阅读说明：本技术 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 (Blade crack online measurement method based on blade tip timing and support vector machine principle ) 是由 段发阶 邓震宇 傅骁 牛广越 程仲海 刘志博 于 2020-03-27 设计创作，主要内容包括：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 a blade crack online measurement method based on blade tip timing and a support vector machine principle, wherein a plurality of blade tip timing sensors are arranged at different positions of a casing of a rotary machine, under a simulation working condition, the arrival time of a blade reaching each blade tip timing sensor is measured through the blade tip timing sensors, the vibration displacement, the amplitude and the vibration frequency of the blade are measured, the vibration data of the blade comprise the vibration data of a crack-free blade and a crack-containing blade, the vibration data of the crack-free blade is marked as a positive sample, the vibration data of the crack-containing blade is marked as a negative sample, the samples are sent to a support vector machine for classification training and establishment of a classification model, and the trained classification model is used for realizing the online measurement of the crack of the rotary machine blade under the operation working condition.)

1. A blade tip timing and support vector machine principle-based blade crack online measurement method includes the steps that a plurality of blade tip timing sensors are arranged at different positions of a casing of a rotary machine, under a simulation working condition, the arrival time of a blade reaching each blade tip timing sensor is measured through the blade tip timing sensors, vibration displacement, amplitude and vibration frequency blade vibration data of the blade are measured, the vibration data include vibration data of a crack-free blade and a crack-containing blade, the vibration data of the crack-free blade are marked as positive samples, the vibration data of the crack-containing blade are marked as negative samples, the samples are sent to a support vector machine to be classified and trained, a classification model is built, and online measurement of cracks of the rotary machine blade under an operation working condition is achieved through the trained classification model. Comprises the following steps:

(1) arranging a plurality of blade tip timing sensors at different positions of a rotating mechanical casing, wherein the blade tip timing sensors are used for measuring the time of blades reaching each blade tip timing sensor at different rotating speeds;

(2) carrying out a simulation working condition test when the blade is intact and has no crack, sending an arrival time signal of each blade measured by the blade tip timing sensor into a blade state monitoring system, and calculating to obtain vibration displacement, amplitude, vibration frequency, initial phase, vibration constant offset and resonance frequency multiplication blade vibration data of the crack-free blade at different rotating speeds by combining the installation positions of the blade tip timing sensors, different rotating speeds of a rotating machine and a blade tip timing algorithm;

(3) for the cracked blade, carrying out a simulation working condition experiment to obtain vibration displacement, amplitude, vibration frequency, initial phase, vibration constant offset and resonance frequency multiplication frequency blade vibration data of the cracked blade at different rotating speeds;

(4) setting the vibration data of the crack-free blade as a positive sample and setting the data label as 0; setting the vibration data of the blade with the crack as a negative sample, and setting a data label as 1;

(5) taking the total number of the negative samples as reference, sampling the positive samples by using an equal interval downsampling method to realize the balance of the positive samples and the negative samples, and combining the balanced positive samples and the balanced negative samples to form a training set;

(6) normalizing the data in the training set by using a minimum and maximum normalization algorithm to eliminate dimension;

(7) according to the training set with the dimensions eliminated, carrying out classification training by using a support vector machine algorithm, and establishing a support vector machine classification model;

(8) when the rotary machine runs in a working state, according to blade arrival time obtained by actually measuring by a blade tip timing sensor, calculating by using a blade state monitoring system to obtain vibration displacement, amplitude, vibration frequency, initial phase, vibration normal deviation and resonance frequency multiplication frequency blade vibration data of the blade at different rotating speeds in the working state;

(9) the blade vibration data measured in the working state is used as a sample to be measured, the sample is sent to a trained support vector machine classification model in a blade state monitoring system after normalization processing, the label of the sample to be measured obtained through calculation of the support vector machine classification model is 0 or 1, and online measurement of the rotary machine blade cracks is achieved.

Technical Field

The invention belongs to the field of rotating machinery state monitoring, and particularly relates to an online blade crack measuring method based on blade tip timing and a support vector machine principle.

Technical Field

The large-scale rotating machinery comprises large-scale equipment such as an aircraft engine, a steam turbine and the like, and is a core component of key equipment such as various military and commercial aircrafts in the aerospace field and generator sets, steam sets and the like in the industrial field. Especially, the working state of the blade which is used as a core element for the rotary machine to do work directly influences the working efficiency and safe and stable operation of the key equipment. The working environment of the rotary mechanical blade is very severe and is under severe conditions of high stress, high and low temperature or high scouring and the like for a long time. These external conditions can cause complex cyclic or non-cyclic stress variations in the blades of large rotating machines, which can lead to cracking of the blades when the stress exceeds the yield strength limit of the blade material, and thus blade fracture can occur. The blade cracks are one of the main reasons for causing the large-scale rotating machinery to be in fault, so that the vibration parameters of the blades are accurately measured, whether the blades generate cracks or not is measured on line, the blade faults can be pre-warned in real time, and the method has very important practical significance in the aspects of research and development tests, state monitoring, fault diagnosis and the like of great rotating machinery such as aero-engines, steam turbines and the like.

Rotating blade vibration measurement technology based on blade tip timing principle^[1-3]The method is a typical non-contact measuring method, and the basic principle is that a certain number of sensors are arranged on a casing of a rotating machine, the arrival time of each blade when rotating past the sensors is measured, and the online measurement of vibration parameters of the blades such as vibration displacement, amplitude, vibration frequency and the like is realized by utilizing a relevant mathematical algorithm. Compared with the traditional off-line blade state detection method and the on-line detection methods such as a strain gauge method, a frequency modulation method, a sound method and the like, the blade tip timing technology has the advantages of non-contact, real-time on-line and capability of measuring all blades and the like, and has good engineering practicability. The support vector machine is a machine learning method based on statistical theory, and can be used for the classification problem of linear and nonlinear data. The support vector machine was originally designed to solve the two-classification problem, and its main idea was to seekAnd finding an optimal classification hyperplane to maximize the distance between the positive and negative samples in the training set and the optimal classification hyperplane, and classifying the tested samples by using the optimal classification hyperplane. Therefore, after vibration data of normal blades and cracked blades are obtained by a blade tip timing method, training is carried out by utilizing a support vector machine algorithm and a classification model is established, and further the online measurement of the blade cracks under the actual operation condition of the rotary machine is realized.

At present, the crack measurement of the blade of the rotary machine depends on an off-line detection technology, and the actual requirement of the large rotary machine for measuring the crack of the blade on line cannot be met.

[1] Blade tip timing-based rotary blade vibration detection and parameter identification technology [ D ]. Tianjin university, 2011.

[2] Zhao xing Ming, Teng Guang Rong, etc. new technique for measuring blade tip timing rotating blade vibration [ J ] measuring and controlling technique, 2006(03):17-19.

[3] The application of the Wangping and the apex timing method in the foreign aeroengine blade vibration measurement is summarized in [ J ] aeronautical science and technology, 2013(06):9-13.

Disclosure of Invention

The invention aims to provide an on-line blade crack measuring method based on the blade tip timing and support vector machine principles, which aims to overcome the defects of the prior art.

The technical scheme of the invention is as follows:

a blade tip timing and support vector machine principle-based blade crack online measurement method includes the steps that a plurality of blade tip timing sensors are arranged at different positions of a casing of a rotary machine, under a simulation working condition, the arrival time of a blade reaching each blade tip timing sensor is measured through the blade tip timing sensors, vibration displacement, amplitude and vibration frequency blade vibration data of the blade are measured, the vibration data include vibration data of a crack-free blade and a crack-containing blade, the vibration data of the crack-free blade are marked as positive samples, the vibration data of the crack-containing blade are marked as negative samples, the samples are sent to a support vector machine to be classified and trained, a classification model is built, and online measurement of cracks of the rotary machine blade under an operation working condition is achieved through the trained classification model. Comprises the following steps:

(1) arranging a plurality of blade tip timing sensors at different positions of a rotating mechanical casing, wherein the blade tip timing sensors are used for measuring the time of blades reaching each blade tip timing sensor at different rotating speeds;

(2) carrying out a simulation working condition test when the blade is intact and has no crack, sending an arrival time signal of each blade measured by the blade tip timing sensor into a blade state monitoring system, and calculating to obtain vibration displacement, amplitude, vibration frequency, initial phase, vibration constant offset and resonance frequency multiplication blade vibration data of the crack-free blade at different rotating speeds by combining the installation positions of the blade tip timing sensors, different rotating speeds of a rotating machine and a blade tip timing algorithm;

(3) for the cracked blade, carrying out a simulation working condition experiment to obtain vibration displacement, amplitude, vibration frequency, initial phase, vibration constant offset and resonance frequency multiplication frequency blade vibration data of the cracked blade at different rotating speeds;

(4) setting the vibration data of the crack-free blade as a positive sample and setting the data label as 0; setting the vibration data of the blade with the crack as a negative sample, and setting a data label as 1;

(5) taking the total number of the negative samples as reference, sampling the positive samples by using an equal interval downsampling method to realize the balance of the positive samples and the negative samples, and combining the balanced positive samples and the balanced negative samples to form a training set;

(6) normalizing the data in the training set by using a minimum and maximum normalization algorithm to eliminate dimension;

(7) according to the training set with the dimensions eliminated, carrying out classification training by using a support vector machine algorithm, and establishing a support vector machine classification model;

(8) when the rotary machine runs in a working state, according to blade arrival time obtained by actually measuring by a blade tip timing sensor, calculating by using a blade state monitoring system to obtain vibration displacement, amplitude, vibration frequency, initial phase, vibration normal deviation and resonance frequency multiplication frequency blade vibration data of the blade at different rotating speeds in the working state;

(9) the blade vibration data measured in the working state is taken as a sample to be measured, after normalization processing, the sample is sent to a trained support vector machine classification model in a blade state monitoring system, the label of the sample to be measured obtained through calculation of the support vector machine classification model is 0 or 1,

and the online measurement of the cracks of the rotating mechanical blade is realized.

The invention has the following beneficial effects and advantages:

the method provided by the invention overcomes the defects of the existing rotating machinery blade crack measurement technology, and provides an online blade crack measurement method based on the principles of blade tip timing and a support vector machine.

Drawings

The following drawings depict selected embodiments of the present invention, all by way of example and not by way of exhaustive or limiting example, and are presented in the figures of the accompanying drawings:

FIG. 1 shows a structure diagram of blade crack online measurement based on the principle of blade tip timing and support vector machine

FIG. 2 is a schematic diagram of a support vector machine classification algorithm

The reference numbers in the figures illustrate:

in fig. 1: 1 is a blade tip timing sensor A; 2 is a blade tip timing sensor B; a tip timing sensor C is used as a 3; 4 is a blade tip timing sensor D; 5 is a rotating machinery case; 6 is a blade; 7 is a blade state monitoring system;

in fig. 2: 8 is an optimal classification hyperplane; 9 is a positive support vector; and 10 is a negative class support vector.

Detailed Description

The steps of the present invention that are described in detail below are intended to be illustrative of embodiments of the invention, and are not intended to be the only form in which the present invention may be manufactured or utilized, and other embodiments that perform the same function are intended to be within the scope of the present invention.

The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

The invention aims to overcome the defect that the existing rotating machinery blade cracks can not be measured on line, and provides a blade crack on-line measuring method based on the blade tip timing and support vector machine principle;

the blade crack online measurement structure based on the blade tip timing and support vector machine principle is shown in fig. 1, four blade tip timing sensors are arranged at any different positions of a rotating mechanical casing 5, and comprise a blade tip timing sensor A1, a blade tip timing sensor B2, a blade tip timing sensor C3 and a blade tip timing sensor D4, and the four blade tip timing sensors can measure the time of each blade reaching the blade tip timing sensor in the same circle;

(II) under the condition that the blade has no crack, putting the rotary machine into a rotary machine working condition simulation test bed, simulating the running condition of the rotary machine, assuming that the rotary machine rotates clockwise, when the blade 6 is excited to synchronously vibrate, the arrival time signals of the blade 6 measured by the four-branch blade tip timing sensors are sent into a blade state monitoring system 7, and according to the installation positions of the four-branch blade tip timing sensors, different rotating speeds of the rotary machine and a blade excited vibration response equation, the vibration displacement equation of the blade 6 passing through the four-branch blade tip timing sensors in the same circle is as follows:

in the formula, y₀Is the vibration displacement, y, of the blade 6 passing the tip timing sensor A1₁Is the vibration displacement, y, of the blade 6 passing the tip timing sensor B2₂Is the vibration displacement, y, of the blade 6 passing the tip timing sensor C3₃Which is the vibrational displacement of the blade 6 as it passes the tip timing sensor D4, a is the amplitude of the blade 6,is the initial phase of the blade 6, C is the constant offset of the vibration of the blade 6, N is the resonance frequency multiple of the blade 6, α₁α arc angle of installation of tip timing sensor B2 relative to tip timing sensor A1₂α arc angle of installation of tip timing sensor C3 relative to tip timing sensor A1₃For the installation camber angle of the tip timing sensor D4 relative to the tip timing sensor A1, all possible values of N are taken into formula (1) by using a traversal algorithm, and the vibration frequency omega and the initial phase of the blade 6 without cracks can be solved by combining a least square methodAmplitude A and vibration constant offset C;

after manually manufacturing the required cracks on the blade 6, loading the blade into a rotary machine, and repeating the simulation operation process by using the working condition simulation test bed of the rotary machine again to obtain the vibration displacement y 'of the blade 6 with the cracks when the blade 6 passes through the blade tip timing sensor A1 at the same rotating speed'₀Vibration displacement y when passing through the tip timing sensor B2₁', vibration displacement y ' when passing through the tip timing sensor C3 '₂And a vibration displacement y 'when passing through the tip timing sensor D4'₃And the vibration frequency omega' and the initial phase of the blade 6 with cracks are obtainedAmplitude A ', vibration constant offset C ' and resonance frequency multiplication N ';

thirdly, measuring blade vibration data y of the crack-free blade 6 at different rotating speeds₀、y₁、y₂、y₃、A、C and N are set as positive samples, and the data tag is set as 0; blade vibration data y 'of the cracked blade 6 measured at different rotating speeds'₀、y₁'、y'₂、y'₃、A'、C 'and N' areFor the negative type sample, the data tag is set to 1;

(IV) a support vector machine is a machine learning method based on a statistical learning theory, and is suitable for linear and nonlinear data classification, as shown in FIG. 2, the main idea is to find an optimal classification hyperplane 8, so that the distance between a positive sample and a negative sample in a training set to the optimal classification hyperplane 8 is maximum, and classify a sample to be tested by using the optimal classification hyperplane 8, a point closest to the optimal classification hyperplane 8 in the positive sample of a test set is called a positive support vector 9, a point closest to the optimal classification hyperplane 8 in the negative sample of the test set is called a negative support vector 10, the determination of the optimal classification hyperplane 8 only depends on the positive sample support vector 9 and the negative sample support vector 10, and the non-support vector points do not work;

because the positive samples are usually more than the negative samples, the total number of the negative samples is taken as a reference, a large number of positive samples are sampled by using an equal interval downsampling method to realize the balance of the positive samples and the negative samples, the positive samples and the negative samples after sample balance are combined into a training set, each row in the training set represents one sample, and each column represents an actual measurement value of the blade vibration data;

because the data units in the training set are different and the numerical magnitude is also different, the maximum and minimum normalization method is adopted to perform dimension elimination processing on the training set, the quality of the training set is improved, and the minimum and maximum normalization equation is as follows:

in the formula (I), the compound is shown in the specification,for the training set j column normalized data, x_ijFor the data before the jth column normalization of the training set,is the mean value, x, of the training set before the jth column data normalization_jmaxIs the maximum value, x, of the training set before j column number normalization_jminThe method comprises the steps that a training set is the minimum value before the j-th line of data normalization of the training set, the training set after dimension elimination is sent to a support vector machine for classification training, a Gaussian radial basis kernel function is selected as a kernel function of the support vector machine, parameter optimization is carried out on the kernel width of the Gaussian radial basis kernel function and a soft interval penalty coefficient of the support vector machine by using a grid search and cross validation method, optimal parameters are solved, and a support vector machine classification model is established;

when the rotary machine runs in the working state, the blade vibration data y of the blades 6 in the working states of different rotating speeds can be obtained by the blade tip timing method "₀，y”₁，y”₂，y”₃，A”，C and N as samples to be detected, and carrying out same-proportion normalization on each sample to be detected by using the following formula:

in the formula (I), the compound is shown in the specification,for the j column data, X, after the sample to be measured is normalized in the same proportion_ijNormalizing the data of the jth column before the sample to be detected in the same proportion;

and (VI) sending the sample to be measured after the normalization in the same proportion into a trained support vector machine classification model, if the data label of the sample to be measured is calculated to be 0 by the support vector machine classification model, indicating that the blade 6 does not crack, and if the data label of the sample to be measured is calculated to be 1 by the support vector machine classification model, indicating that the blade 6 cracks, at the moment, giving an audible and visual alarm prompt through a blade state monitoring system 7, thereby realizing the online measurement of the blade cracks of the rotary machine.