转速下测得的叶片振动数据y'0、y1'、y'2、y'3、A'、C'和N'设为负类样本,数据标签设置为1;(3). The blade vibration data y 0 , y 1 , y 2 , y 3 , A, and C and N are set as positive samples, and the data label is set as 0; the blade vibration data y' 0 , y 1 ', y' 2 , y' 3 , A', C' and N' are set as negative class samples, and the data label is set to 1;
(四).支持向量机是一种基于统计学习理论的机器学习方法,适用于线性和非线性数据分类,如图2所示,其主要思想是寻找一个最优分类超平面8,使训练集中正类样本和负类样本到最优分类超平面8的距离最大,并利用最优分类超平面8对被测样本进行分类,测试集的正类样本中距离最优分类超平面8最近的点称为正类支持向量9,测试集的负类样本中距离最优分类超平面8最近的点称为负类支持向量10,最优分类超平面8的确定只依赖于正类样本支持向量9和负类样本支持向量10,非支持向量点均不起作用;(4) Support vector machine is a machine learning method based on statistical learning theory, suitable for linear and nonlinear data classification, as shown in Figure 2, its main idea is to find an
由于前述正类样本通常会多于负类样本,因此以负类样本总数为参考,利用等间隔下采样方法对大量正类样本进行采样以实现正负样本均衡,样本均衡后的正类样本和负类样本组合成为训练集,训练集中每一行代表一个样本,每一列代表叶片振动数据的实测值;Since the aforementioned positive samples are usually more than negative samples, the total number of negative samples is used as a reference, and a large number of positive samples are sampled by the equal interval downsampling method to achieve positive and negative sample balance. The negative class samples are combined into a training set, each row in the training set represents a sample, and each column represents the measured value of the blade vibration data;
由于训练集中的数据单位不同,数值量级也不同,因此采用最大最小归一化方法对训练集进行消除量纲处理,提高训练集质量,最小最大归一化方程为:Since the data units in the training set are different and the magnitudes of the values are also different, the maximum and minimum normalization method is used to eliminate the dimension of the training set to improve the quality of the training set. The minimum and maximum normalization equation is:
式中,为训练集第j列归一化后的数据,xij为训练集第j列归一化前的数据,为训练集第j列数据归一化前的均值,xjmax是训练集第j列数归一化前的最大值,xjmin是训练集第j列数据归一化前的最小值,消除量纲后的训练集送入支持向量机进行分类训练,支持向量机的核函数选择高斯径向基核函数,并利用网格搜索和交叉验证方法对高斯径向基核函数的核宽和支持向量机的软间隔惩罚系数进行参数优化,求解最优参数,建立支持向量机分类模型;In the formula, is the normalized data of the jth column of the training set, x ij is the data before the normalization of the jth column of the training set, is the mean value of the data in the jth column of the training set before normalization, x jmax is the maximum value before the normalization of the jth column of the training set, and x jmin is the minimum value before the normalization of the jth column of the training set. The post-class training set is sent to the support vector machine for classification training. The kernel function of the support vector machine selects the Gaussian radial basis kernel function, and uses grid search and cross-validation methods to analyze the kernel width and support vector of the Gaussian radial basis kernel function. The soft interval penalty coefficient of the machine is used to optimize the parameters, solve the optimal parameters, and establish a support vector machine classification model;
(五).当旋转机械在工作状态运行时,通过前述叶尖定时方法可以求得叶片6在不同转速工作状态的叶片振动数据y”0,y”1,y”2,y”3,A”,C”和N”,并作为待测样本,每一个待测样本利用下式进行同比例归一化:(5) When the rotating machine is running in the working state, the blade vibration data y” 0 , y” 1 , y” 2 , y” 3 , A of the blade 6 in different rotating speed working states can be obtained by the aforementioned blade tip timing method ", C" and N" are used as samples to be tested, and each sample to be tested is normalized in the same proportion using the following formula:
式中,为待测样本同比例归一化后第j列的数据,Xij为待测样本同比例归一化前第j列的数据;In the formula, is the data of the jth column after normalization of the sample to be tested in the same proportion, X ij is the data of the jth column before the normalization of the same proportion of the sample to be tested;
(六).将同比例归一化后的待测样本送入训练好的支持向量机分类模型,如果支持向量机分类模型计算得出待测样本的数据标签是0,说明叶片6没有产生裂纹,如果支持向量机分类模型计算得出待测样本的数据标签是1,说明叶片6产生了裂纹,此时通过叶片状态监测系统7给出声光报警提示,实现旋转机械叶片裂纹的在线测量。(6) Send the sample to be tested normalized to the same proportion into the trained SVM classification model. If the SVM classification model calculates that the data label of the sample to be tested is 0, it means that the blade 6 has no cracks. , if the support vector machine classification model calculates that the data label of the sample to be tested is 1, it means that a crack has occurred in the blade 6. At this time, the blade
Claims (1)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010231636.2A CN111413404A (en) | 2020-03-27 | 2020-03-27 | On-line measurement method of blade crack based on blade tip timing and support vector machine principle |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010231636.2A CN111413404A (en) | 2020-03-27 | 2020-03-27 | On-line measurement method of blade crack based on blade tip timing and support vector machine principle |
Publications (1)
Publication Number | Publication Date |
---|---|
CN111413404A true CN111413404A (en) | 2020-07-14 |
Family
ID=71491482
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202010231636.2A Pending CN111413404A (en) | 2020-03-27 | 2020-03-27 | On-line measurement method of blade crack based on blade tip timing and support vector machine principle |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN111413404A (en) |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112082742A (en) * | 2020-07-22 | 2020-12-15 | 西安交通大学 | A method, system and device for intelligent identification of aero-engine wheel disc cracks |
CN112364886A (en) * | 2020-10-14 | 2021-02-12 | 天津大学 | Blade crack online measurement method based on blade tip timing and random forest |
CN112464148A (en) * | 2020-10-14 | 2021-03-09 | 天津大学 | Blade crack measuring method based on blade tip timing and whole-process optimization SVM |
CN113504309A (en) * | 2021-05-18 | 2021-10-15 | 西安交通大学 | Blade detection method based on single blade end timing sensor |
CN113533529A (en) * | 2021-05-18 | 2021-10-22 | 西安交通大学 | A method of extracting the natural frequency difference between leaves by single or evenly distributed blade tip timing sensors |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2008215936A (en) * | 2007-03-01 | 2008-09-18 | Tokyo Electric Power Co Inc:The | Ultrasonic flaw detection method for gas turbine blades |
CN103984952A (en) * | 2014-04-18 | 2014-08-13 | 广东电网公司电力科学研究院 | Method for diagnosing surface crack fault of blade of wind turbine generator of electric power system based on machine vision image |
CN107144569A (en) * | 2017-04-27 | 2017-09-08 | 西安交通大学 | The fan blade surface defect diagnostic method split based on selective search |
CN108459027A (en) * | 2018-03-21 | 2018-08-28 | 华北电力大学 | A kind of blade of wind-driven generator detection method of surface flaw based on image procossing |
CN108956075A (en) * | 2018-08-31 | 2018-12-07 | 天津大学 | Movable vane piece crackle inline diagnosis method |
CN110686764A (en) * | 2019-09-17 | 2020-01-14 | 天津大学 | Method for measuring asynchronous vibration frequency of constant-speed blade based on full-phase difference principle |
-
2020
- 2020-03-27 CN CN202010231636.2A patent/CN111413404A/en active Pending
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2008215936A (en) * | 2007-03-01 | 2008-09-18 | Tokyo Electric Power Co Inc:The | Ultrasonic flaw detection method for gas turbine blades |
CN103984952A (en) * | 2014-04-18 | 2014-08-13 | 广东电网公司电力科学研究院 | Method for diagnosing surface crack fault of blade of wind turbine generator of electric power system based on machine vision image |
CN107144569A (en) * | 2017-04-27 | 2017-09-08 | 西安交通大学 | The fan blade surface defect diagnostic method split based on selective search |
CN108459027A (en) * | 2018-03-21 | 2018-08-28 | 华北电力大学 | A kind of blade of wind-driven generator detection method of surface flaw based on image procossing |
CN108956075A (en) * | 2018-08-31 | 2018-12-07 | 天津大学 | Movable vane piece crackle inline diagnosis method |
CN110686764A (en) * | 2019-09-17 | 2020-01-14 | 天津大学 | Method for measuring asynchronous vibration frequency of constant-speed blade based on full-phase difference principle |
Non-Patent Citations (1)
Title |
---|
张继旺: "基于叶尖定时的旋转叶片安全监测及智能诊断方法研究", 《中国博士学位论文全文数据库 工程科技Ⅱ辑》 * |
Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112082742A (en) * | 2020-07-22 | 2020-12-15 | 西安交通大学 | A method, system and device for intelligent identification of aero-engine wheel disc cracks |
CN112082742B (en) * | 2020-07-22 | 2021-08-13 | 西安交通大学 | A method, system and device for intelligent identification of aero-engine wheel disc cracks |
CN112364886A (en) * | 2020-10-14 | 2021-02-12 | 天津大学 | Blade crack online measurement method based on blade tip timing and random forest |
CN112464148A (en) * | 2020-10-14 | 2021-03-09 | 天津大学 | Blade crack measuring method based on blade tip timing and whole-process optimization SVM |
CN113504309A (en) * | 2021-05-18 | 2021-10-15 | 西安交通大学 | Blade detection method based on single blade end timing sensor |
CN113533529A (en) * | 2021-05-18 | 2021-10-22 | 西安交通大学 | A method of extracting the natural frequency difference between leaves by single or evenly distributed blade tip timing sensors |
CN113533529B (en) * | 2021-05-18 | 2022-10-28 | 西安交通大学 | Method for extracting natural frequency difference between blades by single or uniformly distributed blade end timing sensor |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN111413404A (en) | On-line measurement method of blade crack based on blade tip timing and support vector machine principle | |
CN111426459A (en) | Blade crack online measurement method based on blade tip timing and naive Bayes algorithm | |
CN105004462B (en) | Fan energy consumption monitoring system based on fault identification | |
CN110319922B (en) | Blade torsional vibration displacement measurement method based on tip timing principle | |
CN111622815A (en) | Blade crack online measurement method based on blade tip timing and naive Bayes optimization | |
CN112461934B (en) | Aero-engine blade crack source positioning method based on acoustic emission | |
CN109613428A (en) | It is a kind of can be as system and its application in motor device fault detection method | |
CN101625260A (en) | Method for detecting high speed rotating blade synchronous vibration parameters under speed change | |
Wang et al. | An OPR-free blade tip timing method for rotating blade condition monitoring | |
CN110131109A (en) | A wind turbine blade imbalance detection method based on convolutional neural network | |
CN110686764A (en) | Method for measuring asynchronous vibration frequency of constant-speed blade based on full-phase difference principle | |
CN102288362A (en) | System and method for testing unsteady surface pressure of vibrating blade | |
CN108871543A (en) | The anharmonic Fourier's analysis method of blade asynchronous vibration frequency under constant speed | |
Liang et al. | RETRACTED: Research on sensor error compensation of comprehensive logging unit based on machine learning | |
CN102175449A (en) | Blade fault diagnostic method based on strain energy response of wind-driven generator | |
CN110260919A (en) | Method that is a kind of while measuring turbo blade blade tip temperature and strain | |
CN111636932A (en) | On-line measurement of blade cracks based on blade tip timing and ensemble learning algorithm | |
CN103471708B (en) | Rotating machine fault diagnosis method based on nonlinear ICA (Independent Component Analysis) of improved particle swarm | |
CN114358074B (en) | Data-driven rotor system typical fault diagnosis method | |
Li et al. | Single-probe blade tip timing based on sparse Bayesian learning | |
Xu et al. | Optimal placement of blade tip timing sensors considering multi-mode vibration using evolutionary algorithms | |
CN106338372A (en) | Offshore platform damage positioning method based on residual strain energy and system thereof | |
Wang et al. | The method for identifying rotating blade asynchronous vibration and experimental verification | |
CN112364886A (en) | Blade crack online measurement method based on blade tip timing and random forest | |
CN112067289A (en) | Motor shaft and transmission shaft abnormal vibration early warning algorithm based on neural network |
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 | ||
RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200714 |
|
RJ01 | Rejection of invention patent application after publication |