CN102966526A - Diagnosis method for low-frequency fluid vibration excitation failure of compressor based on axial vibration analysis - Google Patents

Abstract

The invention discloses a diagnosis method for a low-frequency fluid vibration excitation failure of a compressor based on axial vibration analysis. The method comprises the steps that an acquired shaft vibration signal is resolved into a direct current component and a dynamic alternating current component; then dynamic signal frequencies related to axial movement of a rotor are removed by coherence analysis; a residual frequency spectrum is continuously refined by further local frequencies; fluid impact characteristic frequency range energy in a low-frequency component is analyzed; analysis and comparison with a typical failure characteristic signal are conducted; and the low-frequency fluid vibration excitation failure can be quickly distinguished and judged. With the adoption of the method, quantification description of states of vane blades impacted by abnormal fluid is conducted with energy spectrum density of a low-frequency air flow characteristic frequency range, and the low-frequency fluid vibration excitation failure is accurately identified and quantified, tracked and monitored in real time through trend tracking analysis.

Description

Compressor low frequency fluid exciting class method for diagnosing faults based on the axial vibration analysis

Technical field

The present invention relates to the fault diagnosis technology of compressor behavior, be specifically related to a kind of method from compressor shaft to oscillating signal that finish low-frequency vibrating failure Quick and quantification tracking and monitoring with.

Background technique

In compressor operating, except turning frequency class fault, because Operating condition adjustment, lack of standardization and the blade of regular job, impeller, valve, runner problem cause low frequency (frequency 0～turn frequently between) fluid exciting class fault, mainly comprise, the non-linear class fault of low frequency is planted in oil whirl, oil whip, obstruction, rotation disengaging, flow-induced vibration, surge, sealing gland wearing and tearing, pedestal looseness and pipeline excitations etc. more than ten, this type of fault generation frequency is higher, especially easily causes the catastrophe failures such as leaf destruction under the cyclic stress effects such as long-term abnormal fluid impact.

How such abnormal fluid impulse fault accurately being differentiated and division, and estimate leaf longevity according to the blade strength degradation trend, prevented the catastrophe failures such as leaf destruction, thrust disc wearing and tearing, is one of compressor daily state monitoring importance.

Tradition low frequency fluid exciting class fault diagnosis mode utilizes the radial vibration signal by the FFT conversion, in frequency domain, analyze, the mode of utilizing characteristic spectra to divide is divided faults such as oil film fault, flow perturbation, machinery become flexible, and usually in 0.37～0.45 frequency multiplication as oil film fault signature frequency range, (0.2～0.4) or (0.6～0.8) frequency multiplication is the flow perturbation characteristic spectra, and 0.5,0.33,0.25 isodisperse unit's frequency multiplication is the loosening eigen frequency of machinery.This conventional method can't accurately be divided the failure frequency that is in characteristic boundary zone and overlap region, be which kind of low frequency fluid exciting class fault (such as faults such as oil whip, flow-induced vibration, flow field disorders) for making a definite diagnosis, also can't carry out Quantitative Monitoring and follow the tracks of.

Summary of the invention

The object of the invention be to provide a kind of intuitively, fast, the method for compressor low frequency fluid exciting class fault accurately and effectively, the method is based on compressor shaft to the compressor low frequency fluid exciting class method for diagnosing faults of many information fusion of analysis of vibration signal.

For this reason: the compressor low frequency fluid exciting class method for diagnosing faults of analyzing based on axial vibration provided by the invention at first gathers the axial vibration signal of compressor bank, extracts respectively DC component and the AC compounent of axial vibration signal; Then change in conjunction with unit Operating condition adjustment and DC component, carry out the coherence analysis that dynamic communication component and axial displacement change, remove because axial displacement changes and cause exchanging the impact of oscillating signal; Then to exchanging dynamic component, utilize local frequencies continuous refinement technology, obtain its accurate eigen frequency, according to low frequency fluid exciting class fault frequency division different characteristic frequency range of living in and energy threshold thereof, judge low frequency fluid exciting class failure mode.

The concrete steps of said method are as follows:

Step 1, the axial vibration signal of collection compressor bank, the DC component of the axial vibration signal that gathers is that X (t) signal, AC compounent are Y (t) signal;

Step 2 utilizes (formula 1) to calculate signal X (t) and the coherence factor γ of signal Y (t) at different frequency ω place _XY(ω)

${\mathrm{\γ}}_{\mathrm{XY}}\left(\mathrm{\ω}\right)=\frac{\left|G_{\mathrm{XY}}\left(\mathrm{\ω}\right)\right|}{{\left[G_X\left(\mathrm{\ω}\right)G_Y\left(\mathrm{\ω}\right)\right]}^{1/2}}---\left(1\right)$

In the formula (1): G _XBe that signal X (t) is at the certainly spectrum at frequencies omega place, G (ω) _YBe that signal Y (t) is at the certainly spectrum at frequencies omega place, G (ω) _XY(ω) be signal X (t) and the cross-spectrum of signal Y (t) at the frequencies omega place;

Step 3 is rejected among the signal Y (t) and signal X (t) coherence coefficient γ _XY(ω) greater than the vibration frequency components of selecting threshold value; To weed out and signal X (t) coherence coefficient γ _XY(ω) be reduced to time domain greater than the signal Y (t) of the vibration frequency components of selected threshold value and obtain signal x to be analyzed ₁(n) (n=0,1,2,3 ..., N-1), wherein, selected threshold value is 0.6～0.8;

Step 4 adopts local frequencies continuous refinement method to treat analytic signal x ₁(n) (n=0,1,2,3 ..., N-1) process and obtain zoom FFT, and then obtain accurate eigen frequency from zoom FFT:

(1) obtains signal x to be analyzed by Fourier transformation ₁(n) (n=0,1,2,3 ..., panorama frequency spectrum X N-1) ₁(k):

$X_1\left(k\right)=\underset{n=0}{\overset{N-1}{\mathrm{\Σ}}}{x}_1(n/f_s)\exp (-2\mathrm{j\πkn}/N),$ 0≤k≤N-1（2）

In the formula (2), f _sBe sample frequency;

(2) to panorama frequency spectrum X ₁(k) the low frequency spectral coverage in carries out obtaining after the windowing process frequency spectrum X (k):

$X\left(k\right)=\underset{n=0}{\overset{N-1}{\mathrm{\Σ}}}x(n/f_s)\exp (-2\mathrm{j\πkn}/N),$ 0≤k≤N-1（3）

(3) frequency spectrum X (k) is carried out inverse Fourier transform, obtaining sample frequency is f _s, sampling number is the burst of N: { x (t _n), t _n=n/f _s, n=0,1,2,3 ..., N-1}, the Fourier transformation of this burst is frequency spectrum X (k);

(4) frequency spectrum X (k) is carried out continuous Fourier transformation and obtains zoom FFT X (f):

$X\left(f\right)=\underset{n=0}{\overset{N}{\mathrm{\Σ}}}x\left(t_n\right)\exp (-2\mathrm{j\πnf}/f_s),$ 0≤f≤f _s(4）

Step 5, when eigen frequency turned frequently less than or equal to 0.1 times, fault type was the loosening or pipeline exciting of ground crackle, ground;

When eigen frequency turns frequently greater than 0.1 times and turns frequently less than or equal to 0.42 times, perhaps, when eigen frequency turned frequently greater than 0.6 times and turns frequently less than or equal to 0.9 times, fault was flow-induced vibration, flow perturbation, surge, rotation breaks away from or block;

When eigen frequency turned frequently greater than 0.43 times and turns frequently less than or equal to 0.47 times, fault was oil whirl or oil whip;

Otherwise, for signal disturbs.

Further utilization to said method, by zoom FFT medium and low frequency (frequency 0～turn frequently between) airflow characteristic band energy spectral density is quantized monitoring, prediction is avoided causing leaf destruction because of high cycle fatigue, the accidents such as thrust bearing shoe valve burns more greatly because the axial flow active force fluctuates are specially execution of step five rear continuation and carry out following steps:

Step 6 when fault is flow-induced vibration or flow perturbation, according to the fault current status of compressor bank, is determined monitoring periods σ;

Step 7 every the σ time, is monitored compressor bank;

Carry out above-mentioned steps one to step 4 during each monitoring, utilize formula (5) to calculate the low frequency airflow characteristic band energy spectral density φ (ω) that ought last time monitor, establishing the total hop count that ought last time monitor the airflow characteristic frequency range that needs monitoring is A/2,

$\mathrm{\φ}\left(\mathrm{\ω}\right)={\∫}_{f_2}^{f_1}{\left|x\left(f\right)\right|}^2\mathrm{df}+{\∫}_{f_4}^{f_3}{\left|x\left(f\right)\right|}^2\mathrm{df}+\·\·\·+{\∫}_{f_a}^{f_a}{\left|x\left(f\right)\right|}^2\mathrm{df}\·\·\·+{\∫}_{f_A}^{f_{A-1}}{\left|x\left(f\right)\right|}^2\mathrm{df}---\left(5\right)$

In the formula (5), a=1,3,5 ..., A-1; f ₁, f ₃F _aF _A-1Initial frequency for need monitoring airflow characteristic frequency range; f ₂, f ₄F _A+1F _ATermination frequency for need monitoring airflow characteristic frequency range;

Step 8, the blade injury degree D that utilizes formula (6) to calculate ought last time to monitor, and when D＞0.5, show that blade enters critical days,

D＝φ(ω)/τ（6）

In the formula (6), τ is the blade injury degree of monitored compressor bank and the correlation coefficient of low frequency airflow characteristic band energy spectral density.

The present invention is based on compressor shaft to Vibration Analysis Technology, utilize unheeded axial vibration parameter in the usually diagnosis, by making up axial rotor dynamics normal modal and frequency spectrum refinement technology, can quick and precisely distinguish various fluid exciting class faults, shorten the unit malfunction elimination time, avoid the engineering staff blindly to carry out the unit maintenance maintenance, improve Fault Identification precision and fault treatment efficient.For the realization compressor blade is subjected to fluid impact, and new thinking has been opened up in the high cycle fatigue that causes fracture quantitative study, and compared with prior art, the present invention has following features:

1) traditional divided oscillation signal analysis method utilizes the radial vibration signal to carry out fault diagnosis usually, thereby has ignored the mass efficient information that each bearing shaft comprises in the oscillating signal.The present invention has considered the vibration information of rotor on the three dimensionality direction, and the low-frequency information of especially axially directly transmitting reaches the various low frequency fluid of accurate identification exciting class fault.

2) by interpositioning, obtain accurately axial vibration amplitude, frequency, phase place, make accurate spectrum division, make oscillating signal can show more clearly unit low frequency fault.

3) still belong to world-famous puzzle because of dynamic monitoring blade crackle and Strength Changes, carry out quantificational description by compressor drum being subjected to flow field active force energy change, can effectively prevent leaf destruction, improve unit safety.

Description of drawings

Below in conjunction with accompanying drawing and embodiment the present invention is described in further detail.

Fig. 1 is the compressor bank air inlet side substantially horizontal spectrogram corresponding with table 1;

Fig. 2 is the compressor bank air inlet side Vertical direction spectrogram corresponding with table 2;

Fig. 3 is the compressor bank exhaust side substantially horizontal spectrogram corresponding with table 3;

Fig. 4 is the compressor bank exhaust side Vertical direction spectrogram corresponding with table 4;

Fig. 5 is the compressor bank axial direction spectrogram corresponding with table 5;

Fig. 6 is the compressor bank axial direction spectrogram corresponding with table 6;

Fig. 7 is the compressor bank axial direction spectrogram corresponding with table 7.

Embodiment

The present invention introduces axial low frequency (frequency 0～turn frequently between) analysis of vibration signal technology, adopts the dynamic communication in the axial vibration signal is partly carried out the frequency spectrum refinement to analyze, and low frequency fluid exciting class fault is carried out accurate identifying and diagnosing and quantized tracking and monitoring.

At first, for allowing the oscillating signal that obtains show more clearly compressor low frequency fault, investigate the coherence that itself and direct current signal change, weed out the impact that the axial float signal produces oscillating signal, and the axial vibration AC portion signal that obtains carried out the FFT conversion, and use the characteristic spectral line interpositioning, obtain accurately amplitude, frequency and phase place, make accurate frequency spectrum, better to appear the actual vibration feature of low frequency fluid exciting class trouble signal suddenly.Contrast characteristic feature collection of illustrative plates, directly convection cell exciting class fault is differentiated.And further utilize the low frequency energy spectral density for quantizing monitoring index, combine with blade actual fatigue life of statistical value, to blade strength decay and changed by the fluid impact field can to quantize to follow the tracks of to describe, not only can shorten the malfunction elimination time, improve Fault Identification precision and fault treatment efficient, also can effectively estimate the blade fatigue fracture life-span, avoid leaf destruction, the generation of the pernicious faults such as thrust-bearing damage.

According to technological scheme of the present invention, implementation step of the present invention is as follows:

1) current vortex sensor that utilizes axial direction to install gathers the axial vibration signal of compressor bank, for the compressor that shaft displacement probe is arranged, can directly utilize its shaft displacement probe sampled signal, for the compressor of installation shaft displacement probe not, take the free end end face as measurement face, current vortex sensor is installed on the axle head end cap; The DC component of the axial vibration signal that gathers is that X (t) signal, AC compounent are Y (t) signal;

2) calculate two random signals at the coherence factor γ at different frequency ω place by formula (1) _XY(ω):

${\mathrm{\γ}}_{\mathrm{XY}}\left(\mathrm{\ω}\right)=\frac{\left|G_{\mathrm{XY}}\left(\mathrm{\ω}\right)\right|}{{\left[G_X\left(\mathrm{\ω}\right)G_Y\left(\mathrm{\ω}\right)\right]}^{1/2}}---\left(1\right)$

In the formula (1): G _XBe that signal X (t) is at the certainly spectrum at frequencies omega place, G (ω) _YBe that signal Y (t) is at the certainly spectrum at frequencies omega place, G (ω) _XY(ω) be signal X (t) and the cross-spectrum of signal Y (t) at the frequencies omega place;

3) reject among the signal Y (t) and signal X (t) coherence coefficient γ _XY(ω) greater than the vibration frequency components of selected threshold value, will weed out and signal X (t) coherence coefficient γ _XY(ω) be reduced to time domain greater than the signal Y (t) of the vibration frequency components of selected threshold value and obtain signal x to be analyzed ₁(n) (n=0,1,2,3 ..., N-1), wherein, selected threshold value is 0.6～0.8;

4) adopt local frequencies continuous refinement method to treat analytic signal x ₁(n) (n=0,1,2,3 ..., N-1) process and obtain zoom FFT, and then obtain accurate eigen frequency from zoom FFT:

1. obtain the panorama spectrum by FFT, doing sample frequency with FFT is f _s, sampling number is the signal time sequence x to be analyzed of N ₁(n) (n=0,1,2,3 ..., panorama spectrum X N-1) ₁(k):

$X_1\left(k\right)=\underset{n=0}{\overset{N-1}{\mathrm{\Σ}}}{x}_1(n/f_s)\exp (-2\mathrm{j\πkn}/N),$ 0≤k≤N-1（2）

2. at panorama spectrum X ₁(k) upper selected low frequency region spectral coverage is done windowing process to this wavelength coverage, and other frequencies of elimination and noise obtain new frequency spectrum X (k):

$X\left(k\right)=\underset{n=0}{\overset{N-1}{\mathrm{\Σ}}}x(n/f_s)\exp (-2\mathrm{j\πkn}/N),$ 0≤k≤N-1（3）

3. be the contrary FFT of frequency spectrum X (k), can obtain a sample frequency and still be f _s, sampling number still is the new time series of N: { x (t _n), t _n=n/f _s, n=0,1,2,3 ..., N-1}, the FFT of this signal are transformed to X (k);

4. use continuous Fourier transform spectromether frequency spectrum X (k) to process calculating, spectrum curve is regarded as continuous, the k that reaches formula (3) regards a continuous real number in interval 0≤k≤N-1 as, then can obtain continuous frequency f, see formula (4), be beneficial to this formula and carry out the frequency spectrum refinement, can according to responsive characteristics of low-frequency frequency range separation Set arbitrarily refinement density, low frequency class fault accurately be judged and differentiation;

$X\left(f\right)=\underset{n=0}{\overset{N}{\mathrm{\Σ}}}x\left(t_n\right)\exp (-2\mathrm{j\πnf}/f_s),$ 0≤f≤f _s（4）

5) the characteristic spectra table of comparisons 0 is determined the fault type of unit.

Table 0

Avoid causing leaf destruction because of high cycle fatigue in order to predict, the accidents such as thrust bearing shoe valve burns more greatly because the axial flow active force fluctuates, after finishing, said method can by zoom FFT medium and low frequency (frequency 0～turn frequently between) airflow characteristic band energy spectral density be quantized monitoring, be specially execution of step five rear continuation and carry out following steps:

6) when fault is flow-induced vibration or flow perturbation, according to the fault current status of compressor bank, determine monitoring periods σ;

7) every the σ time, compressor bank is monitored;

Carry out above-mentioned steps one to step 4 during each monitoring, utilize formula (5) to calculate the low frequency airflow characteristic band energy spectral density φ (ω) that ought last time monitor, establishing the total hop count that ought last time monitor the airflow characteristic frequency range that needs monitoring is A/2,

$\mathrm{\φ}\left(\mathrm{\ω}\right)={\∫}_{f_2}^{f_1}{\left|x\left(f\right)\right|}^2\mathrm{df}+{\∫}_{f_4}^{f_3}{\left|x\left(f\right)\right|}^2\mathrm{df}+\·\·\·+{\∫}_{f_a}^{\mathrm{fa}}{\left|x\left(f\right)\right|}^2\mathrm{df}\·\·\·+{\∫}_{f_A}^{f_{A-1}}{\left|x\left(f\right)\right|}^2\mathrm{df}---\left(5\right)$

In the formula (5), a=1,3,5 ..., A-1; f ₁, f ₃F _aF _A-1Initial frequency for need monitoring airflow characteristic frequency range; f ₂, f ₄F _A+1F _ATermination frequency for need monitoring airflow characteristic frequency range;

Step 8, the blade injury degree D that utilizes formula (6) to calculate ought last time to monitor, and when D＞0.5, show that blade enters critical days,

D＝φ(ω)/τ（6）

In the formula (6), τ is the blade injury degree of monitored compressor bank and the correlation coefficient of low frequency airflow characteristic band energy spectral density.

Different leaves for the different compressors group, its blade injury degree D is different from the correlation coefficient τ of low frequency airflow characteristic band energy spectral density, when adopting method of the present invention to carry out fault treatment to a certain unit, adopt the method for statistics to calculate the blade injury degree of a certain model blade of this unit and the average correlation coefficient τ of low frequency airflow characteristic band energy spectral density value, be specially blade from intact average correlation coefficient τ=∑ (φ when fractureing _p(ω)/D _P)/P, p are current statistics number, and P is the total degree of statistics, and p=1,2,3 ..., P.

Blade injury degree D value among the present invention is 0＜D≤1, represents that when D=1 blade damages fully.The flaw detector monitoring is adopted in the monitoring of traditional blades injury tolerance D, but the method needs shutdown, the dismounting blade, and observation process takes time and effort, and method of the present invention can be carried out on-line monitoring to the injury tolerance of blade.

Abnormal airflow disturbance fault in the low frequency fluid exciting class fault, usually in radial vibration performance frequency in 0.3～0.8 octave coverage, be difficult for oil film, electric, machinery disturbs, become flexible etc., and other low frequency fault signature frequency range is distinguished mutually.Adopt the axial vibration analytical method, feature performance frequency range upper and lower extends to 0.1～0.9 frequency multiplication, common frequency is 0.1～0.42,0.6～0.9 frequency multiplication, and with the faults such as machinery is loosening without overlapping frequency range, the fluid excitation faults such as easier accurate identification flow-induced vibration, rotation disengaging, surge and oil whirl, and the band energy of flow-induced vibration fault size affects trend with flow perturbation and is consistent, and has more practical reference value in long-term follow monitoring blade high cycle fatigue fracture environmental factor.

Below be the embodiment that the inventor provides, so that technological scheme of the present invention is further explained explanation:

It is stator blade adjustable axial-flow compressor AV80-15 that axial flow compressor among this embodiment is used blower fan, model for ironmaking, and the main design parameters of this unit sees Table O ':

Table O '

Working speed	4257	r/min
			Extraction flow	4726	Nm 3/min
Suction pressure	99.7	KPa
			Exhaust pressure	358.7	KPa

After third level leaf destruction has occured in September, 2007 in this unit, with its third level number of blade by 26 original replacings to 29 slice.

In April, 2009, to the moving-stator blade gap of this unit adjust, the machine group rotor is advised circle, change sealing, finish the rotor high-speed dynamic balancing, bearing shell is supported in grinding and adjustment.

In May, 2009, the operation of this unit commitment, the unit inlet and outlet side radially vibration measuring probe shaft vibration value that records unit is 25um to the maximum, but the forward and backward bearing case shell of blower fan shakes and reaches 5.6mm/s (vibration severity).

Whole fault treating procedure to this unit after putting into operation in May, 2009 is as follows:

At first the radial vibration signal of unit is analyzed:

Observe blower fan radial vibration signal, from each measuring point frequency spectrum analysis of components of inlet and outlet side, low-frequency vibration component less, and the distribution frequency range is disturbed with oil film characteristic spectra, machinery, loosening frequency range is approaching, is not easily distinguishable.Monitoring result is seen Fig. 1, table 1, Fig. 2, table 2, Fig. 3, table 3, Fig. 4 and table 4, comprises 0.71 times, 0.7 times, 0.36 times, 0.35 times, 0.23 times, 0.22 times, 0.18 times, 0.05 times isodisperse frequency multiplication value among the result.Wherein, 0.71,0.7 frequency multiplication is maximum, is 4.29um, through confirming that this 0.71,0.7 frequency multiplication energy frequency is 50Hz, is caused by alternating current disturbance, can't draw effective judgement information such as flow-induced vibration from low-frequency range absolute energy size.

Table 1 compressor bank air inlet side substantially horizontal vibration parameters

Table 2 compressor bank air inlet side vertical vibration parameter

Table 3 compressor bank exhaust side substantially horizontal vibration parameters

Table 4 compressor bank exhaust side vertical vibration parameter

Then adopt method of the present invention that the fault of unit has been carried out following processing:

Monitoring, diagnosing adopts the Rotview6.0 equipment of Xi'an Communications University's research and development to gather, and sample information is as follows:

Sample frequency: f _s=2048Hz

Sampling number: N=2048

The axle data of shaking: utilize unit to be with the peek of Bently3300 Buffer output, the axial vibration signal utilizes the original axial displacement sensor of unit to obtain.

The shell data of shaking: utilize the Bently velocity transducer to measure at bearing portion.

1) gathers the axial vibration signal, concrete grammar is to utilize the axial displacement eddy current probe, get the output voltage signal AC portion, carry out after straight, filtering are processed, reject with the DC component coherence factor greater than 0.6 frequency component, the new frequency spectrum low frequency signal that obtains is partly carried out the local frequencies continuous refinement process.

2) from the zoom FFT that obtains, obviously tell, except power frequency energy composition, larger low frequency component is arranged, this component frequency range is near 0.14 frequency multiplication (frequency is 10.26Hz), coincide with normal modal downstream frequency range, relatively the oil film characteristic spectra has larger differentiation, sees Table 5 and Fig. 5, is diagnosed as the flow-induced vibration fault.Calculating its energy spectral density desired value is 0.772 μ m ²/ Hz is τ according to adding up this type of unit (N5, N8 blade profile) injury tolerance correlation coefficient early stage _N5=3.75, τ _N8=3.18, estimation blade injury degree is respectively 0.20 and 0.24, all within margin of safety.

Table 5 compressor bank axial direction vibration parameters (before the maintenance, in May, 2009)

3) in December, 2009, this unit axial vibration is sampled again, observe that energy rises appreciably near 0.14 frequency multiplication, see Table 6 and Fig. 6, twice characteristic spectra district (0.1～0.3 frequency multiplication district) energy spectral density index is by 0.772 μ m before and after the contrast ²/ Hz increases to 2.136 μ m ²/ Hz, this rotor are subjected to flow-induced vibration to affect further aggravation.Calculate for two kinds of blade profiles according to the injury tolerance correlation coefficient, the estimation injury tolerance is respectively: 0.57,0.67 greater than 0.5, enters critical days.Rotor has been carried out strip inspection, blade is detected a flaw, the fatigue crack of about 10 millimeter has appearred in part N8 type root of blade, as continuing operation, must bring the serious consequences such as leaf destruction.Further the investigation Crack carries out frequency measurement to every blade, finds that part N8 type blade 1 rank natural frequency, N5 type blade 2 rank natural frequencys turn frequency with unit and become the integral multiple relation, and blade is subjected to airflow influence to produce resonance, causes this blade crackle hidden danger.

Table 6 compressor bank axial direction vibration parameters (in December, 2009)

4) through the blade that crackle occurs is changed, and with all the other resonance blades repair frequently process after, unit puts into operation again, axial vibration decrease, and keep stable, thrust bearing temperature also obviously descends.Record frequency spectrum medium and low frequency composition and the power frequency composition decrease of axial vibration, see Table 7 and Fig. 7, the air-flow frequency range composition of low frequency almost disappears.

Table 7 compressor bank axial direction vibration parameters (after the maintenance)

Claims (3)

1. a compressor low frequency fluid exciting class method for diagnosing faults of analyzing based on axial vibration is characterized in that method at first gathers the axial vibration signal of compressor bank, extracts respectively DC component and the AC compounent of axial vibration signal; Then change in conjunction with unit Operating condition adjustment and DC component, carry out the coherence analysis that dynamic communication component and axial displacement change, remove because axial displacement changes and cause exchanging the impact of oscillating signal; Then to exchanging dynamic component, utilize local frequencies continuous refinement technology, obtain its accurate eigen frequency, according to low frequency fluid exciting class fault frequency division different characteristic frequency range of living in and energy threshold thereof, judge low frequency fluid exciting class failure mode.

2. the compressor low frequency fluid exciting class method for diagnosing faults of analyzing based on axial vibration as claimed in claim 1 is characterized in that the concrete steps of method are as follows:

Step 1, the axial vibration signal of collection compressor bank, the DC component of the axial vibration signal that gathers is that X (t) signal, AC compounent are Y (t) signal;

Step 2 utilizes (formula 1) to calculate signal X (t) and the coherence factor γ of signal Y (t) at different frequency ω place _XY(ω)

${\mathrm{\γ}}_{\mathrm{XY}}\left(\mathrm{\ω}\right)=\frac{\left|G_{\mathrm{XY}}\left(\mathrm{\ω}\right)\right|}{{\left[G_X\left(\mathrm{\ω}\right)G_Y\left(\mathrm{\ω}\right)\right]}^{1/2}}---\left(1\right)$

In the formula (1): G _XBe that signal X (t) is at the certainly spectrum at frequencies omega place, G (ω) _YBe that signal Y (t) is at the certainly spectrum at frequencies omega place, G (ω) _XY(ω) be signal X (t) and the cross-spectrum of signal Y (t) at the frequencies omega place;

Step 3 is rejected among the signal Y (t) and signal X (t) coherence coefficient γ _XY(ω) greater than the vibration frequency components of selecting threshold value; To weed out and signal X (t) coherence coefficient γ _XY(ω) be reduced to time domain greater than the signal Y (t) of the vibration frequency components of selected threshold value and obtain signal x to be analyzed ₁(n) (n=0,1,2,3 ..., N-1), wherein, selected threshold value is 0.6～0.8;

Step 4 adopts local frequencies continuous refinement method to treat analytic signal x ₁(n) (n=0,1,2,3 ..., N-1) process and obtain zoom FFT, and then obtain accurate eigen frequency from zoom FFT:

(1) obtains signal x to be analyzed by Fourier transformation ₁(n) (n=0,1,2,3 ..., panorama frequency spectrum X N-1) ₁(k):

$X_1\left(k\right)=\underset{n=0}{\overset{N-1}{\mathrm{\Σ}}}{x}_1(n/f_s)\exp (-2\mathrm{j\πkn}/N),$ 0≤k≤N-1（2）

In the formula (2), f _sBe sample frequency;

(2) to panorama frequency spectrum X ₁(k) the low frequency spectral coverage in carries out obtaining after the windowing process frequency spectrum X (k):

$X\left(k\right)=\underset{n=0}{\overset{N-1}{\mathrm{\Σ}}}x(n/f_s)\exp (-2\mathrm{j\πkn}/N),$ 0≤k≤N-1（3）

(3) frequency spectrum X (k) is carried out inverse Fourier transform, obtaining sample frequency is f _s, sampling number is the burst of N: { x (t _n), t _n=n/f _s, n=0,1,2,3 ..., N-1}, the Fourier transformation of this burst is frequency spectrum X (k);

(4) frequency spectrum X (k) is carried out continuous Fourier transformation and obtains zoom FFT X (f):

$X\left(f\right)=\underset{n=0}{\overset{N}{\mathrm{\Σ}}}x\left(t_n\right)\exp (-2\mathrm{j\πnf}/f_s),$ 0≤f≤f _s（4）

Obtain accurate eigen frequency from zoom FFT;

Step 5, when eigen frequency turned frequently less than or equal to 0.1 times, fault type was the loosening or pipeline exciting of ground crackle, ground;

When eigen frequency turned frequently greater than 0.1 times and turns frequently less than or equal to 0.42 times, perhaps, when eigen frequency turned frequently greater than 0.6 times and turns frequently less than or equal to 0.9 times, fault was flow-induced vibration, flow perturbation, surge, rotation disengaging or blocks;

When eigen frequency turned frequently greater than 0.43 times and turns frequently less than or equal to 0.47 times, fault was oil whirl or oil whip;

Otherwise, for signal disturbs.

3. the compressor low frequency fluid exciting class method for diagnosing faults of analyzing based on axial vibration as claimed in claim 2 is characterized in that method is further comprising the steps of:

Step 6 when fault is flow-induced vibration or flow perturbation, according to the fault current status of compressor bank, is determined monitoring periods σ;

Step 7 every the σ time, is monitored compressor bank;

Carry out above-mentioned steps one to step 4 during each monitoring, utilize formula (5) to calculate the low frequency airflow characteristic band energy spectral density φ (ω) that ought last time monitor, establishing the total hop count that ought last time monitor the airflow characteristic frequency range that needs monitoring is A/2,

$\mathrm{\φ}\left(\mathrm{\ω}\right)={\∫}_{f_2}^{f_1}{\left|x\left(f\right)\right|}^2\mathrm{df}+{\∫}_{f_4}^{f_3}{\left|x\left(f\right)\right|}^2\mathrm{df}+\·\·\·+{\∫}_{f_a}^{f_a}{\left|x\left(f\right)\right|}^2\mathrm{df}\·\·\·+{\∫}_{f_A}^{f_{A-1}}{\left|x\left(f\right)\right|}^2\mathrm{df}---\left(5\right)$

In the formula (5), a=1,3,5 ..., A-1; f ₁, f ₃F _aF _A-1Initial frequency for need monitoring airflow characteristic frequency range; f ₁, f ₄F _A+1F _ATermination frequency for need monitoring airflow characteristic frequency range;

Step 8, the blade injury degree D that utilizes formula (6) to calculate ought last time to monitor, and when D＞0.5, show that blade enters critical days,

D＝φ(ω)/τ（6）

In the formula (6), τ is the blade injury degree of monitored compressor bank and the correlation coefficient of low frequency airflow characteristic band energy spectral density.

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