CN102052964B - Real-time recognition method for vibration opposite-phase vector stability of turbogenerator unit rotor - Google Patents

Abstract

The invention discloses a real-time recognition method for the vibration opposite-phase vector stability of a turbogenerator unit rotor, belonging to the fields of mechanical vibration state monitoring and fault diagnosis. A vibration signal, a rotating speed signal and a bonded phase signal of a turbogenerator unit rotor shaft are collected to calculate and analyze vibration data in real time. The method comprises the following steps of: calculating and storing the real parts and the imaginary parts of opposite vibration opposite-phase vectors of two side shafts of the rotor in real time; calculating in real time by combining with statistic values, such as the mean value of the real parts and the imaginary parts of the opposite-phase vectors, a standard deviation, and the like; ordering the mean value sequence of the real parts and the imaginary parts of the opposite-phase vectors and a standard deviation sequence and calculating inversion numbers thereof; and calculating the steady-state parameters of the real parts and the imaginary parts of the opposite-phase vectors. On a basis of real-time quantitative calculation and analysis, various vibration results are combined to carry out the automatic real-time online judgment on whether the vibration opposite-phase vectors of a unit shafting rotor have stability. The invention has the advantages of scientific method, conclusion reliability and the like and can realize automatic real-time online monitoring, analysis, and the like.

Description

A real-time identification method for the steady-state property of anti-phase vector of rotor vibration of turbogenerator

technical field

The invention belongs to the field of vibration state monitoring and fault diagnosis of rotating machinery, and in particular relates to a real-time identification method for the steady-state property of the rotor vibration antiphase vector of a large turbogenerator set for real-time on-line automatic monitoring of the vibration state of a large turbogenerator set.

Background technique

The vibration state of a steam turbine generator set is an important index for the safe operation of the set. Vibration stability during unit operation is one of the most important issues related to the safe and reliable operation of the unit. If the monitoring and analysis of the vibration state is not timely, it may lead to partial or overall serious failure of the unit. Due to the deterioration of the vibration of the unit, load reduction operation, or shutdown treatment, or emergency forced shutdown often occurs.

The turbo-generator set operates at a working speed for a long time, which requires high vibration stability. If the second-order critical speed vibration of the turbogenerator rotor is too large, it will have a great impact on the vibration state at the working speed, and there will be a large vibration anti-phase vector at the working speed.

The existing identification of the stability of the shafting and rotor vibration of the turbogenerator needs to be completed by experts with certain experience in field vibration fault diagnosis, which is poor in objectivity, highly dependent on the subjectivity of experts, and cannot be done. Real-time automatic on-line monitoring, analysis and discrimination of vibration anti-phase vector stability. Therefore, it is very important to propose a real-time identification method for the steady state of the rotor vibration antiphase vector of the turbogenerator.

Contents of the invention

In order to solve the above-mentioned technical problems, the present invention provides a method for real-time identification of the stability of the rotor vibration anti-phase vector of the steam turbine generator set.

The method is based on the shaft relative vibration amplitude and phase data of the rotor in the operation of the steam turbine, combined with computer program calculation and automatically realized.

The technical solution adopted in the present invention is: a method for real-time identification of the steady-state property of the rotor vibration inverse phase vector of a steam turbine generator set, which is characterized in that it includes:

(1) Data collection, real-time collection of shaft relative vibration data measured near the supporting bearings on both sides of the unit rotor, rotor speed signal and key phase signal;

其中

分别为转子A、B两侧轴振工频振动矢量。存储转子两侧轴相对振动反相矢量

的实部R^real、虚部R^imagi，其中FFT为快速傅立叶变换；(2) Real-time calculation and storage of inverse vectors. For the shaft relative vibration data on both sides of the rotor of the unit, use the FFT spectrum analysis method to calculate the power frequency vibration amplitudes a ^ra and a ^rb of the shaft relative vibration on both sides of the rotor A and B synchronously in real time (the amplitude unit is μm) and phase p ^ra , p ^rb data (the phase unit is °). According to the shaft relative vibration power frequency vibration amplitudes a ^ra , a ^rb and phase p ^ra , p ^rb data on both sides of the rotor A and B, calculate the anti-phase vector of the shaft vibration power frequency vibration on both sides of the rotor A and B

in

are the shaft vibration power frequency vibration vectors on both sides of rotor A and B respectively. Store the opposite phase vector of the relative vibration on both sides of the rotor

The real part R ^real , the imaginary part R ^imagi , wherein FFT is Fast Fourier Transform;

的幅值S^amp数据，从T₁时刻向前截取至T₀时刻的转子A、B两侧轴振工频振动的反相矢量

的实部R^real、虚部R^imagi数据，按照数据存储时间先后顺序，分别地将反相矢量

的实部R^real、虚部R^imagi数据，分为m组，每组n个数据，计算反相矢量

实部R^real的组内均值

组内标准偏差

及虚部R^imagi的组内均值组内标准偏差

将上述数据分别地按照组号排成序列。(3) Real-time calculation of anti-phase vector statistic value, according to the stored current time T ₁ before the shaft vibration power frequency vibration anti-phase vector on both sides of rotor A and B

The amplitude S ^amp data of , intercepted forward from T ₁ time to T ₀ time, the anti-phase vector of shaft vibration power frequency vibration on both sides of rotor A and B

The real part R ^real and the imaginary part R ^imagi data, according to the order of data storage time, respectively reverse the vector

The real part R ^real and the imaginary part R ^imagi data are divided into m groups, each group has n data, and the inverse vector is calculated

The group mean of the real part R ^real

Within standard deviation

and the group mean of the imaginary part R ^imagi Within standard deviation

Arrange the above data in sequence according to the group numbers respectively.

实部R^real的组内均值

序列的逆序数

组内标准偏差

序列的逆序数

及虚部R^imagi的组内均值

序列的逆序数

组内标准偏差

序列的逆序数

其中，逆序是指在一个数据序列中，一对数的前后位置与大小顺序相反，即前面的数大于后面的数；逆序数是指一个数据序列中逆序的总数。Calculate the inverse vector separately

The group mean of the real part R ^real

reverse sequence number

Within standard deviation

reverse sequence number

and the group mean of the imaginary part R ^imagi

reverse sequence number

Within standard deviation

reverse sequence number

Among them, the reverse order means that in a data sequence, the front and rear positions of a pair of numbers are opposite to the order of size, that is, the number in the front is greater than the number in the back; the reverse order number refers to the total number of reverse orders in a data sequence.

实部R^real的组内均值

序列的逆序数

组内标准偏差

序列的逆序数

及虚部R^imagi的组内均值

序列的逆序数

组内标准偏差

序列的逆序数

计算反相矢量的实部R^real的稳态参数

以及虚部R^imagi的稳态参数

(4) Real-time calculation of the steady-state parameters of the reversed phase vector, according to the reversed phase vector

The group mean of the real part R ^real

reverse sequence number

Within standard deviation

reverse sequence number

and the group mean of the imaginary part R ^imagi

reverse sequence number

Within standard deviation

reverse sequence number

Calculate the inverted vector The steady-state parameter of the real part R ^real

and the steady-state parameters of the imaginary part R ^imagi

的实部R^real数据满足条件及

并且反相矢量

的虚部R^imagi数据满足条件

及

那么可以判定反相矢量

具备稳态性；否则，反相矢量不具备稳态性。N_1-α/2(0，1)是概率为(1-α/2)的标准正态分布变量值，设定α/2＝2.5％，可知N_0.975(0，1)＝1.9604。。(5) Judgment of the stability of the shafting rotor vibration anti-phase vector, according to the above calculation, if the anti-phase vector

The real part of R ^real data satisfies the condition and

and inverts the vector

The imaginary part of the R ^imagi data satisfies the condition

and

Then it can be determined that the inverse vector

is steady-state; otherwise, the inverting vector Not stable. N _1-α/2 (0, 1) is a standard normal distribution variable value with probability (1-α/2), setting α/2=2.5%, it can be known that N _0.975 (0,1)=1.9604. .

The real-time identification method of the rotor vibration anti-phase vector stability of the steam turbine generator set uses the relative shaft vibration amplitude and phase data of the rotor during the operation of the unit, and obtains the fault diagnosis conclusion through calculation, analysis and judgment. The method is scientific, the conclusion is reliable, and it can realize Automatic real-time online monitoring, analysis and discrimination, etc.

Description of drawings

The present invention is described in detail below in conjunction with accompanying drawing:

Figure 1 is a flow chart of the real-time identification function of rotor vibration anti-phase vector steady state;

Fig. 2 is the flow chart of real-time calculation of inverse vector statistic value;

Fig. 3 is a flow chart of judging the stability of the reverse vector;

Fig. 4 is a schematic diagram of the real-time identification of the steady state of the rotor vibration anti-phase vector of the steam turbine unit.

Detailed ways

The method for real-time identification of the steady-state property of the rotor vibration of the steam turbine-generator set against the phase vector is mainly composed of data collection, real-time calculation and storage of the reverse phase vector, real-time calculation of the statistical value of the reverse phase vector, real-time calculation of the steady-state parameters of the reverse phase vector, and Shafting rotor vibration anti-phase vector steady-state judgment and other links, its functional flow chart is shown in Figure 1. During the real-time identification process, the steady-state parameters of the real part R ^real of the anti-phase vector and the steady-state parameters of the imaginary part R ^imagi are calculated synchronously in real time, and in the determination of the stability of the anti-phase vector of the shafting rotor vibration, at the same time according to the anti-phase vector The steady-state parameters of the real part R ^real and the steady-state parameters of the imaginary part R ^imagi are discriminated, thereby ensuring the reliability of the real-time identification process of the steady-state property of the shafting rotor vibration anti-phase vector and the accuracy of the diagnosis results. The specific implementation steps and diagnostic methods will be further described below in conjunction with the accompanying drawings.

The method can be used to realize the real-time identification of the steady state of the rotor vibration anti-phase vector of the steam turbine unit. The shaft-relative vibration signal of the turbogenerator set required by the real-time identification method and the key-phase signal required for vibration signal analysis and processing can be obtained from the monitoring instrument (TSI) equipped with the turbogenerator set or from professional vibration data acquisition and conditioning equipment. In this embodiment, the shaft-relative vibration signal of the steam turbine generator set and the key-phase signal required for the analysis and processing of the vibration signal are obtained from professional vibration data acquisition and conditioning equipment connected to the vibration sensor. The schematic diagram of the real-time identification of the vibration anti-phase vector steady-state of the shafting rotor of the steam turbine generator set is shown in Figure 4 below. The high-speed data acquisition card is inserted into the slot provided by the industrial microcomputer (IPC). According to the requirements of the high-speed data acquisition card, the professional vibration data acquisition and conditioning equipment processes the shaft relative vibration signal of the steam turbine generator set and the key phase signal required for vibration signal analysis and processing, and the processed shaft relative vibration signal of the steam turbine generator set and the vibration signal analysis The key-phase signal required for processing is input to the high-speed data acquisition card in the IPC. According to this method, a specific computer real-time identification program for the stability of the shafting rotor vibration inverse phase vector is designed, and the real-time identification program is installed in an industrial microcomputer (IPC). A diagnostic cycle process in the real-time identification program of the shafting rotor vibration inversion vector stability, including data acquisition involved in the diagnosis method, real-time calculation and storage of inversion vectors, real-time calculation of inversion vector statistics, inversion vector A series of calculation, analysis and verification links such as real-time calculation of steady-state parameters and determination of the stability of the shafting rotor vibration anti-phase vector.

First of all, the industrial microcomputer (IPC) collects the shaft relative vibration signal of the steam turbine generator set in real time through the high-speed data acquisition card and the key phase signal required for vibration signal analysis and processing.

The rotor unbalance fault real-time identification program is used to monitor and identify whether the low-pressure rotor has an unbalance fault. Using the FFT (fast fourier transform) spectrum analysis method, for the shaft relative vibration data on both sides of the low-pressure rotor A and B of the unit, the real-time synchronous calculation of the shaft relative vibration amplitudes a ^ra and a ^rb (amplitude Value unit is μm) and phase p ^ra , p ^rb data (phase unit is °). Shaft vibration power frequency refers to the frequency corresponding to the working speed when the rotor is running.

分别为转子A、B两侧轴振工频振动矢量。根据下列步骤，计算转子A、B两侧轴振工频振动的反相矢量

的实部R^real、虚部R^imagi。According to the shaft relative vibration power frequency vibration amplitudes a ^ra , a ^rb and phase p ^ra , p ^rb data on both sides of the rotor A and B, calculate the anti-phase vector of the shaft vibration power frequency vibration on both sides of the rotor A and B in

are the shaft vibration power frequency vibration vectors on both sides of rotor A and B respectively. According to the following steps, calculate the anti-phase vector of the shaft vibration power frequency vibration on both sides of the rotor A and B

The real part R ^real and the imaginary part R ^imagi .

的实部A^real、虚部A^imagi以及B侧轴振工频振动矢量

的实部B^real、虚部B^imagi，分别采用公式(1)、(2)、(3)、(4)计算。Calculation of A side shaft vibration power frequency vibration vector

The real part A ^real , the imaginary part A ^imagi and the B side shaft vibration power frequency vibration vector

The real part B ^real and the imaginary part B ^imagi are calculated by formulas (1), (2), (3) and (4) respectively.

A ^real ＝a ^ra ×cos(p ^ra ) (1)

A ^imagi ＝a ^ra ×sin(p ^ra ) (2)

B ^real ＝a ^rb ×cos(p ^rb ) (3)

B ^imagi ＝a ^rb ×sin(p ^rb ) (4)

Calculate the inverted vector The real part R ^real and the imaginary part R ^imagi are calculated using formulas (5) and (6) respectively.

R ^real ＝1/2×(A ^real -B ^real ) (5)

R ^imagi ＝1/2×(A ^imagi -B ^imagi ) (6)

Store the anti-phase vector of the shaft vibration power frequency vibration on both sides of the rotor A and B The real part R ^real and the imaginary part R ^imagi are stored every 0.1 seconds.

的幅值S^amp数据，从T₁时刻向前截取至T₀时刻的转子A、B两侧轴振工频振动的反相矢量的实部R^real、虚部R^imagi数据(振动幅值单位为μm)，|T₁-T₀|＝P_T01，P_T01为预设时间段长度，P_T01＝1200秒。针对T₀时刻至T₁时刻的转子A、B两侧轴振工频振动的反相矢量

的实部R^real、虚部R^imagi数据，振动数据是每隔0.1秒存储一次，并且预设时间段长度P_T01＝1200秒，因此反相矢量

的实部R^real、虚部R^imagi的数据量各为12000个。Figure 2 shows the flow chart of real-time calculation of anti-phase vector statistic value. The real-time calculation link of anti-phase vector statistic value of the real-time identification program is based on the stored power frequency of shaft vibration on both sides of rotor A and B before the current time _T1 . Vibration Antiphase Vector

The amplitude S ^amp data of , intercepted forward from T ₁ time to T ₀ time, the anti-phase vector of shaft vibration power frequency vibration on both sides of rotor A and B The real part R ^real and the imaginary part R ^imagi data (vibration amplitude unit is μm), |T ₁ -T ₀ |=P _T01 , P _T01 is the length of the preset time period, P _T01 =1200 seconds. The anti-phase vector of shaft vibration and power frequency vibration on both sides of rotor A and B from time T ₀ to time T ₁

The real part R ^real and the imaginary part R ^imagi data, the vibration data is stored every 0.1 seconds, and the preset time period length P _T01 = 1200 seconds, so the inversion vector

The data volumes of the real part R ^real and the imaginary part R ^imagi are 12000 respectively.

的实部R^real、虚部R^imagi数据，分为m组，每组n个数据，其中m＝100，n＝120。即反相矢量

的实部R^real(或虚部R^imagi)数据中，第1至第n个元素为第1组，第121至第240个元素为第2组，第241至第360个元素为第3组，...，依次地第(k-1)×120+1至第k×120个元素为第k组，...，第11881至第12000个元素为第100组。反相矢量

实部R^real、虚部R^imagi数据的组号以下标i表示，组内数据的序号以下标j表示，因此

的下标i＝1，2，3，...，100，j＝1，2，3，...，120。轴振工频是指转子正常工作运行时工作转速对应的频率。According to the order of data storage time, respectively reverse the vector

The data of real part R ^real and imaginary part R ^imagi are divided into m groups, each group has n data, where m=100, n=120. the inverse vector

In the real part R ^real (or imaginary part R ^imagi ) data, the 1st to nth elements are the first group, the 121st to 240th elements are the second group, and the 241st to 360th elements are the third group , ..., sequentially (k-1)×120+1th to k×120th elements are the kth group, ..., the 11881st to 12000th elements are the 100th group. inverse vector

The group number of the real part R ^real and the imaginary part R ^imagi data is represented by the subscript i, and the serial number of the data in the group is represented by the subscript j, so

The subscripts i=1, 2, 3, . . . , 100, j=1, 2, 3, . . . , 120. Shaft vibration power frequency refers to the frequency corresponding to the working speed of the rotor during normal operation.

组内标准偏差

及虚部R^imagi的组内均值

组内标准偏差

分别采用公式(7)、(8)、(9)、(10)计算。Calculate the inverted vector The group mean of the real part R ^real

Within standard deviation

and the group mean of the imaginary part R ^imagi

Within standard deviation

Calculated using formulas (7), (8), (9) and (10) respectively.

${\mathrm{\μ}}_i^{\mathrm{real}}=1/120\underset{j=1}{\overset{120}{\mathrm{\Σ}}}{R}_{\mathrm{ij}}^{\mathrm{real}},$ where i = 1, 2, 3, ..., 100 (7)

${\mathrm{\μ}}_i^{\mathrm{imagi}}=1/120\underset{j=1}{\overset{120}{\mathrm{\Σ}}}{R}_{\mathrm{ij}}^{\mathrm{imagi}},$ where i=1, 2, 3, ..., 100 (8)

${\mathrm{\σ}}_i^{\mathrm{real}}=\sqrt{1/120\underset{j=1}{\overset{120}{\mathrm{\Σ}}}{(R_{\mathrm{ij}}^{\mathrm{real}}-{\mathrm{\μ}}_i^{\mathrm{real}})}^2},$ where i = 1, 2, 3, ..., 100 (9)

${\mathrm{\σ}}_i^{\mathrm{imagi}}=\sqrt{1/120\underset{j=1}{\overset{120}{\mathrm{\Σ}}}{(R_{\mathrm{ij}}^{\mathrm{imagi}}-{\mathrm{\μ}}_i^{\mathrm{imagi}})}^2},$ where i = 1, 2, 3, ..., 100 (10)

实部R^real的组内均值

组内标准偏差

及虚部R^imagi的组内均值

组内标准偏差

分别地按照组号排成序列。will invert the vector

The group mean of the real part R ^real

Within standard deviation

and the group mean of the imaginary part R ^imagi

Within standard deviation

Arranged in sequence according to the group number respectively.

实部R^real的组内均值

序列的逆序数

组内标准偏差

序列的逆序数

及虚部R^imagi的组内均值

序列的逆序数组内标准偏差

序列的逆序数

其中，逆序是指在一个数据序列中，一对数的前后位置与大小顺序相反，即前面的数大于后面的数；逆序数是指一个数据序列中逆序的总数。Calculate the inverse vector separately

The group mean of the real part R ^real

reverse sequence number

Within standard deviation

reverse sequence number

and the group mean of the imaginary part R ^imagi

reverse sequence number Within standard deviation

reverse sequence number

Among them, the reverse order means that in a data sequence, the front and rear positions of a pair of numbers are opposite to the order of size, that is, the number in the front is greater than the number in the back; the reverse order number refers to the total number of reverse orders in a data sequence.

的实部R^real的稳态参数

以及虚部R^imagi的稳态参数

分别采用公式(11)、(12)、(13)、(14)计算。In the real-time calculation link of the inverse vector steady-state parameters of the real-time identification program, the inverse vector is calculated

The steady-state parameter of the real part R ^real

and the steady-state parameters of the imaginary part R ^imagi

Calculated using formulas (11), (12), (13) and (14) respectively.

${\mathrm{<span\; class="notranslate">\; \&epsiv;</span>}}_{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}^{\mathrm{<span\; class="notranslate">\; real</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}^{\mathrm{<span\; class="notranslate">\; real</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 0.5</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 11</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; \&epsiv;</span>}}_{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; real</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; real</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 0.5</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 12</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; \&epsiv;</span>}}_{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}^{\mathrm{<span\; class="notranslate">\; imagi</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}^{\mathrm{<span\; class="notranslate">\; imagi</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 0.5</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 13</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; \&epsiv;</span>}}_{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; imagi</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; imagi</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 0.5</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&mu;</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; A</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 14</span>}<span\; class="notranslate">\; )</span>$

In the formulas (11), (12), (13), and (14), μ _A is the theoretical mean value of the inverse ordinal number of the sequence (the number of data items is n), μ _A = m(m-1)/4, m = 100 ;σ _A is the theoretical standard deviation of the inverse ordinal number of the sequence (the number of data items is n), ${\mathrm{\&sigma;}}_A=\sqrt{m({2m}^2+3m-5)/72},m=100.$

实部R^real的组内均值

序列的逆序数

组内标准偏差

序列的逆序数

及虚部R^imagi的组内均值

序列的逆序数

组内标准偏差

序列的逆序数

根据公式(11)、(12)、(13)、(14)，可以计算得到反相矢量的实部R^real的稳态参数

以及虚部R^imagi的稳态参数

Assuming that the shaft vibration on both sides of the low-pressure rotor A and B is the reverse phase vector from the time T ₁ to the time T ₀ of the power frequency vibration

The group mean of the real part R ^real

reverse sequence number

Within standard deviation

reverse sequence number

and the group mean of the imaginary part R ^imagi

reverse sequence number

Within standard deviation

reverse sequence number

According to the formulas (11), (12), (13), (14), the inverse vector can be calculated The steady-state parameter of the real part R ^real

and the steady-state parameters of the imaginary part R ^imagi

的实部R^real数据满足条件

及

并且反相矢量

的虚部R^imagi数据满足条件

及那么可以判定反相矢量

具备稳态性；否则，反相矢量

不具备稳态性。其中，N_1-α/2(0，1)是概率为(1-α/2)的标准正态分布变量值，设定α/2＝2.5％，可知N_0.975(0，1)＝1.9604。Finally, the real-time identification program judges based on the steady-state parameters of the real part R ^real of the anti-phase vector and the steady-state parameters of the imaginary part R ^imagi to determine whether the anti-phase vector of the shafting rotor vibration of the unit is stable, as shown in Figure 3. If the inverting vector

The real part of R ^real data satisfies the condition

and

and inverts the vector

The imaginary part of the R ^imagi data satisfies the condition

and Then it can be determined that the inverse vector

is steady-state; otherwise, the inverting vector

Not stable. Among them, N _1-α/2 (0, 1) is the standard normal distribution variable value with probability (1-α/2), setting α/2=2.5%, it can be seen that N _0.975 (0, 1)=1.9604 .

的实部R^real数据满足条件

及

并且反相矢量

的虚部R^imagi数据满足条件及因此可以判断低压转子在工作转速下振动反相矢量

具备稳态性。According to the current assumptions, the low-pressure rotor vibration anti-phase vector

The real part of R ^real data satisfies the condition

and

and inverts the vector

The imaginary part of the R ^imagi data satisfies the condition and Therefore, it can be judged that the vibration anti-phase vector of the low-pressure rotor at the operating speed is

It is stable.

Claims (3)

1. real-time recognition method for vibration opposite-phase vector stability of turbogenerator unit rotor is characterized in that, may further comprise the steps:

-data acquisition, near the axle Relative Vibration data that record the radial journal bearing of Real-time Collection machine group rotor both sides, tach signal and the key signal of rotor;

-anti-phase vector real-time operation and storage for described axle Relative Vibration data, utilizes the FFT frequency spectrum analysis method, and real-time synchronization calculates rotor A, B two side shaft Relative Vibration power frequency vibration amplitude a ^Ra, a ^RbWith phase place p ^Ra, p ^RbData; According to rotor A, B two side shaft Relative Vibration power frequency vibration amplitude a ^Ra, a ^RbWith phase place p ^Ra, p ^RbData, the anti-phase vector of calculating rotor A, B two side shaft Relative Vibration power frequency vibrations

Wherein

Be respectively rotor A, B two side shaft Relative Vibration power frequency vibration vectors; The anti-phase vector of storage rotor A, B two side shaft Relative Vibration power frequency vibrations

Real part R ^Real, imaginary part R ^Imagi

-anti-phase vector statistics value calculates in real time, according to the current time T that has stored ₁Front rotor A, the anti-phase vector of B two side shaft Relative Vibration power frequency vibrations

Amplitude S ^AmpData are from T ₁Constantly be truncated to forward T ₀Rotor A constantly, the anti-phase vector of B two side shaft Relative Vibration power frequency vibrations

Real part R ^Real, imaginary part R ^ImagiData are according to the time data memory sequencing, respectively with anti-phase vector

Real part R ^Real, imaginary part R ^ImagiData are divided into the m group, every group of n data; Calculate anti-phase vector

Real part R ^RealGroup in average Group internal standard deviation

And imaginary part R ^ImagiGroup in average

Group internal standard deviation

Above-mentioned data are lined up sequence according to group number respectively;

Calculate respectively anti-phase vector

Real part R ^RealGroup in average The backward number of sequence

Group internal standard deviation

The backward number of sequence

And imaginary part R ^ImagiGroup in average

The backward number of sequence

Group internal standard deviation

The backward number of sequence

-anti-phase vector Steady-state Parameters calculates in real time, according to anti-phase vector

Real part R ^RealGroup in average The backward number of sequence

Group internal standard deviation The backward number of sequence

And imaginary part R ^ImagiGroup in average The backward number of sequence

Group internal standard deviation

The backward number of sequence

Calculate anti-phase vector

Real part R ^RealSteady-state Parameters

And imaginary part R ^ImagiSteady-state Parameters

-axle is the anti-phase vector stable state of rotor oscillation sex determination, according to above-mentioned calculating, if anti-phase vector

Real part R ^RealData satisfy condition

And

And anti-phase vector

Imaginary part R ^ImagiData satisfy condition

And

Then judge anti-phase vector

Possesses stable state; Otherwise, anti-phase vector

Do not possess stable state, described N _1-α/2(0,1) is that probability is the standardized normal distribution variate-value of (1-α/2), sets α/2=2.5%, then N _0.975(0,1)=1.9604.

2. the method for claim 1 is characterized in that, it is the current time T that basis has been stored that described anti-phase vector statistics value calculates in real time ₁Front rotor A, the anti-phase vector of B two side shaft Relative Vibration power frequency vibrations

Amplitude S ^AmpData are from T ₁Constantly be truncated to forward T ₀Rotor A constantly, the anti-phase vector of B two side shaft Relative Vibration power frequency vibrations

Real part R ^Real, imaginary part R ^ImagiData, | T ₁-T ₀|=P _T01, P _T01Be Preset Time segment length, P _T01=1200 seconds, for T ₀Constantly to T ₁Rotor A constantly, the anti-phase vector of B two side shaft Relative Vibration power frequency vibrations

Real part R ^Real, imaginary part R ^ImagiData, vibration data are every storage in 0.1 second once, and Preset Time segment length P _T01=1200 seconds, so anti-phase vector Real part R ^Real, imaginary part R ^ImagiData volume respectively be 12000;

According to the time data memory sequencing, respectively with anti-phase vector

Real part R ^Real, imaginary part R ^ImagiData are divided into m group, every group of n data, m=100 wherein, n=120, i.e. anti-phase vector

Real part R ^RealOr imaginary part R ^ImagiIn the data, the 1st to n element is the 1st group, n+1 to a 2n element is the 2nd group, and the 2nd * n+1 to the 3 * n element is the 3rd group ..., in turn k * n element of (k-1) * n+1 to the is the k group, ..., m * n element of (m-1) * n+1 to the is m group, wherein m=100, n=120, anti-phase vector

Real part R ^Real, imaginary part R ^ImagiThe group number of data represents that with subscript i the sequence number of data represents with subscript j in the group, therefore

Subscript i=1,2,3 ..., m, j=1,2,3 ..., n, frequency corresponding to working speed when power frequency refers to the rotor normal operation;

Calculate anti-phase vector Real part R ^RealGroup in average Group internal standard deviation

And imaginary part R ^ImagiGroup in average

Group internal standard deviation

Adopt respectively formula (1), (2), (3), (4) to calculate;

${\mathrm{\&mu;}}_i^{\mathrm{real}}=1/n\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{R}_{\mathrm{ij}}^{\mathrm{real}},$ I=1 wherein, 2,3 ..., m (1)

${\mathrm{\&mu;}}_i^{\mathrm{imagi}}=1/n\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{R}_{\mathrm{ij}}^{\mathrm{imagi}},$ I=1 wherein, 2,3 ..., m (2)

${\mathrm{\&sigma;}}_i^{\mathrm{real}}=\sqrt{1/n\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{(R_{\mathrm{ij}}^{\mathrm{real}}-{\mathrm{\&mu;}}_i^{\mathrm{real}})}^2},$ I=1 wherein, 2,3 ..., m (3)

${\mathrm{\&sigma;}}_i^{\mathrm{imagi}}=\sqrt{1/n\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}{(R_{\mathrm{ij}}^{\mathrm{imagi}}-{\mathrm{\&mu;}}_i^{\mathrm{imagi}})}^2},$ I=1 wherein, 2,3 ..., m (4)

With anti-phase vector Real part R ^RealGroup in average

Group internal standard deviation And imaginary part R ^ImagiGroup in average

Group internal standard deviation

Line up sequence according to group number respectively;

Calculate respectively anti-phase vector

Real part R ^RealGroup in average

The backward number of sequence

Group internal standard deviation

The backward number of sequence

And imaginary part R ^ImagiGroup in average

The backward number of sequence

Group internal standard deviation

The backward number of sequence

3. method as claimed in claim 2 is characterized in that, it is to calculate anti-phase vector that described anti-phase vector Steady-state Parameters calculates in real time

Real part R ^RealSteady-state Parameters

And imaginary part R ^ImagiSteady-state Parameters

Adopt respectively formula (5), (6), (7), (8) to calculate,

${\mathrm{\&epsiv;}}_{\mathrm{\&mu;}}^{\mathrm{real}}=(S_{\mathrm{\&mu;}}^{\mathrm{real}}+0.5-{\mathrm{\&mu;}}_A)/{\mathrm{\&sigma;}}_A---\left(5\right)$

${\mathrm{\&epsiv;}}_{\mathrm{\&sigma;}}^{\mathrm{real}}=(S_{\mathrm{\&sigma;}}^{\mathrm{real}}+0.5-{\mathrm{\&mu;}}_A)/{\mathrm{\&sigma;}}_A---\left(6\right)$

${\mathrm{\&epsiv;}}_{\mathrm{\&mu;}}^{\mathrm{imagi}}=(S_{\mathrm{\&mu;}}^{\mathrm{imagi}}+0.5-{\mathrm{\&mu;}}_A)/{\mathrm{\&sigma;}}_A---\left(7\right)$

${\mathrm{\&epsiv;}}_{\mathrm{\&sigma;}}^{\mathrm{imagi}}=(S_{\mathrm{\&sigma;}}^{\mathrm{imagi}}+0.5-{\mathrm{\&mu;}}_A)/{\mathrm{\&sigma;}}_A---\left(8\right)$

In formula (5), (6), (7), (8), μ A is the backward mathematics opinion average of sequence, μ _A=m (m-1)/4, m=100; σ _AThe backward mathematics opinion standard deviation of sequence,

M=100.

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