CN111397877B - Rotary machine beat vibration fault detection and diagnosis method - Google Patents
Rotary machine beat vibration fault detection and diagnosis method Download PDFInfo
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- CN111397877B CN111397877B CN202010256575.5A CN202010256575A CN111397877B CN 111397877 B CN111397877 B CN 111397877B CN 202010256575 A CN202010256575 A CN 202010256575A CN 111397877 B CN111397877 B CN 111397877B
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- G01M13/00—Testing of machine parts
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- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
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- G01M13/00—Testing of machine parts
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Abstract
A rotary machine beat vibration fault detection and diagnosis method comprises the steps of firstly, carrying out bilateral continuation preprocessing on an axis vibration displacement signal at a fault response sensitive sensing point, and decomposing a fault signal into a plurality of eigenmode functions by using a VMD (virtual machine decomposition) method, so that the mode aliasing problem existing in the existing characteristic signal extraction method is solved; then, qualitatively detecting whether the eigenmode function with the center frequency closest to the rotor power frequency is a beat vibration signal by using a oscillogram; finally, Hilbert demodulation analysis is carried out on the resolved beat vibration signals, fault characteristic information is extracted by comprehensively adopting analysis tools such as a Hilbert demodulation spectrum and an instantaneous frequency spectrum, so that accurate beat vibration fault characteristic frequency is obtained, and the problem that the beat vibration fault characteristic frequency cannot be effectively identified due to too low frequency resolution is solved; the method well makes up the defects of detecting and diagnosing the beat vibration fault of the rotary machine by traditional methods such as a oscillogram, a spectrogram and the like, and is favorable for improving the accuracy of fault diagnosis.
Description
Technical Field
The invention belongs to the technical field of mechanical fault diagnosis, and particularly relates to a method for detecting and diagnosing a beat vibration fault of a rotary machine.
Background
The beat vibration fault is a special phenomenon in the rotating machinery, and is mainly characterized in that the envelope amplitude of the vibration waveform of the rotor slowly changes periodically along with time. In the rotating machinery equipment, when the frequency of a certain excitation component is close to the power frequency of the rotor, no matter whether the frequency is greater than or less than the power frequency, the beat phenomenon of the rotor can be caused, and therefore the beat phenomenon is easily induced. For example, in a dual-rotor aircraft engine, the inner and outer rotors often have different operating speeds, and when the rotating speeds of the two rotors are relatively close to each other, the engine may generate beat vibration; in a rotor system supported by a magnetic suspension bearing, if the rotating speed of a main shaft is closer to the frequency of a frequency converter or the natural frequency of the rotor system, the beat vibration phenomenon of the main shaft can be caused; in a common industrial turbine device such as a steam turbine and a compressor, the loosening of a rotating part of a rotor, fluid excitation and the like often induce such a failure. This unstable vibration behavior is extremely harmful to the healthy and smooth operation of the equipment, often resulting in equipment downtime and shortened equipment service life. During the operation of the equipment, it is necessary to monitor the vibration characteristics of the rotor in real time so as to detect and early warn the beat vibration fault symptoms of the rotor at an early stage.
At present, the common tools for detecting the rotor beat vibration phenomenon mainly include time domain oscillogram and frequency spectrogram. Although they can help to some extent identify rotor beat faults, they all have their own drawbacks. As in the time domain oscillogram, the characteristics of the beat signal are often overwhelmed by the rotor subharmonics, higher harmonics, and noise signals, and it is difficult for the diagnostic engineer to directly observe the characteristics of the beat signal from the time domain oscillogram. Although some adaptive signal decomposition methods such as EMD and EEMD can extract a plurality of eigenmode functions from the original signal, because they all have mode aliasing phenomena of different degrees, the beat signal is difficult to be extracted independently and completely as a single component. In addition, the observation of the beat phenomenon by the waveform alone is not sufficient to fully diagnose the root cause of the fault, and the beat fault signature frequency needs to be determined. In the process of spectrum analysis, when the sampling frequency is too large or the number of sampling points is too small, the frequency resolution is easily too low, two component components of the beat signal cannot be effectively separated in a spectrogram, and a diagnostic engineer may often mistake the beat signal as a characteristic component, thereby causing misdiagnosis or missed diagnosis. Therefore, how to more effectively detect and identify the beat vibration fault of the rotor is an urgent problem to be solved for improving the fault diagnosis efficiency and accuracy of the rotating machinery.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a beat vibration fault detection and diagnosis method for a rotary machine, which can effectively separate and detect beat vibration fault signals from original vibration displacement signals of a rotor, eliminate the interference of harmonic waves and noise signals and accurately identify the beat vibration fault characteristic frequency.
In order to achieve the purpose, the invention adopts the technical scheme that:
a beat vibration fault detection and diagnosis method for a rotary machine comprises the following steps:
1) selecting a rotor shaft vibration displacement signal x (t) at a fault response sensitive sensing point as an analysis object, and performing bilateral continuation preprocessing on the signal x (t);
2) decomposing the signal x (t) after the bilateral continuation preprocessing in the step 1) by using a VMD (spatial Mode decomposition) method to obtain a plurality of eigenmode functions u (t) corresponding to the signal x (t)k(t) and its center frequency fkK is 1,2, …, N denotes the number of eigenmode functions;
3) from the plurality of eigenmode functions u of step 2)k(t) selecting an eigenmode function with the center frequency closest to the rotor power frequency omega, and marking the eigenmode function as uj(t); observe uj(T) if the waveform envelope amplitude has a periodic variation rule and the variation period T isbIf u is more than or equal to 4/omega, then u is judgedj(t) continuing to execute step 4 for the beat vibration signal; otherwise, u is determinedj(t) if not, the beat vibration detection process is finished;
4) to beat vibration signal uj(t) performing spectral analysis, calculating the frequency resolution Δ f of the spectrum, and determining uj(t) waveform envelope amplitude variation frequency ωbRelation to Δ f; if ω isbU is greater than or equal to 4 delta fj(t) the spectrogram can express the beat vibration fault characteristic frequency omegafAnd the diagnosis is finished; otherwise, the beat vibration fault characteristic frequency omega is caused due to the fact that the frequency resolution is too lowfThe identification can not be effectively realized, and the step 5 needs to be continuously executed;
5) to beat vibration signal uj(t) carrying out Hilbert transformation to obtain a corresponding analytic signal, and then obtaining a Hilbert demodulation spectrum and an instantaneous frequency spectrum of the analytic signal; according to beat vibration signal uj(t) analyzing the characteristic frequency omega of beat vibration fault by the characteristics of Hilbert demodulation spectrum and instantaneous frequency spectrumfRelation with power frequency omega to obtain omegafThe exact value of (c).
The measurement mode of the rotor shaft vibration displacement signal in the step 1) is non-contact measurement, and the continuation preprocessing of the signal adopts a support vector machine method.
The VMD method in the step 2) needs to preset an eigenmode function u when being executedkThe number N of (t) is determined according to the spectrogram characteristics of the signal x (t).
The beat vibration signal u in the step 5)j(t) the frequency corresponding to the dominant component in the Hilbert demodulation spectrum is the frequency omega of the change of the waveform envelope amplitudebThe frequency is a beat signal ujThe difference in frequency between the two component components in (t), i.e. ωb＝|ω-ωf|；uj(t) the instantaneous frequency exhibits a periodic variation, under the condition that the power frequency component is the dominant component, if uj(t) when the maximum value of the instantaneous frequency of the sensor occurs at the beat valley and the minimum value of the instantaneous frequency of the sensor occurs at the beat peak, the beat vibration fault characteristic frequency omegaf<Omega; if u isj(t) when the maximum value of the instantaneous frequency appears at the beat peak and the minimum value appears at the beat valley, the beat vibration fault characteristic frequency omegaf>Omega; the beat signal ujAnd (t) providing a judgment basis for determining the beat vibration fault characteristic frequency by virtue of the characteristics of the Hilbert demodulation spectrum and the instantaneous frequency change.
The invention has the beneficial effects that:
according to the method, firstly, the VMD is utilized to decompose an original vibration signal into a plurality of eigenmode functions, so that the mode aliasing problem existing in the existing characteristic signal extraction methods such as EMD and EEMD is solved, the characteristics of the beat vibration signal can be more clearly expressed in the time domain, and the beat vibration fault can be better qualitatively detected by utilizing a oscillogram; and then Hilbert demodulation analysis is carried out on the separated beat vibration signals, and analysis tools such as Hilbert demodulation spectrums and instantaneous frequency spectrums are comprehensively utilized to extract fault information, so that the problem that the beat vibration fault characteristic frequency of the traditional spectrogram caused by too low frequency resolution cannot be effectively identified is solved.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
FIG. 2 is a schematic of an embodiment beat signal.
FIG. 3 is a waveform diagram of the simulation signal x (t) in the embodiment; the graph (b) is a spectrum diagram of the simulation signal x (t) in the embodiment.
FIG. 4 is a diagram of the VMD effect of the simulation signal x (t) in the embodiment.
FIG. 5 shows eigenmode function u in an example1Spectrum diagram of (a).
FIG. 6 is an example eigenmode function u1Hilbert demodulation spectrum of (1).
FIG. 7 shows eigenmode function u in an example1Hilbert instantaneous frequency spectrum.
Detailed Description
The invention is explained in more detail below with reference to the figures and examples.
In the embodiment, the effectiveness of the invention is verified by simulating the shaft vibration displacement simulation signal of the rotor beat vibration fault, the expression of the simulation signal is shown as the formula (1), and the simulation signal consists of a component x1(t)、x2(t)、x3(t), n (t), wherein: x is the number of1(t) represents the subsynchronous component of the rotor at a frequency of 15 Hz; x is the number of2(t) simulating a beat signal, as shown in FIG. 2, which is obtained by superimposing two vibration components, the first component being a vibration signal at 50Hz of power frequency ω, the second component being a vibration signal at 50Hz of power frequency ωThe component being the frequency omegafA fault signal of 48 Hz; x is the number of3(t) represents a double harmonic component of power frequency; n (t) represents a random noise interference signal with a standard deviation of 2;
x(t)＝x1(t)+x2(t)+x3(t)+n(t) (1)
wherein:
the simulation signal is subjected to discrete sampling, the sampling frequency of the simulation signal is set to be 1024Hz, the sampling time length of the simulation signal is set to be 1s, the waveform and the frequency spectrum of the simulation signal are shown in figure 3, and in a waveform diagram (a), due to the interference of noise and other harmonic components, an obvious beat vibration phenomenon is difficult to observe directly from a time domain waveform; whereas in spectrogram (b), the frequency resolution of the spectrogram is only 1Hz due to the limited number of sampled data points. The frequency of the fault component in the simulation signal is 48Hz, the frequency of the power frequency component is 50Hz, and the frequency values of the two components are very close to each other. Due to the fact that the frequency resolution is too low, the frequency resolution is easily mistaken for one frequency component, the real fault component is covered by the power frequency component, and the beat vibration fault cannot be accurately identified. The simulation signal is analyzed by the method of the invention, and the effectiveness of the simulation signal on beat vibration fault detection and diagnosis is verified.
Referring to fig. 1, a method for detecting and diagnosing a beat vibration fault of a rotary machine includes the following steps:
1) taking the simulation signal x (t) as an analysis object, and performing bilateral continuation preprocessing operation on the signal x (t) by using a support vector machine method;
2) performing spectrum analysis on the signal x (t), as shown in a graph (b) in fig. 3, wherein 3 obvious frequency components and noise components can be obviously observed from the spectrum graph; according to the spectrogram characteristics of the signal x (t), setting the number N of decomposition modes to be 4 and the penalty factor to be 5000, and then decomposing the signal x (t) after bilateral prolongation by using a VMD method to obtain u1、u2、u3、u4A total of 4 eigenMode function, as shown in FIG. 4: eigenmode function u1、u2、u3The corresponding center frequencies are 49.6Hz, 100.0Hz and 14.9Hz in sequence, and respectively correspond to the component x in the simulation signal2(t)、x3(t)、x1(t)，u4Corresponding to a random noise signal n (t); as can be seen from fig. 4, although the signal components are not arranged in sequence according to the frequency, the time domain waveforms of the signal components are relatively regular and clear, no obvious modal aliasing occurs, and all the 3 fundamental mode components contained in the signal x (t) can be well extracted;
3) in the simulation signal, the power frequency is set to 50Hz due to the eigenmode function u1The center frequency of the eigenmode function u is closest to the power frequency1Is a detection object; by observing u in FIG. 41Can see u1The waveform amplitude envelope has periodic and slow change and shows the characteristic of beat vibration phenomenon, and u is calculated by referring to a schematic diagram 2 of beat vibration signals1Waveform amplitude envelope variation period TbThe period of rotation of the rotor is 0.02s, so T is 0.5sbIf > 0.08s, then u is determined1Continuing to execute the step 4 for the beat vibration signal;
4) to beat vibration signal u1Performing a spectral analysis, as shown in fig. 5; the frequency resolution Deltaf in the spectrogram is still 1Hz, u1Waveform envelope amplitude variation frequency ωb＝1/Tb2Hz, due to ωb<4 delta f, the frequency resolution is too low, so that the beat vibration fault characteristic frequency cannot be effectively identified; it can be observed from fig. 5 that the eigenmode function u1The spectrogram of (a) seems to have only one significant frequency component, the frequency of which is 50Hz, and other fault components are not significant, and step 5 needs to be continuously performed;
5) to beat vibration signal u1Hilbert transformation is carried out to obtain corresponding analytic signals, and then Hilbert demodulation spectrums and instantaneous frequency spectrums of the analytic signals are obtained and are respectively shown in fig. 6 and fig. 7; in FIG. 6, a very distinct spectral peak exists at a frequency of 2Hz, with an amplitude of 9.566, indicating that the difference in frequency between the two component components of the beat signal is 2Hz, since the power frequency is known to be 50Hz, the fault characteristic frequency ωfOnly 48Hz or 52Hz is possible; while in FIG. 7, u1The instantaneous frequency of the fault is slowly changed with the period of 0.5s along with the time, the instantaneous frequency value reaches the maximum at the valley beating position, and the characteristic shows that the fault characteristic frequency omega is the dominant component on the premise that the power frequency component is the dominant componentfAnd finally determining that the beat vibration fault characteristic frequency is 48Hz when the beat vibration fault characteristic frequency is smaller than 50Hz of the power frequency, wherein the analysis conclusion is completely consistent with the theoretical assumption of the simulation signal. It follows that even in the case of too low frequency resolution, the diagnostic information provided by the Hilbert demodulation spectrum, instantaneous frequency spectrum, etc. of the beat signal can be used to determine its fault signature frequency.
Claims (3)
1. A beat vibration fault detection and diagnosis method for a rotary machine is characterized by comprising the following steps:
1) selecting a rotor shaft vibration displacement signal x (t) at a fault response sensitive sensing point as an analysis object, and performing bilateral continuation preprocessing on the signal x (t);
2) decomposing the signal x (t) after the bilateral continuation preprocessing in the step 1) by using a VMD (spatial Mode decomposition) method to obtain a plurality of eigenmode functions u (t) corresponding to the signal x (t)k(t) and its center frequency fkK is 1,2, …, N denotes the number of eigenmode functions;
3) from the plurality of eigenmode functions u of step 2)k(t) selecting an eigenmode function with the center frequency closest to the rotor power frequency omega, and marking the eigenmode function as uj(t); observe uj(T) if the waveform envelope amplitude has a periodic variation rule and the variation period T isbIf u is more than or equal to 4/omega, then u is judgedj(t) continuing to execute step 4) for beat vibration signals; otherwise, u is determinedj(t) if not, the beat vibration detection process is finished;
4) to beat vibration signal uj(t) performing spectral analysis, calculating the frequency resolution Δ f of the spectrum, and determining uj(t) waveform envelope amplitude variation frequency ωbRelation to Δ f; if ω isbU is greater than or equal to 4 delta fjFrequency of (t)Spectrogram capable of expressing beat vibration fault characteristic frequency omegafAnd the diagnosis is finished; otherwise, the beat vibration fault characteristic frequency omega is caused due to the fact that the frequency resolution is too lowfCan not be effectively identified, and the step 5) needs to be continuously executed;
5) to beat vibration signal uj(t) carrying out Hilbert transformation to obtain a corresponding analytic signal, and then obtaining a Hilbert demodulation spectrum and an instantaneous frequency spectrum of the analytic signal; according to beat vibration signal uj(t) analyzing the characteristic frequency omega of beat vibration fault by the characteristics of Hilbert demodulation spectrum and instantaneous frequency spectrumfRelation with power frequency omega to obtain omegafThe exact value of (c).
2. The method for detecting and diagnosing the beating fault of the rotary machine according to claim 1, wherein: the measurement mode of the rotor shaft vibration displacement signal in the step 1) is non-contact measurement, and the continuation preprocessing of the signal adopts a support vector machine method.
3. The method for detecting and diagnosing the beating fault of the rotary machine according to claim 1, wherein: the beat vibration signal u in the step 5)j(t) the frequency corresponding to the dominant component in the Hilbert demodulation spectrum is the frequency omega of the change of the waveform envelope amplitudebThe frequency is a beat signal ujThe difference in frequency between the two component components in (t), i.e. ωb＝|ω-ωf|；uj(t) the instantaneous frequency exhibits a periodic variation, under the condition that the power frequency component is the dominant component, if uj(t) when the maximum value of the instantaneous frequency of the sensor occurs at the beat valley and the minimum value of the instantaneous frequency of the sensor occurs at the beat peak, the beat vibration fault characteristic frequency omegaf<Omega; if u isj(t) when the maximum value of the instantaneous frequency appears at the beat peak and the minimum value appears at the beat valley, the beat vibration fault characteristic frequency omegaf>Omega; the beat signal ujAnd (t) providing a judgment basis for determining the beat vibration fault characteristic frequency by virtue of the characteristics of the Hilbert demodulation spectrum and the instantaneous frequency change.
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