CN110296095A - Thermal power plant's station boiler air-introduced machine operating status intellectual monitoring diagnostic method - Google Patents
Thermal power plant's station boiler air-introduced machine operating status intellectual monitoring diagnostic method Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D27/00—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids
- F04D27/001—Testing thereof; Determination or simulation of flow characteristics; Stall or surge detection, e.g. condition monitoring
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2260/00—Function
- F05D2260/80—Diagnostics
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2270/00—Control
- F05D2270/30—Control parameters, e.g. input parameters
- F05D2270/334—Vibration measurements
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention discloses a kind of thermal power plant's station boiler air-introduced machine operating status intellectual monitoring diagnostic method, this method is arranged in bearing block for exporting the vibrating sensor of vibration signal, and obtains the technological parameter of air-introduced machine revolving speed；The original vibration signal Xi of acquisition vibrating sensor output simultaneously makees spectrum analysis, extracts the characteristic signal of motor；The caused vibration classification indicators of motor stator failure are established respectively, are extracted electric and magnetic oscillation fault signature caused by motor gas-gap unevenness, are established the extremely caused electric and magnetic oscillation classification indicators of motor rotor conducting bar, extract mechanical oscillation fault signature caused by fan rotor imbalance, establish mechanical oscillation classification indicators and extract the mechanical oscillation fault signature due to caused by installation die misalignment that motor and each bearing block rolling bearing of blower generate extremely；And the fault pre-alarming of air-introduced machine is provided accordingly.The operating status of this method real-time monitoring air-introduced machine finds all kinds of defects of motor and blower, it is ensured that the normal operation of station boiler equipment in time.
Description
Technical field
The present invention relates to equipment detection and diagnosis technical fields more particularly to a kind of thermal power plant's station boiler air-introduced machine to run
State intelligent monitoring diagnostic method.
Background technique
Structure is complicated for thermal power plant's station boiler, and there are also the dedusting of flue gas, desulphurization plant, flue gas emission resistance is larger, therefore
Flue gas is excluded in flue setting air-introduced machine, while air-introduced machine also provides the negative pressure of boiler furnace needs.The work of air-introduced machine
Principle is the hot-air after burning is sucked out in boiler furnace, and negative pressure is caused in boiler furnace, and hot-air is changed by various
After hot device, eventually passes through and atmosphere is discharged by chimney after dedusting, desulphurization and denitration.Air inducing machine equipment includes motor and blower, master
Wanting failure mode to show as, electrical fault, fan rotor are uneven, installation misaligns and rolling bearing fault.
Wherein, fan rotor is uneven, installation misaligns and the failures such as rolling bearing cause fan vibration abnormal, in turn
Motor stator coil temperature is caused to improve (up to 400 DEG C), current of electric and load increase, and power source trip, station boiler stops
Fortune, seriously affects the normal production run of thermal power plant's station boiler.
Summary of the invention
Technical problem to be solved by the invention is to provide a kind of thermal power plant's station boiler air-introduced machine operating statuses intelligently to supervise
Diagnostic method is surveyed, the operating status of this method real-time monitoring air-introduced machine finds all kinds of defects of motor and blower in time, it is ensured that electricity
It stands the normal operation of boiler plant.
In order to solve the above technical problems, thermal power plant's station boiler air-introduced machine operating status intellectual monitoring diagnostic method of the present invention
Include the following steps:
Step 1: the vibrating sensor for exporting vibration signal is arranged in motor and each bearing block of blower, and by drawing
The technological parameter of blower fan control system acquisition air-introduced machine revolving speed；
Step 2: the original vibration signal Xi=D (i), i=l, 2 of acquisition vibrating sensor output, ----N, to the original
Beginning vibration signal Xi makees spectrum analysis, extracts the characteristic signal of motor oscillating abnormal state；
The global vibration value A of original vibration signal is calculated by formula (1)；
Step 3: vibrating classification indicators caused by establishing motor stator failure；Stator failure can be in 2 frequencys multiplication of supply frequency
Place generates strong vibration, for the original vibration signal obtained by bearing block, through FFT transform by Xi (t) (i=1,2) with
And reconstitute motor stator vibration signal S (t) after oscillating component Xp (t) the inverse superposition at 2 times of supply frequencies,
FFT transform is carried out to reconstruction signal S (t), obtains vibration amplitude F at 2 times of supply frequencies, calculate vibration amplitude F with
The ratio M of original signal global vibration value A,
M=F/A (3)
Ratio M is set to monitor motor stator failure coefficient, motor stator failure is forecast as M > 20%；
Step 4: extracting electric and magnetic oscillation fault signature caused by motor gas-gap unevenness；Stator bias can be in rotor and stator
Between generate a uniform air gap, to form the very strong vibration of a directionality, there is magnetic in two sides at 2 times of supply frequencies
Pole is by the sideband of frequency, and magnetic pole is the product of slip frequency and number of magnetic poles by frequency, and choosing mid-band frequency is
100Hz, frequency range arrive 100+2 × motor pole number × slip frequency since 100-2 × motor pole number × slip frequency
Terminate, the vibration peak at 100Hz and the frequency spectrum weighted value in selected frequency range are subjected to operation, set air gap unevenness failure
Factor G；
G=(AG+1/AG+UG+1/UG)/(2AG+2/AG) (4)
A in formula (3)G、UGVibration peak respectively in vibration amplitude and selected frequency range of the motor at 100Hz adds
Weight average；
Motor gas-gap unevenness fault compression G is monitored, motor gas-gap unevenness failure is forecast as G > 1.2；
Step 5: establishing the extremely caused electric and magnetic oscillation classification indicators of motor rotor conducting bar；Original vibration signal is passed through
FFT transform extracts the vibration amplitude component Xi (t) (i=1,2) at l times of speed-frequency f, 2 times of speed-frequency 2f, passes through ZOOM-
FFT refinement analysis obtains vibration amplitude XL1 and XH1 at mono- 2 Δ f and f+2 Δ f of f, obtains at 2f-2 Δ f and 2f+2 Δ f
Vibration amplitude XL2 and XH2, wherein Δ f is slip frequency, if motor rotor conducting bar broken strip coefficient is J,
J=max ([(XL1+XH1)/2]/X1, [(XL2+XH2)/2]/X2) (5)
In formula, max indicates to take the larger value in two numbers, and [(XL1+XH1)/2]/X1 indicates two sides at 1 times of speed-frequency
Sideband vibration amplitude be averaged after divided by the vibration amplitude at 1 times of speed-frequency, [(XL2+XH2)/2]/X2 indicates that 2 times turn
Divided by the vibration amplitude at 2 times of speed-frequencies after two sides are averaged with vibration amplitude at fast frequency,
According to formula (4) monitoring motor rotor conducting bar whether there is or not broken strip, the diagnosing motor rotor bar broken strip as J > 20%；
Step 6: extracting mechanical oscillation fault signature caused by fan rotor imbalance；For the original obtained by bearing block
Beginning vibration signal extracts 1 times of speed-frequency, the vibration amplitude component Xi (t) (i=1,2) at 2 times of speed-frequencies through FFT transform,
Fan body quality imbalance fault signal ψ (t) is reconstructed after being superimposed by inverse,
The ratio H of imbalance fault signal ψ (t) and original signal global vibration value A are monitored,
H=ψ (t)/A (7)
It is uneven that fan rotor is diagnosed as H > 60%；
Step 7: establishing the mechanical oscillation classification indicators that motor and each bearing block rolling bearing of blower generate extremely；It rolls
Bearing fault shows that unusual Vibration Level has an impact, the vibration performance frequencies of each components of rolling bearing and bearing parameter
Relationship are as follows:
Outer race rumble spectrum: f0=nfr(1-dcosα/D)/2 (8)
Bearing inner ring rumble spectrum: fi=nfr(1+dcosα/D)/2 (9)
Bearing roller rumble spectrum: fp=fr(D/d){1-[d(cosα)/D]2}/2 (10)
Bearing retainer rumble spectrum: fh={ fi[1-d(cosα)/D]±fo[1+d(cosα)/D]}/2 (11)
In formula: n is rolling element number, frFor inner and outer ring relative rotation speed frequency, d be rolling element diameter, D is pitch diameter, α is
Contact angle；
In rolling bearing, early period failure due to impact signal energy it is low, be usually submerged in ambient noise, utilize
The characteristic signal of rolling bearing is extracted in Hilbert transformation, determines the bearing fault factor using bearing features frequency amplitude tracing,
The frequency band near bearing characteristic frequency is chosen as monitoring object, by the frequency in the vibration peak and selected frequency band at characteristic frequency
It composes weighted value and carries out operation, set bearing fault compression Bk；
Bk=(Afk+1/Afk+Ufk+1/Ufk)/(2Afk+2/Afk) (12)
Wherein, Afk, UfkRespectively bearing features frequency fi, fo, fp, fhVibration in the vibration amplitude at place and selected frequency band
Peak value weighted average,
The alarming value for setting the rolling bearing component failure factor is respectively rolling bearing inner ring alarm limit of malfunction S1, rolling
Moving axis outer ring alarm limit of malfunction S2, rolling bearing rolling element alarm limit of malfunction S3With rolling bearing retainer alarm limit of malfunction S4, prison
Depending on the bearing fault factor B at each characteristic frequencyk(k=l, 2,3,4)；Work as B1> S1When, determine rolling bearing inner ring failure；When
B2> S2When, determine housing washer failure；Work as B3> S3When, determine rolling bearing rolling element failure；Work as B4> S4When, determine
Rolling bearing retainer failure；
Step 8: extracting the mechanical oscillation fault signature due to caused by installation die misalignment；For the original vibration obtained
Dynamic signal extracts the vibration at 1 times of speed-frequency, 2 times of speed-frequencies, 3 times of speed-frequencies and 4 times of speed-frequencies through FFT transform
Amplitude components Xi (t) (i=1,2,3,4), fault-signal caused by reconstruct installation die misalignment, setting pair after being superimposed by inverse
Middle bad error coefficient is P,
Die misalignment failure coefficient P is monitored, forecasts that motor installs die misalignment as P > 40%.
Since thermal power plant's station boiler air-introduced machine operating status intellectual monitoring diagnostic method of the present invention uses above-mentioned technology
Scheme, i.e. this method pass through first in motor and each bearing block setting of blower for exporting the vibrating sensor of vibration signal
The technological parameter of air inducing machine control system acquisition air-introduced machine revolving speed；The original vibration signal Xi of vibrating sensor output is acquired, it is right
Original vibration signal Xi makees spectrum analysis, extracts the characteristic signal of motor oscillating abnormal state；Motor stator event is established respectively
Vibration classification indicators caused by barrier extract electric and magnetic oscillation fault signature caused by motor gas-gap unevenness, establish motor rotor conducting bar
Abnormal caused electric and magnetic oscillation classification indicators extract mechanical oscillation fault signature caused by fan rotor imbalance, establish motor
The mechanical oscillation classification indicators generated extremely with each bearing block rolling bearing of blower and extraction are since installation die misalignment causes
Mechanical oscillation fault signature；And the fault pre-alarming of air-introduced machine is provided accordingly.The operating status of this method real-time monitoring air-introduced machine,
All kinds of defects of discovery motor and blower in time, it is ensured that the normal operation of station boiler equipment.
Detailed description of the invention
The present invention will be further described in detail below with reference to the accompanying drawings and embodiments:
Fig. 1 is the flow diagram of thermal power plant's station boiler air-introduced machine operating status intellectual monitoring diagnostic method of the present invention.
Specific embodiment
Embodiment is as shown in Figure 1, thermal power plant's station boiler air-introduced machine operating status intellectual monitoring diagnostic method packet of the present invention
Include following steps:
Step 1: the vibrating sensor for exporting vibration signal is arranged in motor and each bearing block of blower, and by drawing
The technological parameter of blower fan control system acquisition air-introduced machine revolving speed；
Step 2: the original vibration signal Xi of acquisition vibrating sensor output, makees frequency spectrum point to original vibration signal Xi
The characteristic signal of motor oscillating abnormal state is extracted in analysis；
The global vibration value A of original vibration signal is calculated by formula (1)；
Step 3: vibrating classification indicators caused by establishing motor stator failure；Stator failure can be in 2 frequencys multiplication of supply frequency
Place generates strong vibration, for the original vibration signal obtained by bearing block, through FFT transform by Xi (t) (i=1,2) with
And reconstitute motor stator vibration signal S (t) after oscillating component Xp (t) the inverse superposition at 2 times of supply frequencies,
FFT transform is carried out to reconstruction signal S (t), obtains vibration amplitude F at 2 times of supply frequencies, calculate vibration amplitude F with
The ratio M of original signal global vibration value A,
M=F/A (3)
Ratio M is set to monitor motor stator failure coefficient, motor stator failure is forecast as M > 20%；
Step 4: extracting electric and magnetic oscillation fault signature caused by motor gas-gap unevenness；Stator bias can be in rotor and stator
Between generate a uniform air gap, to form the very strong vibration of a directionality, there is magnetic in two sides at 2 times of supply frequencies
Pole is by the sideband of frequency, and magnetic pole is the product of slip frequency and number of magnetic poles by frequency, and choosing mid-band frequency is
100Hz, frequency range arrive 100+2 × motor pole number × slip frequency since 100-2 × motor pole number × slip frequency
Terminate, the vibration peak at 100Hz and the frequency spectrum weighted value in selected frequency range are subjected to operation, set air gap unevenness failure
Factor G；
G=(AG+1/AG+UG+1/UG)/(2AG+2/AG) (4)
A in formula (3)G、UGVibration peak respectively in vibration amplitude and selected frequency range of the motor at 100Hz adds
Weight average；
Motor gas-gap unevenness fault compression G is monitored, motor gas-gap unevenness failure is forecast as G > 1.2；
Step 5: establishing the extremely caused electric and magnetic oscillation classification indicators of motor rotor conducting bar；Original vibration signal is passed through
FFT transform extracts the vibration amplitude component Xi (t) (i=1,2) at 1 times of speed-frequency f, 2 times of speed-frequency 2f, passes through ZOOM-
FFT refinement analysis obtains vibration amplitude XL1 and XH1 at f-2 Δ f and f+2 Δ f, obtains the vibration at 2f-2 Δ f and 2f+2 Δ f
Dynamic amplitude XL2 and XH2, wherein Δ f is slip frequency, if motor rotor conducting bar broken strip coefficient is J,
J=max ([(XL1+XH1)/2]/X1, [(XL2+XH2)/2]/x2) (5)
In formula, max indicates to take the larger value in two numbers, and [(XL1+XH1)/2]/X1 indicates two sides at 1 times of speed-frequency
Sideband vibration amplitude be averaged after divided by the vibration amplitude at 1 times of speed-frequency, [(XL2+XH2)/2]/X2 indicates that 2 times turn
Divided by the vibration amplitude at 2 times of speed-frequencies after two sides are averaged with vibration amplitude at fast frequency,
According to formula (4) monitoring motor rotor conducting bar whether there is or not broken strip, the diagnosing motor rotor bar broken strip as J > 20%；
Step 6: extracting mechanical oscillation fault signature caused by fan rotor imbalance；For the original obtained by bearing block
Beginning vibration signal extracts 1 times of speed-frequency, the vibration amplitude component Xi (t) (i=1,2) at 2 times of speed-frequencies through FFT transform,
Fan body quality imbalance fault signal ψ (t) is reconstructed after being superimposed by inverse,
The ratio H of imbalance fault signal ψ (t) and original signal global vibration value A are monitored,
H=ψ (t)/A (7)
It is uneven that fan rotor is diagnosed as H > 60%；
Step 7: establishing the mechanical oscillation classification indicators that motor and each bearing block rolling bearing of blower generate extremely；It rolls
Bearing fault shows that unusual Vibration Level has an impact, the vibration performance frequencies of each components of rolling bearing and bearing parameter
Relationship are as follows:
Outer race rumble spectrum: f0=nfr(1-dcosα/D)/2 (8)
Bearing inner ring rumble spectrum: fi=nfr(1+dcosα/D)/2 (9)
Bearing roller rumble spectrum: fp=fr(D/d){1-[d(cosα)/D]2}/2 (10)
Bearing retainer rumble spectrum: fh={ fi[1-d(cosα)/D]±fo[1+d(cosα)/D]}/2 (11)
In formula: n is rolling element number, frFor inner and outer ring relative rotation speed frequency, d be rolling element diameter, D is pitch diameter, d is
Contact angle；
In rolling bearing, early period failure due to impact signal energy it is low, be usually submerged in ambient noise, utilize
The characteristic signal of rolling bearing is extracted in Hilbert transformation, determines the bearing fault factor using bearing features frequency amplitude tracing,
The frequency band near bearing characteristic frequency is chosen as monitoring object, by the frequency in the vibration peak and selected frequency band at characteristic frequency
It composes weighted value and carries out operation, set bearing fault compression Bk；
Bk=(Afk+1/Afk+Ufk+1/Ufk)/(2Afk+2/Afk) (12)
Wherein, Afk, UfkRespectively bearing features frequency fi, fo, fp, fhVibration in the vibration amplitude at place and selected frequency band
Peak value weighted average,
The alarming value for setting the rolling bearing component failure factor is respectively rolling bearing inner ring alarm limit of malfunction S1, rolling
Moving axis outer ring alarm limit of malfunction S2, rolling bearing rolling element alarm limit of malfunction S3With rolling bearing retainer alarm limit of malfunction S4, prison
Depending on the bearing fault factor B at each characteristic frequencyk(k=1,2,3,4)；Work as B1> S1When, determine rolling bearing inner ring failure；When
B2> S2When, determine housing washer failure；Work as B3> S3When, determine rolling bearing rolling element failure；Work as B4> S4When, determine
Rolling bearing retainer failure；
Step 8: extracting the mechanical oscillation fault signature due to caused by installation die misalignment；For the original vibration obtained
Dynamic signal extracts the vibration at 1 times of speed-frequency, 2 times of speed-frequencies, 3 times of speed-frequencies and 4 times of speed-frequencies through FFT transform
Amplitude components Xi (t) (i=1,2,3,4), fault-signal caused by reconstruct installation die misalignment, setting pair after being superimposed by inverse
Middle bad error coefficient is P,
Die misalignment failure coefficient P is monitored, forecasts that motor installs die misalignment as P > 40%.
This method is directed to the main failure forms of air-introduced machine, and the bearing block that vibrating sensor is installed on motor and blower is hung down
Histogram is upward, and for acquiring the vibration data of motor and blower, the failure for obtaining motor and fan condition by signal reconstruction is special
Parameter is levied, intellectual monitoring and the diagnosis of air-introduced machine operating status are realized using classification indicators, holds the bad of air-introduced machine operating status
Change trend.When classification indicators exception, on-line system provides warning information, and guidance operation and equipment management personnel take reply to arrange
It applies, so that the operating status of real-time monitoring air-introduced machine, finds all kinds of defects of motor and blower in time, ensure that the normal of equipment
Operation.
Claims (1)
1. a kind of thermal power plant's station boiler air-introduced machine operating status intellectual monitoring diagnostic method, it is characterised in that this method includes such as
Lower step:
Step 1: in motor and each bearing block setting of blower for exporting the vibrating sensor of vibration signal, and pass through air-introduced machine
The technological parameter of control system acquisition air-introduced machine revolving speed；
Step 2: the original vibration signal Xi=D (i), i=1,2 of acquisition vibrating sensor output, N are original to this
Vibration signal Xi makees spectrum analysis, extracts the characteristic signal of motor oscillating abnormal state；
The global vibration value A of original vibration signal is calculated by formula (1)；
Step 3: vibrating classification indicators caused by establishing motor stator failure；Stator failure can produce at 2 frequencys multiplication of supply frequency
Raw strong vibration passes through Xi (t) (i=1,2) and 2 times through FFT transform for the original vibration signal obtained by bearing block
Reconstitute motor stator vibration signal S (t) after oscillating component Xp (t) inverse superposition at supply frequency,
FFT transform is carried out to reconstruction signal S (t), obtains vibration amplitude F at 2 times of supply frequencies, calculate vibration amplitude F with it is original
The ratio M of signal global vibration value A,
M=F/A (3)
Ratio M is set to monitor motor stator failure coefficient, motor stator failure is forecast as M > 20%；
Step 4: extracting electric and magnetic oscillation fault signature caused by motor gas-gap unevenness；Stator bias can produce between rotor and stator
A raw uniform air gap, to form the very strong vibration of a directionality, it is logical to there is magnetic pole for two sides at 2 times of supply frequencies
The sideband of overfrequency, magnetic pole are the product of slip frequency and number of magnetic poles by frequency, and selection mid-band frequency is 100Hz,
Frequency range terminates since 100-2 × motor pole number × slip frequency to 100+2 × motor pole number × slip frequency,
Vibration peak at 100Hz and the frequency spectrum weighted value in selected frequency range are subjected to operation, set air gap unevenness fault compression
G；
G=(AG+1/AG+UG+1/UG)/(2AG+2/AG) (4)
A in formula (3)G、UGVibration peak weighting respectively in vibration amplitude and selected frequency range of the motor at 100Hz is flat
?；
Motor gas-gap unevenness fault compression G is monitored, motor gas-gap unevenness failure is forecast as G > 1.2；
Step 5: establishing the extremely caused electric and magnetic oscillation classification indicators of motor rotor conducting bar；Original vibration signal is become through FFT
The vibration amplitude component Xi (t) (i=1,2) extracted at 1 times of speed-frequency f, 2 times of speed-frequency 2f is changed, it is thin by ZOOM-FFT
Change the vibration amplitude XL1 and XH1 at analysis acquisition f-2 Δ f and f+2 Δ f, obtains the vibration amplitude at 2f-2 Δ f and 2f+2 Δ f
XL2 and XH2, wherein Δ f is slip frequency, if motor rotor conducting bar broken strip coefficient is J,
J=max ([(XL1+XH1)/2]/X1, [(XL2+XH2)/2]/X2) (5)
In formula, max indicates to take the larger value in two numbers, and [(XL1+XH1)/2]/X1 indicates two sides band at 1 times of speed-frequency
Vibration amplitude be averaged after divided by the vibration amplitude at 1 times of speed-frequency, [(XL2+XH2)/2]/X2 indicates 2 times of speed-frequencies
Place two sides be averaged with vibration amplitude after divided by the vibration amplitude at 2 times of speed-frequencies,
According to formula (4) monitoring motor rotor conducting bar whether there is or not broken strip, the diagnosing motor rotor bar broken strip as J > 20%；
Step 6: extracting mechanical oscillation fault signature caused by fan rotor imbalance；For the original vibration obtained by bearing block
Dynamic signal passes through through the vibration amplitude component Xi (t) (i=1,2) at 1 times of speed-frequency of FFT transform extraction, 2 times of speed-frequencies
Fan body quality imbalance fault signal ψ (t) is reconstructed after inverse superposition,
The ratio H of imbalance fault signal ψ (t) and original signal global vibration value A are monitored,
H=ψ (t)/A (7)
It is uneven that fan rotor is diagnosed as H > 60%；
Step 7: establishing the mechanical oscillation classification indicators that motor and each bearing block rolling bearing of blower generate extremely；Rolling bearing
Failure shows that unusual Vibration Level has impact, the vibration performance frequency of each components of rolling bearing and the relationship of bearing parameter
Are as follows:
Outer race rumble spectrum: f0=nfr(1-d cosα/D)/2 (8)
Bearing inner ring rumble spectrum: fi=nfr(1+d cosα/D)/2 (9)
Bearing roller rumble spectrum: fp=fr(D/d){1-[d(cosα)/D]2}/2 (10)
Bearing retainer rumble spectrum: fh={ fi[1-d(cosα)/D]±fo[1+d(cosα)/D]}/2 (11)
In formula: n is rolling element number, frFor inner and outer ring relative rotation speed frequency, d be rolling element diameter, D is pitch diameter, α is contact
Angle；
In rolling bearing, early period failure due to impact signal energy it is low, be usually submerged in ambient noise, utilize
The characteristic signal of rolling bearing is extracted in Hilbert transformation, determines the bearing fault factor using bearing features frequency amplitude tracing,
The frequency band near bearing characteristic frequency is chosen as monitoring object, by the frequency in the vibration peak and selected frequency band at characteristic frequency
It composes weighted value and carries out operation, set bearing fault compression Bk；
Bk=(Afk+1/Afk+Ufk+1/Ufk)/(2Afk+2/Afk) (12)
Wherein, Afk, UfkRespectively bearing features frequency fi, fo, fp, fhVibration peak in the vibration amplitude at place and selected frequency band
Weighted average,
The alarming value for setting the rolling bearing component failure factor is respectively rolling bearing inner ring alarm limit of malfunction S1, rolling bearing
Outer ring alarm limit of malfunction S2, rolling bearing rolling element alarm limit of malfunction S3With rolling bearing retainer alarm limit of malfunction S4, monitoring is respectively
Bearing fault factor B at characteristic frequencyk(k=1,2,3,4)；Work as B1> S1When, determine rolling bearing inner ring failure；Work as B2> S2
When, determine housing washer failure；Work as B3> S3When, determine rolling bearing rolling element failure；Work as B4> S4When, determine to roll
Bearing retainer failure；
Step 8: extracting the mechanical oscillation fault signature due to caused by installation die misalignment；For the original vibration letter obtained
Number, the vibration amplitude at 1 times of speed-frequency, 2 times of speed-frequencies, 3 times of speed-frequencies and 4 times of speed-frequencies is extracted through FFT transform
Component Xi (t) (i=1,2,3,4), fault-signal caused by reconstruct installation die misalignment after being superimposed by inverse, setting centering is not
Good failure coefficient is P,
Die misalignment failure coefficient P is monitored, forecasts that motor installs die misalignment as P > 40%.
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| CN201910432079.8A CN110296095B (en) | 2019-05-21 | 2019-05-21 | Intelligent monitoring and diagnosing method for operation state of induced draft fan of power station boiler of thermal power plant |
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| CN113052716A (en) * | 2019-12-27 | 2021-06-29 | 新疆金风科技股份有限公司 | Abnormity early warning method and device for main bearing of wind generating set |
| CN113466752A (en) * | 2021-07-16 | 2021-10-01 | 江苏方天电力技术有限公司 | Method, system and device for diagnosing turn-to-turn short circuit fault of stator of synchronous phase modulator |
| CN113482945A (en) * | 2021-06-29 | 2021-10-08 | 中电华创电力技术研究有限公司 | Fan vibration fault diagnosis method and device based on vibration characteristic value |
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| CN113482945A (en) * | 2021-06-29 | 2021-10-08 | 中电华创电力技术研究有限公司 | Fan vibration fault diagnosis method and device based on vibration characteristic value |
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