CN110987438A - Method for detecting periodical vibration impact signals of hydraulic generator in variable rotating speed process - Google Patents

Method for detecting periodical vibration impact signals of hydraulic generator in variable rotating speed process Download PDF

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CN110987438A
CN110987438A CN201911229108.7A CN201911229108A CN110987438A CN 110987438 A CN110987438 A CN 110987438A CN 201911229108 A CN201911229108 A CN 201911229108A CN 110987438 A CN110987438 A CN 110987438A
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vibration impact
frequency
rotating speed
envelope
signal
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CN110987438B (en
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林家洋
魏运水
王昕�
王利霞
陈学仁
张民威
苏疆东
何继全
刘艺
王振鑫
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Beijing Zhongyuan Ruixun Science & Technology Co ltd
State Grid Fujian Electric Power Co Ltd
Fujian Shuikou Power Generation Group Co Ltd
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Beijing Zhongyuan Ruixun Science & Technology Co ltd
State Grid Fujian Electric Power Co Ltd
Fujian Shuikou Power Generation Group Co Ltd
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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
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    • G—PHYSICS
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    • G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
    • G06F17/141—Discrete Fourier transforms
    • G06F17/142—Fast Fourier transforms, e.g. using a Cooley-Tukey type algorithm

Abstract

The invention relates to a method for detecting a periodical vibration impact signal in a variable rotating speed process of a hydraulic generator, which comprises the following steps of: step S1: collecting initial vibration signal data; step S2, calculating an optimal band-pass filter according to the kurtosis of the fast envelope spectrum, and step S3, performing band-pass filtering by using the optimal band-pass filter to obtain a vibration impact signal waveform; step S4, solving the vibration impact envelope of the vibration impact signal waveform; step S5, calculating the time interval between a plurality of pulses; step S6, estimating a polynomial rotating speed fitting coefficient; step S7, carrying out variable frequency resampling on the vibration impact envelope to obtain the optimal value of the rotating speed fitting coefficient; step S8, constructing a fitting rotation speed polynomial, and performing variable frequency resampling on the vibration impact envelope to obtain a vibration impact envelope waveform sampled in a whole period; step S9, performing fast Fourier transform on the vibration impact envelope waveform sampled in the whole period to obtain a frequency spectrum; and step S10, carrying out fault analysis and evaluation according to the acquired frequency spectrum and the fitted rotating speed.

Description

Method for detecting periodical vibration impact signals of hydraulic generator in variable rotating speed process
Technical Field
The invention relates to the field of hydraulic generator fault detection, in particular to a method for detecting a periodical vibration impact signal in a variable rotating speed process of a hydraulic generator.
Background
The variable rotating speed process of the water-turbine generator set comprises a starting-up process, a stopping process and even an accident stopping process, is a transition process which must be carried out in the normal operation process of the water-turbine generator set, and besides the above processes, the variable rotating speed processes such as a load shedding test process, an overspeed test process and the like are also test processes which must be carried out before the set is normally put into operation. In the various rotating speed changing processes, the structural load of each part of the unit changes violently, the working condition changes complicatedly, and the probability of unit failure is higher. From the viewpoint of fault diagnosis, the process has rich fault symptoms, and therefore, the process is also a key process for effectively identifying various faults. Particularly, if faults such as dynamic and static friction and structural component cracks exist on the unit, periodic vibration impact signals caused by the faults also exist in the vibration signals in the process, and whether the faults such as the dynamic and static friction and the structural component cracks exist in the unit can be judged by detecting and identifying the impact signals. Conventionally, a vibration signal is assisted to synchronously identify a rotating speed and a key phase signal with periodic variation, and then a periodic impact signal related to the rotating speed is identified through signal analysis. However, in many cases, the key phase signal measurement cannot be performed synchronously or the key phase measurement fails, and in such a case, a method for detecting the vibration impact signal under the condition without the key phase needs to be found.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a method for detecting a periodic vibration impact signal during a variable speed process of a hydraulic generator without a key phase signal, which can identify and detect the periodic vibration impact signal without the key phase.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for detecting a periodical vibration impact signal in a variable-speed process of a hydraulic generator comprises the following steps:
step S1: acquiring vibration signal data of a variable-speed process of the hydraulic generator, namely initial vibration signal data;
step S2, calculating an optimal band-pass filter according to the kurtosis of the fast envelope spectrum;
step S3, performing band-pass filtering by adopting an optimal band-pass filter according to the initial vibration signal data to obtain a vibration impact signal waveform;
step S4, adopting digital envelope demodulation to solve and obtain the vibration impact envelope x of the vibration impact signal waveforme(i)；
Step S5, according to the vibration impact envelope waveform data xe(i) Calculating a plurality of pulsesTime interval between the punches: delta T1，ΔT2,…ΔTl；
Step S6, estimating a polynomial speed fitting coefficient by a least square method according to the estimated speed;
step S7, based on the polynomial rotating speed fitting coefficient, adopting a successive approximation method to carry out variable frequency resampling on the vibration impact envelope to obtain the optimal value of the rotating speed fitting coefficient;
step S8, constructing a fitting rotating speed polynomial according to the optimal value of the rotating speed fitting coefficient, and performing variable frequency resampling on the vibration impact envelope to obtain a vibration impact envelope waveform sampled in a whole period;
step S9, performing fast Fourier transform on the vibration impact envelope waveform sampled in the whole period to obtain a frequency spectrum;
and step S10, carrying out fault analysis and evaluation according to the acquired frequency spectrum and the fitted rotating speed.
Further, the step S2 is specifically:
step S21: assume the signals are as follows:
Figure BDA0002303061390000031
in the formula: h (t, omega) is a time-frequency complex envelope of the analyzed signal x (t) and is obtained by adopting fast Fourier transform calculation;
step S22, according to the order moment definition of the spectrum, the spectral kurtosis is expressed as follows:
Figure BDA0002303061390000032
in the formula: c4y(ω)Is the fourth order spectral cumulant of signal y (t), and S (ω) is the spectral moment of the instant;
step S23, adopting rapid spectrum kurtosis algorithm to calculate the kurtosis value of the time domain signal under each frequency band, and corresponding the frequency band B (F) with the maximum kurtosis valuec,ΔBw) As the optimal frequency band, an optimal band pass filter is obtained.
Further, the fast spectral kurtosis algorithm specifically includes:
step S231, setting initial filtering center frequency Fi_cAnd band pass width Δ Bi_w；
Step S232, adopting a mode of '1/3-step two' to gradually layer, decompose and adjust the center frequency and the bandwidth to obtain the enveloping spectral kurtosis under all band-pass filters, and further obtaining the optimal B (F)c,ΔBw)。
Further, the step S6 is specifically:
step S61, according to the vibration impact envelope waveform data xe(i) The calculated time intervals between the pulses are: delta T1，ΔT2,…ΔTl,
Wherein Δ TlIs the time interval between the l +1 th pulse and the l pulses;
step S62, calculating to obtain the instant average rotating speed r1，r2，…rlWherein
Figure BDA0002303061390000033
The estimated average rotating speed between the first week +1 and the first week of the unit; will r is1，r2，…rl(l is more than or equal to 4) lower belt type,
r(t)＝a3t3+a2t2+a1t1+a0(3)
and solving by using a least square method to obtain a polynomial rotating speed fitting coefficient
Figure BDA0002303061390000041
Further, the step S7 is specifically: to be provided with
Figure BDA0002303061390000042
On the basis, a successive approximation method is adopted to solve the optimal polynomial coefficient, and the specific flow steps are as follows:
(a) order to
Figure BDA0002303061390000043
Step size
Figure BDA0002303061390000044
(b) Order to
Figure BDA0002303061390000045
Step size
Figure BDA0002303061390000046
(c) Order to
Figure BDA0002303061390000047
Step size
Figure BDA0002303061390000048
(d) Order to
Figure BDA0002303061390000049
Step size
Figure BDA00023030613900000410
(e) Substituting the above parameters into equation (8) has:
Figure BDA00023030613900000411
Figure BDA00023030613900000412
(f) to be provided with
Figure RE-GDA00023575784100000413
For resampling frequency pairs xe(i) Sampling is carried out one by adopting peak value holding, n iss512 or 1024 points are selected, and the total collected data points are p.nsWherein p is more than or equal to 8. The detailed resampling process is detailed in a flow chart of resampling envelope data according to time-varying rotating speed;
(g) is provided with
Figure BDA00023030613900000414
For the resampled vibration impact envelope data, then
Figure BDA00023030613900000415
Performing fast Fourier transform to obtain
Figure BDA00023030613900000416
Spectrum of
Figure BDA00023030613900000417
Computing
Figure BDA00023030613900000418
Relative main frequency of
Figure BDA00023030613900000419
And amplitude thereof
Figure BDA00023030613900000420
In particular, if
Figure BDA00023030613900000421
The relative main frequency is not 1 time of the rotation speed frequency, and then another
Figure BDA00023030613900000422
(h) Let A3＝A3+ΔA3Such as
Figure BDA00023030613900000423
Executing the next step, otherwise, repeatedly executing the step (e);
(i) let A2＝A2+ΔA2Such as
Figure BDA00023030613900000424
Executing the next step, otherwise, repeatedly executing the step (d);
(j) let A1＝A1+ΔA1Such as
Figure BDA00023030613900000425
Executing the next step, otherwise, repeatedly executing the step (c);
(k) let A0＝A0+ΔA0Such as
Figure BDA00023030613900000426
Executing the next step, otherwise, repeatedly executing the step (b);
(l) From all obtained spectral dominant frequency amplitudes
Figure BDA0002303061390000051
Finding A corresponding to the maximum value0,A1,A2,A3Let a0＝A0,a1＝A1,a2＝A2,a3＝A3Then a above0，a1，a2,a3Is the optimal solution of the polynomial (8).
(m) according to the optimal polynomial coefficient a0，a1，a2,a3For xe(i) Resampling to generate new variable-speed whole-period vibration impact envelope
Figure BDA0002303061390000052
Further, in step S8, the vibration impact envelope after resampling is
Figure BDA0002303061390000053
The fitting coefficient of the rotating speed polynomial is a0，a1，a2,a3To, for
Figure BDA0002303061390000054
Fast Fourier transform to obtain frequency spectrum data
Figure BDA0002303061390000055
Further, the initial vibration signal data includes vibration signals of the frame, the top cover, the stator base and the like mounted on the hydraulic generator set and swing signals of the guide bearings of the upper guide, the lower guide, the water guide and the like.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention adopts a successive approximation mode to solve a polynomial fitting rotating speed change function, then resamples according to the time-varying rotating speed, and can realize the identification and detection of the periodic vibration impact signal under the condition of no key phase through the transformation and analysis of the vibration signal in the rotating speed varying process.
2. The invention can improve the accuracy of frequency spectrum analysis, reduce frequency spectrum leakage and improve the accuracy of judging faults such as dynamic and static friction, structural component cracks and the like.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a diagram illustrating fast spectral kurtosis calculations according to an embodiment of the present invention;
FIG. 3 is a vibration shock waveform signal after filtering with an optimal band pass filter in one embodiment of the present invention;
FIG. 4 is a vibration impact envelope signal in accordance with an embodiment of the present invention;
FIG. 5 is a graphical illustration of the calculation of the time interval of the envelope pulse of the vibration impulse in accordance with an embodiment of the present invention;
FIG. 6 is a flow chart of resampling envelope data according to time-varying rotation speed in an embodiment of the invention;
FIG. 7 is a frequency spectrum diagram of a vibration signal of a unit at a variable rotation speed according to an embodiment of the present invention;
fig. 8 is a frequency spectrum diagram of a vibration signal after resampling under a variable rotation speed of a unit according to an embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
Referring to fig. 1, the present invention provides a method for detecting a periodic vibration impact signal during a variable rotation speed process of a hydro-generator, including the following steps:
step S1: acquiring vibration signal data of a variable-speed process of the hydraulic generator, namely initial vibration signal data;
step S2, calculating an optimal band-pass filter according to the kurtosis of the fast envelope spectrum;
step S3, performing band-pass filtering by adopting an optimal band-pass filter according to the initial vibration signal data to obtain a vibration impact signal waveform;
step S4, adopting digital envelope demodulation to solve and obtain the vibration impact envelope x of the vibration impact signal waveforme(i)；
Step S5, according to the vibration impact envelope waveform data xe(i) Calculating the time interval between the pulses: delta T1，ΔT2,…ΔTl；
Step S6, estimating a polynomial speed fitting coefficient by a least square method according to the estimated speed;
step S7, based on the polynomial rotating speed fitting coefficient, adopting a successive approximation method to carry out variable frequency resampling on the vibration impact envelope to obtain the optimal value of the rotating speed fitting coefficient;
step S8, constructing a fitting rotating speed polynomial according to the optimal value of the rotating speed fitting coefficient, and performing variable frequency resampling on the vibration impact envelope to obtain a vibration impact envelope waveform sampled in a whole period;
step S9, performing fast Fourier transform on the vibration impact envelope waveform sampled in the whole period to obtain a frequency spectrum;
and step S10, carrying out fault analysis and evaluation according to the acquired frequency spectrum and the fitted rotating speed.
In this embodiment, the step S2 specifically includes:
step S21: assume the signals are as follows:
Figure BDA0002303061390000071
in the formula: h (t, omega) is a time-frequency complex envelope of the analyzed signal x (t) and is obtained by adopting fast Fourier transform calculation;
step S22, according to the order moment definition of the spectrum, the spectral kurtosis is expressed as follows:
Figure BDA0002303061390000072
in the formula: c4y(ω)Is the fourth order spectral cumulant of signal y (t), and S (ω) is the spectral moment of the instant;
step S23, adopting rapid spectrum kurtosis algorithm to calculate the kurtosis value of the time domain signal under each frequency band, and corresponding the frequency band B (F) with the maximum kurtosis valuec,ΔBw) As the optimal frequency band, an optimal band pass filter is obtained. The fast spectral kurtosis algorithm specifically comprises the following steps:
step S231, setting initial filtering center frequency Fi_cAnd band pass width Δ Bi_w；
Step S232, adopting a mode of '1/3-step two' to gradually layer, decompose and adjust the center frequency and the bandwidth to obtain the enveloping spectral kurtosis under all band-pass filters, and further obtaining the optimal B (F)c,ΔBw)。
As shown in fig. 2, the kurtosis corresponding to the band-pass bands of each hierarchical decomposition is calculated by using the fast spectral kurtosis pair x (i), where different colors represent different kurtosis values, and the redness of a color is larger. It can be intuitively observed from the figure that the kurtosis value reaches a maximum value when the band pass filter is selected at 53.343Hz,80.015Hz, and thus 53.343Hz,80.015Hz is the optimal band pass filter for the vibration signal.
In this embodiment, the step S3 is specifically to perform narrow-band filtering on the original vibration signal after obtaining the optimal narrow-band filter, and then obtain the envelope waveform of the impulse signal by using digital envelope demodulation techniques such as Hilbert (Hilbert) transform. Fig. 3 is a vibration impact time domain waveform after filtering an original vibration signal by using an optimal band pass filter, and fig. 4 is a vibration impact envelope signal obtained according to digital envelope demodulation. A plurality of high amplitude impulse signals are clearly observed from the waveform, and the period thereof is varied.
In the present embodiment, the time series of the vibration-impact envelope obtained in step two is set to xe(i) (i 1,2.. n) at an acquisition frequency fsWith a period of TsThe total collection time is Δ T ═ nTsAnd the delta T is not less than the time length of 8 rotation periods at the lowest measurable rotation speed of the unit. x is the number ofe(i) Corresponding continuous signal is xe(t)。
As analyzed above, both rub-on and structural crack failures appear as periodic repetitions of the impact signal, with the unit rotating 1,2 or more times a revolution.
Therefore, it can be assumed that at a certain time the unit rotation speed is r (t) (r/min), then the unit rotation speed frequency is
Figure BDA0002303061390000081
Thus, then the frequency of occurrence of the shock pulses is:
Figure BDA0002303061390000082
where R (t) is a function of the speed of rotation over time, fp(t) is a function of the frequency of the vibration-shock pulse as a function of time, then it can be seen that fp(t) is proportional to R (t).
Then x may be adjustede(t) is expressed as:
Figure BDA0002303061390000091
in the above formula, n (t) is a noise signal. In addition to the noise signal, xe(t) is formed by a series of fundamental frequencies fp(t) and its multiple frequency k.fp(t) (k ═ 2,3. - ∞) signal composition. At steady rotational speed fp(t) is constant, and during variable speed fp(t) is a time varying function whose relation to the rotation speed satisfies the formula (3).
According to the sampling theorem and the principle of digital Fourier transform, under the condition of fixed sampling frequency or period, if the period continuation of the signal in the DFT acquisition time window is completely consistent with the actual signal, the leakage phenomenon can not occur. In other words, for time-varying signals
Figure BDA0002303061390000092
If the acquisition time window contains exactly an integer number of signal periods and the acquisition frequency is equal to fpThe integer multiple relation of (t) can avoid the frequency spectrum leakage.
Then if a fitting function r (t) can be found, r (t) can be approximated ideally, that is:
R(t)→r(t) (5)
then:
Figure BDA0002303061390000093
then if a variable step sampling frequency is set:
Figure BDA0002303061390000094
then for any
Figure BDA0002303061390000095
As long as the acquisition time length is ensured to meet the condition that the delta T contains p (p is more than or equal to 8, the total sampling point number is p.ns) Frequency of complete cycles of fp(t) signal, then for any
Figure BDA0002303061390000096
The sampling long time window can meet the requirement of collecting complete signals of k.p periods, and the time-varying collection frequency is completely equal to the time-varying frequency fp(t) is an integer multiple, then it is guaranteed that for any signal xe_k(t) after resampling, fast fourier transform it, whose spectrum is not leaky.
In this embodiment, the 3 rd-order polynomial is adopted to fit and approach the variation function of the unit rotating speed, so that the rotating speed approximation of the unit speed-up and speed-down process, the overspeed test, the load shedding test and other variable rotating speed processes can be met, namely:
r(t)＝a3t3+a2t2+a1t1+a0(8)
in the above equation, t is calculated at the time when the sampling starts to be 0, i-th dataThe corresponding time of the sample is T ═ Ts(i-1). The process of finding the fitting function is to find the polynomial coefficient a0， a1，a2，a3The process of (1). The method comprises the following specific steps:
(1) estimation of polynomial coefficients
To find the four polynomial coefficients, first, a set of polynomial coefficients needs to be estimated
Figure BDA0002303061390000101
The method comprises the following steps:
from vibration impact envelope waveform data xe(i) Calculating the time interval between the plurality of pulses: delta T1，ΔT2,…ΔTlWherein Δ TlIs the time interval between the l +1 th pulse and the l pulses (see fig. 8):
further calculate to obtain the instant average rotating speed r1，r2，…rlWherein
Figure BDA0002303061390000102
Approximated as the average rotational speed between week i +1 and week i of the unit. Will r is1，r2，…rl(l is more than or equal to 4) is substituted into a formula (8) and solved by a least square method to obtain
Figure BDA0002303061390000103
In this embodiment, from the solution process, r1，r2，…rlAre average estimated rotational speeds calculated based on the time interval between pulses, and thus,
Figure BDA0002303061390000104
it is not necessary for all
Figure BDA0002303061390000105
The optimal coefficients for the full-period flush. Thus, it is possible to provide
Figure BDA0002303061390000106
Figure BDA0002303061390000107
On the basis, a successive approximation method is adopted to solve the optimal polynomial coefficient.
The specific process steps are as follows:
(a) order to
Figure BDA0002303061390000111
Step size
Figure BDA0002303061390000112
(b) Order to
Figure BDA0002303061390000113
Step size
Figure BDA0002303061390000114
(c) Order to
Figure BDA0002303061390000115
Step size
Figure BDA0002303061390000116
(d) Order to
Figure BDA0002303061390000117
Step size
Figure BDA0002303061390000118
(e) Substituting the above parameters into equation (8) has:
Figure BDA0002303061390000119
Figure BDA00023030613900001110
(f) to be provided with
Figure RE-GDA00023575784100001111
For resampling frequency pairs xe(i) Sampling is carried out one by adopting peak value holding, n iss512 or 1024 points are selected, and the total collected data points are p.nsWherein p is more than or equal to 8. The detailed resampling process is detailed in a flow chart of resampling envelope data according to time-varying rotating speed;
(g) is provided with
Figure BDA00023030613900001112
For the resampled vibration impact envelope data, then
Figure BDA00023030613900001113
Performing fast Fourier transform to obtain
Figure BDA00023030613900001114
Spectrum of
Figure BDA00023030613900001115
Computing
Figure BDA00023030613900001116
Relative main frequency of
Figure BDA00023030613900001117
And amplitude thereof
Figure BDA00023030613900001118
In particular, if
Figure BDA00023030613900001119
The relative main frequency is not 1 time of the rotation speed frequency, and then another
Figure BDA00023030613900001120
(h) Let A3＝A3+ΔA3Such as
Figure BDA00023030613900001121
Executing the next step, otherwise, repeatedly executing the step (e);
(i) let A2＝A2+ΔA2Such as
Figure BDA00023030613900001122
Executing the next step, otherwise, repeatedly executing the step (d);
(j) let A1＝A1+ΔA1Such as
Figure BDA00023030613900001123
Executing the next step, otherwise, repeatedly executing the step (c);
(k) let A0＝A0+ΔA0Such as
Figure BDA00023030613900001124
Executing the next step, otherwise, repeatedly executing the step (b);
(l) From all obtained spectral dominant frequency amplitudes
Figure BDA00023030613900001125
Finding A corresponding to the maximum value0,A1,A2,A3Let a0＝A0,a1＝A1,a2＝A2,a3＝A3Then a above0，a1，a2,a3Is the optimal solution of the polynomial (8).
(m) according to the optimal polynomial coefficient a0，a1，a2,a3For xe(i) Resampling to generate new variable-speed whole-period vibration impact envelope
Figure BDA00023030613900001126
In this embodiment, the resampled vibration impact envelope is
Figure BDA0002303061390000121
The fitting coefficient of the rotating speed polynomial is a0，a1，a2,a3To, for
Figure BDA0002303061390000122
Fast Fourier transform to obtain frequency spectrum data
Figure BDA0002303061390000123
In this embodiment, FIG. 7 shows the original vibration envelope data x directly vibrating with a unit covere(i) The spectrum obtained by performing a fast Fourier transform, and FIG. 8 is for xe(i) Resampled vibration impact envelope
Figure BDA0002303061390000124
And performing fast Fourier transform to obtain frequency spectrum.
Comparing fig. 7 and 8, it can be seen that:
in fig. 7, the main frequency lines have significant side lobes, but the side lobes of the main frequency lines in fig. 8 are small;
the amplitude of each of the major frequencies of fig. 7 is significantly less than the amplitude of each of the major frequencies of fig. 8, typically the amplitude of the 1 st major frequency of fig. 7 is about 1.17, and the amplitude of the 1 st major frequency of fig. 8 is 1.568.
Therefore, the spectrum in fig. 7 has leakage, which results in significant errors in frequency and amplitude, and ideal leakage-free spectrum data can be obtained under the condition of no key phase signal by using the method.
In this embodiment, a frequency spectrum with a small error can be obtained, and meanwhile, coefficients of each order of a fitting rotation speed function can be obtained, so as to calculate the rotation speed at each time according to the formula (8). And comparing the fitting rotating speed with the actual rotating speed of the unit, so that the impact frequency per week can be determined, and the fault type can be further determined. And the development degree of various faults can be determined by analyzing the change of the amplitude of the main frequency.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.

Claims (7)

1. A method for detecting a periodical vibration impact signal in a variable-speed process of a hydraulic generator is characterized by comprising the following steps of:
step S1: acquiring vibration signal data of a variable-speed process of the hydraulic generator, namely initial vibration signal data;
step S2, calculating an optimal band-pass filter according to the kurtosis of the fast envelope spectrum;
step S3, performing band-pass filtering by adopting an optimal band-pass filter according to the initial vibration signal data to obtain a vibration impact signal waveform;
step S4, adopting digital envelope demodulation to solve and obtain the vibration impact envelope x of the vibration impact signal waveforme(i)；
Step S5, according to the vibration impact envelope waveform data xe(i) Calculating the time interval between the plurality of pulses: delta T1，ΔT2,…ΔTl；
Step S6, estimating a polynomial speed fitting coefficient by a least square method according to the estimated speed;
step S7, based on the polynomial rotating speed fitting coefficient, adopting a successive approximation method to carry out variable frequency resampling on the vibration impact envelope to obtain the optimal value of the rotating speed fitting coefficient;
step S8, constructing a fitting rotating speed polynomial according to the optimal value of the rotating speed fitting coefficient, and performing variable-frequency resampling on the vibration impact envelope to obtain a vibration impact envelope waveform sampled in a whole period;
step S9, performing fast Fourier transform on the vibration impact envelope waveform sampled in the whole period to obtain a frequency spectrum;
and step S10, carrying out fault analysis and evaluation according to the acquired frequency spectrum and the fitted rotating speed.
2. The method for detecting the periodic vibration impact signal in the variable-speed process of the hydro-generator according to claim 1, wherein the step S2 specifically comprises:
step S21: assume the signals are as follows:
Figure FDA0002303061380000021
in the formula: h (t, omega) is a time-frequency complex envelope of the analyzed signal x (t) and is obtained by adopting fast Fourier transform calculation;
step S22, according to the order moment definition of the spectrum, the spectral kurtosis is expressed as follows:
Figure FDA0002303061380000022
in the formula: c4y(ω)Is the fourth order spectral cumulant of signal y (t), and S (ω) is the spectral moment of the instant;
step S23, adopting rapid spectrum kurtosis algorithm to calculate the kurtosis value of the time domain signal under each frequency band, and corresponding the frequency band B (F) with the maximum kurtosis valuec,ΔBw) As the optimal frequency band, an optimal band pass filter is obtained.
3. The method for detecting the periodic vibration impact signals of the hydro-generator in the variable rotating speed process according to claim 2, wherein the fast spectral kurtosis algorithm is specifically as follows:
step S231, setting initial filtering center frequency Fi_cAnd band pass width Δ Bi_w；
Step S232, adopting a mode of '1/3-step two' to gradually layer, decompose and adjust the center frequency and the bandwidth to obtain the enveloping spectrum kurtosis under all band-pass filters, and further obtaining the optimal B (F)c,ΔBw)。
4. The method for detecting the periodic vibration impact signal in the variable-speed process of the hydro-generator according to claim 1, wherein the step S6 specifically comprises:
step S61, according to the vibration impact envelope waveform data xe(i) The calculated time intervals between the pulses are: delta T1，ΔT2,…ΔTl,
Wherein Δ TlIs the time interval between the l +1 th pulse and the l pulses;
step S62, calculating to obtain the instant averageMean rotational speed r1，r2，…rlWherein
Figure FDA0002303061380000023
The estimated average rotating speed between the first week +1 and the first week of the unit; will r is1，r2，…rl(l.gtoreq.4) the following formula,
r(t)＝a3t3+a2t2+a1t1+a0(3)
and solving by using a least square method to obtain a polynomial rotating speed fitting coefficient
Figure FDA0002303061380000031
5. The method for detecting the periodic vibration impact signal in the variable-speed process of the hydro-generator according to claim 4, wherein the step S7 is specifically as follows: to be provided with
Figure FDA0002303061380000032
On the basis, a successive approximation method is adopted to solve the optimal polynomial coefficient, and the specific flow steps are as follows:
(a) order to
Figure FDA0002303061380000033
Step size
Figure FDA0002303061380000034
(b) Order to
Figure FDA0002303061380000035
Step size
Figure FDA0002303061380000036
(c) Order to
Figure FDA0002303061380000037
Step size
Figure FDA0002303061380000038
(d) Order to
Figure FDA0002303061380000039
Step size
Figure FDA00023030613800000310
(e) Substituting the above parameters into equation (3) has:
Figure FDA00023030613800000311
Figure FDA00023030613800000312
(f) to be provided with
Figure FDA00023030613800000313
For resampling frequency pairs xe(i) Sampling is carried out one by adopting peak value holding, n iss512 or 1024 points are selected, and the total collected data points are p.nsWherein p is more than or equal to 8;
(g) is provided with
Figure FDA00023030613800000314
For the resampled vibration impact envelope data, pair
Figure FDA00023030613800000315
Performing fast Fourier transform to obtain
Figure FDA00023030613800000316
Spectrum of
Figure FDA00023030613800000317
Computing
Figure FDA00023030613800000318
Relative main frequency of
Figure FDA00023030613800000319
And amplitude thereof
Figure FDA00023030613800000320
If it is not
Figure FDA00023030613800000321
The relative main frequency is not 1 time of the rotation speed frequency, and then another
Figure FDA00023030613800000322
(h) Let A3＝A3+ΔA3Such as
Figure FDA00023030613800000323
Executing the next step, otherwise, repeatedly executing the step (e);
(i) let A2＝A2+ΔA2Such as
Figure FDA00023030613800000324
Executing the next step, otherwise, repeatedly executing the step (d);
(j) let A1＝A1+ΔA1Such as
Figure FDA00023030613800000325
Executing the next step, otherwise, repeatedly executing the step (c);
(k) let A0＝A0+ΔA0Such as
Figure FDA00023030613800000326
Executing the next step, otherwise, repeatedly executing the step (b);
(l) From all obtained spectral dominant frequency amplitudes
Figure FDA0002303061380000041
Finding A corresponding to the maximum value0,A1,A2,A3Let a0＝A0,a1＝A1,a2＝A2,a3＝A3Then a above0，a1，a2,a3Is the optimal solution of the polynomial (8).
6. The method for detecting the periodic vibration impact signal in the variable-speed process of the hydraulic generator according to claim 5, wherein the step S8 is to obtain the vibration impact envelope after resampling as
Figure FDA0002303061380000042
The fitting coefficient of the rotating speed polynomial is a0，a1，a2,a3To, for
Figure FDA0002303061380000043
Fast Fourier transform to obtain frequency spectrum data
Figure FDA0002303061380000044
7. The method for detecting the periodic vibration impact signal of the hydro-generator during the variable-speed process according to any one of claims 1 to 5, wherein: the initial vibration signal data comprise collected vibration signals of the frame, the top cover and the stator base which are arranged on the water turbine generator set and swing signals of the guide bearing parts such as an upper guide, a lower guide and a water guide.
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