CN111426459A - Blade crack online measurement method based on blade tip timing and naive Bayes algorithm - Google Patents
Blade crack online measurement method based on blade tip timing and naive Bayes algorithm Download PDFInfo
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- CN111426459A CN111426459A CN202010285996.0A CN202010285996A CN111426459A CN 111426459 A CN111426459 A CN 111426459A CN 202010285996 A CN202010285996 A CN 202010285996A CN 111426459 A CN111426459 A CN 111426459A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
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- 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
- G01M7/02—Vibration-testing by means of a shake table
- G01M7/025—Measuring arrangements
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
- G06F18/24155—Bayesian classification
Abstract
The invention relates to a blade crack online measurement method based on a blade tip timing and naive Bayes algorithm. Measuring vibration data of a crack-free blade and a crack-containing blade under a simulation working condition by combining a rotary mechanical working condition simulation test bed, marking the vibration data of the crack-free blade as a positive sample, and marking the vibration data of the crack-containing blade as a negative sample; utilizing an equal interval downsampling method to realize the balance of positive and negative samples and establishing a naive Bayes classification model; and classifying the blade vibration data under the operation condition in real time through a naive Bayes classification model, and realizing the online measurement of the full-stage blade cracks of the rotary machine.
Description
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 a blade tip timing and naive Bayes algorithm.
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 have faults, so that the vibration parameters of the blades are accurately measured, whether the blades have the cracks or not is measured on line, the faults of the blades can be pre-warned in real time, and the method has very important practical significance for research and development tests, state monitoring and fault diagnosis of great rotating machinery such as aero-engines and steam turbines.
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 tip timing technology has the advantages of non-contact, real-time on-line and capability of measuring all blades and the likeThe method has good engineering practicability. The naive Bayes algorithm is a simple and strong linear classifier constructed based on Bayes decision theory[4]. Under the condition that all relevant probabilities are known, the naive Bayes algorithm assumes that all features are independent of each other, and considers how to select the optimal classification based on the probability and the misjudgment loss. The naive Bayes algorithm requires that the characteristics are mutually independent, and although the requirement is difficult to be satisfied in reality, the naive Bayes algorithm has good classification effect in practical application[5,6]. Therefore, after vibration data of normal blades and cracked blades are obtained through a blade tip timing technology, a naive Bayes classification model is established by combining a sample equalization naive Bayes algorithm, and further online measurement of full-stage blade cracks under the actual operation condition of the rotary machine is achieved.
At present, the crack measurement of the full-stage blade of the rotary machine depends on an off-line detection technology, and the actual requirement of the large-scale rotary machine for measuring the crack of the full-stage 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-tip timing method in the blade vibration measurement of foreign aeroengines is summarized in [ J ]. aeronautical science technology,
2013(06):9-13.
[4] zhongshihua, machine learning [ M ]. beijing: qinghua university Press, 2016.
[5]Domingos,Pedro,and Michael Pazzani.On the optimality of the simpleBayesian classifier under zero-one loss[J].Machine learning，1997，29(2-3):103-130.
[6]Andrew Y.Ng,&Michael I.Jordan.On discriminative vs.generativeclassifiers:a comparison of logistic regression and naive Bayes[J].NeuralProcessing Letters，2002，2(3):169.
Disclosure of Invention
The invention aims to provide a blade crack online measurement method based on a blade tip timing and naive Bayes algorithm, which aims to overcome the defects of the prior art, obtains the vibration data of a crack-free blade and a crack-containing blade by the blade tip timing technique, performs classification training of the crack-containing blade and the crack-free blade by combining a sample equalization and naive Bayes algorithm, establishes a naive Bayes classification model, and realizes online measurement of the rotary machine full-stage blade crack in a working state. The technical scheme of the invention is as follows:
a blade crack online measurement method based on a blade tip timing and naive Bayes algorithm is characterized in that a plurality of blade tip timing sensors are arranged at different positions of a casing of a rotary machine, the arrival time of a blade reaching each blade tip timing sensor is measured through the blade tip timing sensors, and blade vibration data including vibration displacement, amplitude and vibration frequency of the blade are obtained; measuring vibration data of a crack-free blade and a crack-containing blade under a simulation working condition by combining a rotary mechanical working condition simulation test bed, marking the vibration data of the crack-free blade as a positive sample, and marking the vibration data of the crack-containing blade as a negative sample; utilizing an equal interval downsampling method to realize the balance of positive and negative samples and establishing a naive Bayes classification model; and classifying the blade vibration data under the operation condition in real time through a naive Bayes classification model, and realizing the online measurement of the full-stage blade cracks of the rotary machine.
Preferably, the method 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) the method comprises the steps that a rotating machinery working condition simulation test bed is utilized, a simulation working condition test is carried out when a blade is intact and has no cracks, the arrival time signal of each blade measured by a blade tip timing sensor is sent to a blade state monitoring system, and blade vibration data including vibration displacement, amplitude, vibration frequency, initial phase, vibration normal deviation and resonance frequency multiplication of the crack-free blade at different rotating speeds are calculated by combining the installation positions of a plurality of blade tip timing sensors and different rotating speeds of a rotating machine;
(3) artificially manufacturing the required cracks on the blade, and performing a simulation working condition experiment again to obtain blade vibration data of the cracked blade, wherein the blade vibration data comprises vibration displacement, amplitude, vibration frequency, an initial phase, vibration constant offset and resonance frequency multiplication;
(4) marking the vibration data of the crack-free blade as a positive sample, and marking a sample label as 0; marking the vibration data of the blade with the crack as a negative sample, and marking a sample label as 1;
(5) because the positive samples are far more than the negative samples, the total number of the negative samples is taken as reference, the positive samples and the negative samples are sampled by utilizing an equal interval downsampling method to realize the balance of the positive samples and the negative samples, the sample proportion is optimized, the positive samples and the negative samples after sample balance are combined into a training set, each row of the training set is a training sample which is a measured value of the blade vibration data at different moments, and each column in the training set is a sample characteristic which is the blade vibration data;
(6) establishing a naive Bayes classification model by utilizing a training set and a naive Bayes algorithm;
(7) when the rotary machine runs in a working state, according to blade arrival time and a blade tip timing algorithm which are obtained by actually measuring a blade tip timing sensor, the blade state monitoring system calculates and obtains blade vibration data including vibration displacement, amplitude, vibration frequency, an initial phase, vibration normal deviation and resonance frequency multiplication of the blade at different rotating speeds in the working state;
(8) the method comprises the steps of taking real-time blade vibration data measured in a working state as a sample to be measured, sending the sample to be measured into a trained naive Bayes classification model, if a characteristic value of the sample to be measured appears in a training set, directly calculating a positive probability value and a negative probability value of the sample to be measured, if a certain characteristic value of a certain sample to be measured does not appear in the training set, calculating a conditional probability of the characteristic, then calculating the positive probability value and the negative probability value of the sample to be measured, and calculating a label of the sample to be measured through the naive Bayes classification model to be 0 or 1, so that online measurement of the full-level blade cracks of the rotary machine is realized.
The method overcomes the defects of the existing rotating machinery blade crack measuring technology, provides the blade crack online measuring method based on the blade tip timing and naive Bayes algorithm, measures the vibration data of the blade without cracks and with cracks under the simulation working condition through the blade tip timing technology, and combines the naive Bayes classification model to realize the online measurement of the rotating machinery full-grade blade cracks under the working condition.
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 blade crack online measurement structure diagram based on a blade tip timing and naive Bayes 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;
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 full-stage blade cracks of the rotary machine cannot be measured on line, and provides an on-line blade crack measuring method based on a blade tip timing and naive Bayes algorithm;
the blade tip timing and naive Bayes algorithm-based blade crack online measurement structure is shown in FIG. 1, wherein four blade tip timing sensors are arranged at any different positions of a rotary mechanical casing 5, and each blade tip timing sensor comprises 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 method
Amplitude A and vibration constant offset C;
after the blade 6 is artificially manufactured into the required cracks, the cracks are arranged in a rotating machine, the rotating machine working condition simulation test bed is utilized again to repeat the simulation operation process, and the blade with the cracks under the same rotating speed can be obtainedVibration displacement y 'of sheet 6 passing through tip timing sensor a 1'0Vibration displacement y 'when passing through blade tip timing sensor B2'1Vibration displacement y 'when passing through blade 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 obtained
Amplitude 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, the sample label is marked as 0, and blade vibration data y 'of the cracked blade 6 measured at different rotating speeds'0、y′1、y'2、y'3、A'、
C 'and N' are set as negative samples, and the sample label is marked as 1;
because the positive samples are far more than the negative samples, the blade vibration data obtained by using the blade tip timing technology is continuous and gradual and has no data loss, the total number of the negative samples is taken as 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 sample proportion of a training set is optimized, the positive samples and the negative samples after sample balance are combined into the training set, each row of the training set is a training sample which is a blade vibration data measured value at different moments, each column in the training set is a sample characteristic which is blade vibration data, and the blade vibration parameters comprise vibration displacement, vibration frequency, initial phase, amplitude, vibration constant offset and resonance frequency;
establishing a naive Bayes classification model, calculating class prior probability of a training set, and combining Laplace correction, wherein the calculation formula of the class prior probability of the training set is as follows:
in the formula, DcThe method is characterized in that the method is a set formed by class c samples in a training set, l is the number of samples in the training set, blade vibration data obtained by using a blade tip timing technology is continuous data, and the conditional probability of the characteristics of the training set is as follows:
in the formula, σc,jFor the variance of class c samples over the jth feature, NjNumber of possible values for jth feature, xjIs the value of the sample x on the jth feature, μc,jThe Bayesian criterion is the mean value of the class c sample on the jth feature:
equation (4) is a naive Bayes classification model established by using a training set and a naive Bayes algorithm;
when the rotary machine runs in a working state, real-time blade vibration data y' of the blades 6 in different rotating speed working states can be obtained through the blade tip timing technology0，y″1，y″2，y″3，A″，
C 'and N', and used as samples to be detected;
(VII) sending the sample to be tested into a trained naive Bayes classification model, if the characteristic value of the sample to be tested appears in the training set, directly calculating the positive class probability value and the negative class probability value of the sample to be tested by using a formula (4), if a certain characteristic value of a certain sample to be tested does not appear in the training set, firstly calculating the conditional probability of the characteristic by using a formula (3), and then substituting the formula (4) to calculate the positive class probability value and the negative class probability value of the sample to be tested;
and (eight) if the positive probability value of the sample to be measured is calculated by the naive Bayes classification model to be greater than the negative probability value, the label of the sample to be measured is given as '0' by the naive Bayes classification model, which indicates that no crack is generated on the blade 6, and if the negative probability value of the sample to be measured is calculated by the naive Bayes classification model to be greater than the positive probability value, the label of the sample to be measured is given as '1' by the naive Bayes classification model, which indicates that the crack is generated on the blade 6, at the moment, the numerical display and the acousto-optic alarm prompt are given by the blade state monitoring system 7 in the figure 1, so that the online measurement of the full-scale blade crack of the.
Claims (2)
1. A blade tip timing and naive Bayes algorithm-based blade crack online measurement method is characterized in that a plurality of blade tip timing sensors are arranged at different positions of a casing of a rotary machine, the arrival time of a blade reaching each blade tip timing sensor is measured through the blade tip timing sensors, and blade vibration data including vibration displacement, amplitude and vibration frequency of the blade are obtained. Measuring vibration data of a crack-free blade and a crack-containing blade under a simulation working condition by combining a rotary mechanical working condition simulation test bed, marking the vibration data of the crack-free blade as a positive sample, and marking the vibration data of the crack-containing blade as a negative sample; utilizing an equal interval downsampling method to realize the balance of positive and negative samples and establishing a naive Bayes classification model; and classifying the blade vibration data under the operation condition in real time through a naive Bayes classification model, and realizing the online measurement of the full-stage blade cracks of the rotary machine.
2. The on-line measuring method according to claim 1, characterized by being performed according to 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) the method comprises the steps that a rotating machinery working condition simulation test bed is utilized, a simulation working condition test is carried out when a blade is intact and has no cracks, the arrival time signal of each blade measured by a blade tip timing sensor is sent to a blade state monitoring system, and blade vibration data including vibration displacement, amplitude, vibration frequency, initial phase, vibration normal deviation and resonance frequency multiplication of the crack-free blade at different rotating speeds are calculated by combining the installation positions of a plurality of blade tip timing sensors and different rotating speeds of a rotating machine;
(3) artificially manufacturing the required cracks on the blade, and performing a simulation working condition experiment again to obtain blade vibration data of the cracked blade, wherein the blade vibration data comprises vibration displacement, amplitude, vibration frequency, an initial phase, vibration constant offset and resonance frequency multiplication;
(4) marking the vibration data of the crack-free blade as a positive sample, and marking a sample label as 0; marking the vibration data of the blade with the crack as a negative sample, and marking a sample label as 1;
(5) because the positive samples are far more than the negative samples, the total number of the negative samples is taken as reference, the positive samples and the negative samples are sampled by utilizing an equal interval downsampling method to realize the balance of the positive samples and the negative samples, the sample proportion is optimized, the positive samples and the negative samples after sample balance are combined into a training set, each row of the training set is a training sample which is a measured value of the blade vibration data at different moments, and each column in the training set is a sample characteristic which is the blade vibration data;
(6) establishing a naive Bayes classification model by utilizing a training set and a naive Bayes algorithm;
(7) when the rotary machine runs in a working state, according to blade arrival time and a blade tip timing algorithm which are obtained by actually measuring a blade tip timing sensor, the blade state monitoring system calculates and obtains blade vibration data including vibration displacement, amplitude, vibration frequency, an initial phase, vibration normal deviation and resonance frequency multiplication of the blade at different rotating speeds in the working state;
(8) the method comprises the steps of taking real-time blade vibration data measured in a working state as a sample to be measured, sending the sample to be measured into a trained naive Bayes classification model, if a characteristic value of the sample to be measured appears in a training set, directly calculating a positive probability value and a negative probability value of the sample to be measured, if a certain characteristic value of a certain sample to be measured does not appear in the training set, calculating a conditional probability of the characteristic, then calculating the positive probability value and the negative probability value of the sample to be measured, and calculating a label of the sample to be measured through the naive Bayes classification model to be 0 or 1, so that online measurement of the full-level blade cracks of the rotary machine is realized.
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| 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 |
| CN113065228A (en) * | 2021-03-10 | 2021-07-02 | 中国大唐集团科学技术研究院有限公司西北电力试验研究院 | Steam turbine valve closing time evaluation method based on naive Bayes |
| CN114033732A (en) * | 2021-11-10 | 2022-02-11 | 中国航发沈阳发动机研究所 | Rotor blade during operation twists reverse angle measurement system |
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Application publication date: 20200717 |