CN112051064A - Method and system for extracting fault characteristic frequency of rotary mechanical equipment - Google Patents
Method and system for extracting fault characteristic frequency of rotary mechanical equipment Download PDFInfo
- Publication number
- CN112051064A CN112051064A CN202010929716.5A CN202010929716A CN112051064A CN 112051064 A CN112051064 A CN 112051064A CN 202010929716 A CN202010929716 A CN 202010929716A CN 112051064 A CN112051064 A CN 112051064A
- Authority
- CN
- China
- Prior art keywords
- fault
- frequency
- signal
- frequency band
- energy
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
- G01M13/04—Bearings
- G01M13/045—Acoustic or vibration analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
Landscapes
- Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
- General Physics & Mathematics (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention relates to a method and a system for extracting fault characteristic frequency of rotary mechanical equipment, wherein the method comprises the following steps: dividing the acquired vibration signal of the rotating machine into a plurality of fault frequency band data through a band-pass filter; transient energy tracking is carried out on the vibration signals of different fault frequency bands by using a Teager energy operator, fault frequency band data with an energy value lower than an average energy value are removed, and fault frequency band data with an energy value higher than the average energy value are reserved; screening the reserved fault frequency bands by using an improved autocorrelation method, and selecting the optimal fault frequency band; and carrying out square envelope spectrum analysis on the optimal fault frequency band, extracting fault characteristic frequency and realizing fault diagnosis. The invention can realize the accurate extraction of the bearing fault characteristic frequency, and further research and analyze the fault of the bearing.
Description
Technical Field
The invention relates to the technical field of mechanical equipment fault diagnosis, in particular to a method and a system for extracting fault characteristic frequency of rotary mechanical equipment based on an optimal fault frequency band tracked by circulating transient energy.
Background
Bearings, gears, etc. as part of a rotating machine, if damage or defects occur, they can directly affect the stable operation of the equipment and even cause damage to the entire equipment. The key technology of fault diagnosis is to extract fault features from signals. Due to the influence of background noise and randomness in selecting a signal to be analyzed, the fault feature extraction method is difficult to implement in practical application and has low accuracy.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a method and a system for extracting a fault characteristic frequency of a rotating mechanical device based on a cyclic transient energy tracking optimal fault frequency band, which can accurately extract a fault characteristic frequency of a bearing, and further study and analyze a fault of the bearing.
In order to achieve the purpose, the invention adopts the following technical scheme: a method for extracting fault characteristic frequency of rotating mechanical equipment comprises the following steps: s1, dividing the collected vibration signals of the rotating machine into a plurality of fault frequency band data through a band-pass filter; s2, transient energy tracking is carried out on the vibration signals of different fault frequency bands by using a Teager energy operator, fault frequency band data with energy values lower than the average energy value are removed, and fault frequency band data with energy values higher than the average energy value are reserved; s3, screening the reserved fault frequency bands by using an improved autocorrelation method, and selecting the optimal fault frequency band; and S4, performing square envelope spectrum analysis on the optimal fault frequency band, extracting fault characteristic frequency and realizing fault diagnosis.
Further, the band-pass filter adopts a Chebyshev I-type filter.
Further, the center frequency of the band-pass filter is set as the frequency conversion and the frequency multiplication thereof, and the left boundary and the right boundary of the pass band are m frequency multiplication of the fault characteristic frequency; the left boundary and the right boundary of the passband attenuation cutoff are k frequency multiplication of fault characteristic frequency, m is a positive integer, k is a positive integer, and k is m + 1.
Further, the method for transient energy tracking of vibration signals of different fault frequency bands by the Teager energy operator comprises the following steps:
the Teager energy operator ψ (x (t)) of the vibration signal x (t) at time t is:
ψ(x(t))＝(x′(t))2-x(t)x″(t)
in the formula, x '(t) and x' (t) are first order differential and second order differential of the vibration signal x (t) to time t at time t;
since the vibration signal x (t) collected in practice is the discrete signal x (n), the differential needs to be replaced by the difference in the Teager energy operator, and the Teager energy operator ψ (x (n)) of the discrete signal x (n) is:
ψ(x(n))＝x2(n)-x(n-1)x(n+1)
and (3) carrying out nonlinear operator operation on x (n) to obtain:
ψ(x(n))＝a2(n)sin2(w(n)) (1)
wherein a (n) is the amplitude of the discrete signal x (n), and w (n) is the frequency of the discrete signal x (n);
after the energy operator ψ (y (n)) is performed on the backward difference signal y (n) ═ x (n) — x (n-1), the amplitude a (n) and frequency w (n) approximate values of the discrete signal are obtained:
and (3) demodulating all fault frequency band data by using a Teager energy operator by using a formula (1) to obtain a corresponding time domain signal psi (t), and respectively calculating the energy value of each group of time domain signals and the average energy value of all fault frequency bands.
Further, the energy value W is:
further, the method for screening the optimal fault frequency band by adopting the improved autocorrelation method comprises the following steps:
s31, calculating the number of sampling points corresponding to the frequency conversion according to the sampling frequency of the vibration signal;
s32, demodulating the filtered signal to obtain the time domain discrete signal psiaThe corresponding data points are shifted backwards by a plurality of points to generate a new group of time domain discrete signals psibAnd the number of the data points is the same as that of the original data points; wherein, a plurality of points are sampling points corresponding to the frequency conversion;
s33, calculating by using the improved autocorrelation function to obtain correlation coefficients of two groups of time domain discrete signals;
and S34, finding a group of data with the maximum correlation coefficient, namely the optimal fault frequency band.
Further, in the step S33, the autocorrelation function R of the modified time-domain signal ψ (t)ψ(τ) is:
wherein "+ is a convolution operator; τ is a time interval; psi (t) is a time domain signal; psi*(τ) is the conjugate of ψ (τ);
calculating the autocorrelation function R of the two sets of data according to the formula (2)aAnd RbTwo groups of autocorrelation function sequences are generated, and then correlation coefficients of the two groups of sequences are solved.
Further, the square envelope spectrum analysis method comprises the following steps:
s41, hilbert transforming the t-time vibration signal x (t):
wherein pi is a circumference ratio;
s42, structure analysis signal z (t), and conjugate complex analysis signal z (t)':
z(t)＝x(t)+jx(t)，
z(t)′＝x(t)-jx(t)′；
s43, obtaining a squared envelope signal y (t) by multiplying the analytic signal and the conjugate analytic signal to reconstruct a new synthesized signal:
y(t)＝z(t)z(t)′；
and S44, carrying out Fourier transform on the square envelope signal to obtain a square envelope spectrum, and directly reading the fault characteristic frequency and frequency doubling components thereof in the spectrogram.
A rotating machinery equipment fault signature frequency extraction system, comprising: the system comprises a fault frequency band dividing module, a transient energy tracking module, an optimal fault frequency band selecting module and a fault characteristic frequency extracting module;
the fault frequency band dividing module divides the acquired vibration signals of the rotating machinery into a plurality of fault frequency band data through a band-pass filter;
the transient energy tracking module adopts a Teager energy operator to perform transient energy tracking on vibration signals of different fault frequency bands, removes fault frequency band data with an energy value lower than an average energy value, and reserves fault frequency band data with an energy value higher than the average energy value;
the optimal fault frequency band selection module screens reserved fault frequency bands by using an improved autocorrelation method and selects an optimal fault frequency band;
the fault characteristic frequency extraction module carries out square envelope spectrum analysis on the optimal fault frequency band, extracts fault characteristic frequency and realizes fault diagnosis.
Further, in the transient energy tracking module, the method for transient energy tracking of the Teager energy operator on the vibration signals of different fault frequency bands includes:
the Teager energy operator ψ (x (t)) of the vibration signal x (t) at time t is:
ψ(x(t))＝(x′(t))2-x(t)x″(t)
in the formula, x '(t) and x' (t) are first order differential and second order differential of the vibration signal x (t) to time t at time t;
since the vibration signal x (t) collected in practice is the discrete signal x (n), the differential needs to be replaced by the difference in the Teager energy operator, and the Teager energy operator ψ (x (n)) of the discrete signal x (n) is:
ψ(x(n))＝x2(n)-x(n-1)x(n+1)
and (3) carrying out nonlinear operator operation on x (n) to obtain:
ψ(x(n))＝a2(n)sin2(w(n)) (1)
wherein a (n) is the amplitude of the discrete signal x (n), and w (n) is the frequency of the discrete signal x (n);
after the energy operator ψ (y (n)) is performed on the backward difference signal y (n) ═ x (n) — x (n-1), the amplitude a (n) and frequency w (n) approximate values of the discrete signal are obtained:
and (3) demodulating all fault frequency band data by using a Teager energy operator by using a formula (1) to obtain a corresponding time domain signal psi (t), and respectively calculating the energy value of each group of time domain signals and the average energy value of all fault frequency bands.
Due to the adoption of the technical scheme, the invention has the following advantages: 1. the invention adopts a circulating transient energy tracking method to search for the optimal fault frequency band, can determine the frequency band with the most concentrated fault energy distribution, solves the problem of randomness when selecting the signals to be analyzed, reduces the processing range of vibration data, extracts more obvious fault characteristics and improves the effect of extracting the fault characteristics. 2. The invention analyzes the optimal fault frequency band by using a square envelope spectrum method, can separate the fault characteristic frequency from a plurality of frequency components, realizes the decoupling of the fault characteristic frequency, and can more clearly extract the fault characteristic frequency of the signal.
Drawings
FIG. 1 is a schematic overall flow diagram of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and examples.
As shown in fig. 1, the present invention provides a method for extracting a fault characteristic frequency of a rotating mechanical device based on a cyclic transient energy tracking optimal fault frequency band, which includes the following steps:
s1, dividing the collected vibration signals of the rotating machine into a plurality of fault frequency band data through a band-pass filter;
in the present embodiment, the band pass filter is a chebyshev type I filter;
for example, the sampling frequency is 12000Hz, the fault characteristic frequency is 161.5Hz, and the conversion frequency is 30 Hz. And carrying out fault frequency band division on the vibration signal by adopting a Chebyshev I-type filter.
Mathematical model G of Chebyshev type I filtern(ω) is:
in the formula, TnIs an n-order chebyshev polynomial:
wherein, the degree of amplitude fluctuation in the pass band is represented, and | is less than or equal to 1; omega0Is the cut-off frequency of the pass-band; omega is the starting frequency of the passband; n is the order of the filter, a0、a1、a2、anIs a coefficient of each order.
In this embodiment, the center frequency of the band pass filter is set to the rotation frequency and the multiple thereof. The left and right boundaries of the pass band are m multiples of the characteristic frequency of the fault (m is a positive integer and may take the value 2, for example 161.5 × 2 ═ 323 Hz). The pass band attenuation cutoff left and right boundaries are k multiples of the fault signature frequency, (k is a positive integer, and k is m +1, here the value 3, i.e., 484.5 Hz).
In consideration of the stopband attenuation of the filter, in the present embodiment, the fault frequency bands of the vibration signal are divided into 162 groups by the band pass filter, i.e., the pass band from the center frequency of 20 times the frequency of the.
S2, transient energy tracking is carried out on the vibration signals of different fault frequency bands by using a Teager energy operator, so that fault frequency band data with energy values lower than the average energy value are removed, and fault frequency band data with energy values higher than the average energy value are reserved;
the Teager energy operator can track the signal transient energy, and transient energy changes are affected by noise and fault frequency impact. The periodic occurrence of the fault pulse is embodied as the change of the cyclic transient energy, and the pulse noise occurs randomly without periodicity, so the change of the cyclic transient impact energy is identified by adopting a Teager energy operator.
the Teager energy operator ψ (x (t)) of the vibration signal x (t) at time t is:
ψ(x(t))＝(x′(t))2-x(t)x″(t) (3)
in the formula, x' (t) and x ″ (t) are first order and second order differentials of the vibration signal x (t) with respect to time t at time t.
Since the actually acquired vibration signal x (t) is the discrete signal x (n), the difference needs to be used in the Teager energy operator instead of the differential, that is, the Teager energy operator ψ (x (n)) of the discrete signal x (n) is:
ψ(x(n))＝x2(n)-x(n-1)x(n+1) (4)
since the modulation signal changes slowly with respect to the carrier signal, ψ (a) (n) is 0,
performing a non-linear operator operation on x (n) to obtain:
ψ(x(n))＝a2(n)sin2(w(n)) (5)
wherein a (n) is the amplitude of the discrete signal x (n), and w (n) is the frequency of the discrete signal x (n);
after the energy operator ψ (y (n)) is performed on the backward difference signal y (n) ═ x (n) — x (n-1), the amplitude a (n) and frequency w (n) of the discrete signal can be found as an approximate value:
the Teager energy operator demodulation is carried out on all fault frequency band (for example, 162 sets of fault frequency bands) data obtained by using the band-pass filter by using a formula (5) to obtain 162 sets of time domain signals ψ (t), and energy values of each set of time domain signals and average energy values of all fault frequency bands are respectively calculated. Fault frequency bands (e.g., 81 sets of fault frequency bands) are selected in which the energy value is higher than the average energy value.
Wherein the energy value W is:
s3, screening the reserved fault frequency bands by using an improved autocorrelation method, and selecting the optimal fault frequency band, wherein the fault characteristic component of the optimal fault frequency band is most obvious.
Due to the periodic occurrence of fault impact, an improved autocorrelation method is adopted to screen the optimal fault frequency band, and the screening method comprises the following steps:
and S31, calculating the number of sampling points corresponding to the conversion frequency according to the sampling frequency of the vibration signal.
S32, demodulating the filtered signal to obtain the time domain discrete signal psiaShifting back the corresponding data points by a plurality of points (the points are the sampling points corresponding to the frequency conversion), and generating a new group of time domain discrete signals psibAnd is the same as the number of original data points.
S33, calculating a correlation coefficient r of two groups of time domain discrete signals:
improved autocorrelation function R of time-domain signal psi (t)ψ(τ) is:
wherein "+ is a convolution operator; τ is a time interval; psi (t) is a time domain signal; psi*(τ) is the conjugate of ψ (τ).
Calculating the autocorrelation function R of the two sets of data according to equation (9)aAnd RbAnd generating two groups of autocorrelation function sequences, and finally solving the correlation coefficient r of the two groups of sequences.
Correlation coefficient r:
wherein, Cov (R)a,Rb) Is RaAnd RbOf (1) covariance, Var [ R ]a]Is RaVariance of (1), Var [ R ]b]Is RbThe variance of (c).
And S34, finding a group of data with the maximum correlation coefficient, namely the optimal fault frequency band.
S4, performing square envelope spectrum analysis on the optimal fault frequency band, extracting fault characteristic frequency and realizing fault diagnosis;
the square envelope spectrum analysis can inhibit noise, highlight the fault characteristic frequency gathered at a low frequency band, and better diagnose the fault.
The square envelope spectrum analysis method comprises the following steps:
s41, hilbert transforming the t-time vibration signal x (t):
wherein pi is the circumference ratio.
S42, structure analysis signal z (t), and conjugate complex analysis signal z (t)':
z(t)＝x(t)+jx(t)， (8)
z(t)′＝x(t)-jx(t)′ ； (9)
s43, obtaining a squared envelope signal y (t) by multiplying the analytic signal and the conjugate analytic signal to reconstruct a new synthesized signal:
y(t)＝z(t)z(t)′ (10)
and S44, carrying out Fourier transform on the square envelope signal to obtain a square envelope spectrum, and directly reading the fault characteristic frequency and frequency multiplication components thereof in the spectrogram.
The invention also provides a system for extracting the fault characteristic frequency of the rotary mechanical equipment, which comprises a fault frequency band dividing module, a transient energy tracking module, an optimal fault frequency band selecting module and a fault characteristic frequency extracting module;
the fault frequency band dividing module divides the acquired vibration signals of the rotating machinery into a plurality of fault frequency band data through a band-pass filter;
the transient energy tracking module adopts a Teager energy operator to perform transient energy tracking on vibration signals of different fault frequency bands, removes fault frequency band data with an energy value lower than an average energy value, and retains fault frequency band data with an energy value higher than the average energy value;
the optimal fault frequency band selection module screens the reserved fault frequency bands by using an improved autocorrelation method and selects the optimal fault frequency bands;
and the fault characteristic frequency extraction module performs square envelope spectrum analysis on the optimal fault frequency band, extracts fault characteristic frequency and realizes fault diagnosis.
In the above embodiment, in the transient energy tracking module, the method for transient energy tracking of the Teager energy operator on the vibration signals of different fault frequency bands includes:
the Teager energy operator ψ (x (t)) of the vibration signal x (t) at time t is:
ψ(x(t))＝(x′(t))2-x(t)x″(t)
in the formula, x '(t) and x' (t) are first order differential and second order differential of the vibration signal x (t) to time t at time t;
since the vibration signal x (t) collected in practice is the discrete signal x (n), the differential needs to be replaced by the difference in the Teager energy operator, and the Teager energy operator ψ (x (n)) of the discrete signal x (n) is:
ψ(x(n))＝x2(n)-x(n-1)x(n+1)
and (3) carrying out nonlinear operator operation on x (n) to obtain:
ψ(x(n))＝a2(n)sin2(w(n))
wherein a (n) is the amplitude of the discrete signal x (n), and w (n) is the frequency of the discrete signal x (n);
after the energy operator ψ (y (n)) is performed on the backward difference signal y (n) ═ x (n) — x (n-1), the amplitude a (n) and frequency w (n) approximate values of the discrete signal are obtained:
all fault frequency band data are expressed by the formula psi (x (n) ═ a2(n)sin2(w (n)) demodulating the Teager energy operator to obtain a corresponding time domain signal psi (t), and respectively calculating the energy value of each group of time domain signals and the average energy value of all fault frequency bands.
In summary, the invention adopts the band-pass filter to divide the fault vibration signal into different fault frequency band data, screens the fault frequency band by using the cyclic transient energy tracking, selects the optimal fault frequency band, and extracts the fault characteristics by performing the square envelope spectrum on the optimal fault frequency band. The Teager energy operator method can track the cycle transient energy of the signal, and the influence of non-fault factors can be eliminated due to the periodic occurrence of fault pulses. Based on the periodicity of the impact characteristics, the selection of the optimal fault frequency band can be realized by improving the autocorrelation coefficient method. The square envelope spectrum can extract the fault characteristic frequency of the signal, so that fault diagnosis of the rolling bearing is realized.
The above embodiments are only for illustrating the present invention, and the steps may be changed, and on the basis of the technical solution of the present invention, the modification and equivalent changes of the individual steps according to the principle of the present invention should not be excluded from the protection scope of the present invention.
Claims (10)
1. A method for extracting fault characteristic frequency of rotating mechanical equipment is characterized by comprising the following steps:
s1, dividing the collected vibration signals of the rotating machine into a plurality of fault frequency band data through a band-pass filter;
s2, transient energy tracking is carried out on the vibration signals of different fault frequency bands by using a Teager energy operator, fault frequency band data with energy values lower than the average energy value are removed, and fault frequency band data with energy values higher than the average energy value are reserved;
s3, screening the reserved fault frequency bands by using an improved autocorrelation method, and selecting the optimal fault frequency band;
and S4, performing square envelope spectrum analysis on the optimal fault frequency band, extracting fault characteristic frequency and realizing fault diagnosis.
2. The fault signature frequency extraction method of claim 1, wherein: the band-pass filter adopts a Chebyshev I-type filter.
3. The fault signature frequency extraction method of claim 1, wherein: the center frequency of the band-pass filter is set as the frequency conversion and the frequency multiplication, and the left boundary and the right boundary of the pass band are m frequency multiplication of the fault characteristic frequency; the left boundary and the right boundary of the passband attenuation cutoff are k frequency multiplication of fault characteristic frequency, m is a positive integer, k is a positive integer, and k is m + 1.
4. The method for extracting the fault characteristic frequency according to claim 1, wherein the method for transient energy tracking of the vibration signals of different fault frequency bands by the Teager energy operator comprises the following steps:
the Teager energy operator ψ (x (t)) of the vibration signal x (t) at time t is:
ψ(x(t))＝(x′(t))2-x(t)x″(t)
in the formula, x '(t) and x' (t) are first order differential and second order differential of the vibration signal x (t) to time t at time t;
since the vibration signal x (t) collected in practice is the discrete signal x (n), the differential needs to be replaced by the difference in the Teager energy operator, and the Teager energy operator ψ (x (n)) of the discrete signal x (n) is:
ψ(x(n))＝x2(n)-x(n-1)x(n+1)
and (3) carrying out nonlinear operator operation on x (n) to obtain:
ψ(x(n))＝a2(n)sin2(w(n)) (1)
wherein a (n) is the amplitude of the discrete signal x (n), and w (n) is the frequency of the discrete signal x (n);
after the energy operator ψ (y (n)) is performed on the backward difference signal y (n) ═ x (n) — x (n-1), the amplitude a (n) and frequency w (n) approximate values of the discrete signal are obtained:
and (3) demodulating all fault frequency band data by using a Teager energy operator by using a formula (1) to obtain a corresponding time domain signal psi (t), and respectively calculating the energy value of each group of time domain signals and the average energy value of all fault frequency bands.
5. The fault signature frequency extraction method of claim 4, wherein the energy value W is:
6. the fault signature frequency extraction method of claim 1, wherein: the method for screening the optimal fault frequency band by adopting the improved autocorrelation method comprises the following steps:
s31, calculating the number of sampling points corresponding to the frequency conversion according to the sampling frequency of the vibration signal;
s32, demodulating the filtered signal to obtain the time domain discrete signal psiaThe corresponding data points are shifted backwards by a plurality of points to generate a new group of time domain discrete signals psibAnd the number of the data points is the same as that of the original data points; wherein, a plurality of points are sampling points corresponding to the frequency conversion;
s33, calculating by using the improved autocorrelation function to obtain correlation coefficients of two groups of time domain discrete signals;
and S34, finding a group of data with the maximum correlation coefficient, namely the optimal fault frequency band.
7. The fault signature frequency extraction method of claim 6, wherein: in the step S33, the autocorrelation function R of the modified time-domain signal ψ (t)ψ(τ) is:
wherein "+ is a convolution operator; τ is a time interval; psi (t) is a time domain signal; psi*(τ) is the conjugate of ψ (τ);
calculating the autocorrelation function R of the two sets of data according to the formula (2)aAnd RbGenerating two groups of autocorrelation function sequences, and calculating the phases of the two groups of sequencesAnd (4) a correlation coefficient.
8. The fault signature frequency extraction method of claim 1, wherein the squared envelope spectrum analysis method comprises the steps of:
s41, hilbert transforming the t-time vibration signal x (t):
wherein pi is a circumference ratio;
s42, structure analysis signal z (t), and conjugate complex analysis signal z (t)':
z(t)＝x(t)+jx(t)，
z(t)′＝x(t)-jx(t)′；
s43, obtaining a squared envelope signal y (t) by multiplying the analytic signal and the conjugate analytic signal to reconstruct a new synthesized signal:
y(t)＝z(t)z(t)′；
and S44, carrying out Fourier transform on the square envelope signal to obtain a square envelope spectrum, and directly reading the fault characteristic frequency and frequency doubling components thereof in the spectrogram.
9. A rotating machinery equipment fault characteristic frequency extraction system is characterized by comprising: the system comprises a fault frequency band dividing module, a transient energy tracking module, an optimal fault frequency band selecting module and a fault characteristic frequency extracting module;
the fault frequency band dividing module divides the acquired vibration signals of the rotating machinery into a plurality of fault frequency band data through a band-pass filter;
the transient energy tracking module adopts a Teager energy operator to perform transient energy tracking on vibration signals of different fault frequency bands, removes fault frequency band data with an energy value lower than an average energy value, and reserves fault frequency band data with an energy value higher than the average energy value;
the optimal fault frequency band selection module screens reserved fault frequency bands by using an improved autocorrelation method and selects an optimal fault frequency band;
the fault characteristic frequency extraction module carries out square envelope spectrum analysis on the optimal fault frequency band, extracts fault characteristic frequency and realizes fault diagnosis.
10. The system of claim 9, wherein: in the transient energy tracking module, the method for transient energy tracking of vibration signals of different fault frequency bands by the Teager energy operator comprises the following steps:
the Teager energy operator ψ (x (t)) of the vibration signal x (t) at time t is:
ψ(x(t))＝(x′(t))2-x(t)x″(t)
in the formula, x '(t) and x' (t) are first order differential and second order differential of the vibration signal x (t) to time t at time t;
since the vibration signal x (t) collected in practice is the discrete signal x (n), the differential needs to be replaced by the difference in the Teager energy operator, and the Teager energy operator ψ (x (n)) of the discrete signal x (n) is:
ψ(x(n))＝x2(n)-x(n-1)x(n+1)
and (3) carrying out nonlinear operator operation on x (n) to obtain:
ψ(x(n))＝a2(n)sin2(w(n)) (1)
wherein a (n) is the amplitude of the discrete signal x (n), and w (n) is the frequency of the discrete signal x (n);
after the energy operator ψ (y (n)) is performed on the backward difference signal y (n) ═ x (n) — x (n-1), the amplitude a (n) and frequency w (n) approximate values of the discrete signal are obtained:
and (3) demodulating all fault frequency band data by using a Teager energy operator by using a formula (1) to obtain a corresponding time domain signal psi (t), and respectively calculating the energy value of each group of time domain signals and the average energy value of all fault frequency bands.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2020103134751 | 2020-04-20 | ||
| CN202010313475 | 2020-04-20 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112051064A true CN112051064A (en) | 2020-12-08 |
| CN112051064B CN112051064B (en) | 2022-08-16 |
Family
ID=73610336
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010929716.5A Active CN112051064B (en) | 2020-04-20 | 2020-09-07 | Method and system for extracting fault characteristic frequency of rotary mechanical equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112051064B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2002038916A2 (en) * | 2000-11-07 | 2002-05-16 | Halliburton Energy Services, Inc. | An apparatus and method for determining downhole bit failure |
| US20110099007A1 (en) * | 2009-10-22 | 2011-04-28 | Broadcom Corporation | Noise estimation using an adaptive smoothing factor based on a teager energy ratio in a multi-channel noise suppression system |
| CN102129858A (en) * | 2011-03-16 | 2011-07-20 | 天津大学 | Musical note segmenting method based on Teager energy entropy |
| CN102661783A (en) * | 2012-04-24 | 2012-09-12 | 北京信息科技大学 | Characteristic extracting method for prediction of rotating mechanical failure trend |
| CN102788695A (en) * | 2012-07-18 | 2012-11-21 | 南京航空航天大学 | Identification method of rolling bearing abrasion |
| CN108152037A (en) * | 2017-11-09 | 2018-06-12 | 同济大学 | Method for Bearing Fault Diagnosis based on ITD and improvement shape filtering |
| CN109765055A (en) * | 2019-01-31 | 2019-05-17 | 杭州安脉盛智能技术有限公司 | Rolling bearing fault testing method and system based on EWT, spectrum virtual value and KNN |
-
2020
- 2020-09-07 CN CN202010929716.5A patent/CN112051064B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2002038916A2 (en) * | 2000-11-07 | 2002-05-16 | Halliburton Energy Services, Inc. | An apparatus and method for determining downhole bit failure |
| US20110099007A1 (en) * | 2009-10-22 | 2011-04-28 | Broadcom Corporation | Noise estimation using an adaptive smoothing factor based on a teager energy ratio in a multi-channel noise suppression system |
| CN102129858A (en) * | 2011-03-16 | 2011-07-20 | 天津大学 | Musical note segmenting method based on Teager energy entropy |
| CN102661783A (en) * | 2012-04-24 | 2012-09-12 | 北京信息科技大学 | Characteristic extracting method for prediction of rotating mechanical failure trend |
| CN102788695A (en) * | 2012-07-18 | 2012-11-21 | 南京航空航天大学 | Identification method of rolling bearing abrasion |
| CN108152037A (en) * | 2017-11-09 | 2018-06-12 | 同济大学 | Method for Bearing Fault Diagnosis based on ITD and improvement shape filtering |
| CN109765055A (en) * | 2019-01-31 | 2019-05-17 | 杭州安脉盛智能技术有限公司 | Rolling bearing fault testing method and system based on EWT, spectrum virtual value and KNN |
Non-Patent Citations (4)
| Title |
|---|
| JIANG ZHANGLEI: "Research on quantification method of fault deterioration based on 1.5-spectrum feature extraction method", 《IEEE》 * |
| 刘建春等: "基于谱峭度和Teager能量算子的轴承故障特征增强", 《噪声与振动控制》 * |
| 刘晶: "基于能量算子的滚动轴承故障诊断研究", 《中国优秀博硕士学位论文全文数据库(硕士)工程科技Ⅱ辑》 * |
| 夏均忠等: "基于Teager能量算子和ZFFT的滚动轴承故障特征提取", 《振动与冲击》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112051064B (en) | 2022-08-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lu et al. | A review of stochastic resonance in rotating machine fault detection | |
| Wang et al. | Subband averaging kurtogram with dual-tree complex wavelet packet transform for rotating machinery fault diagnosis | |
| Li et al. | Non-stationary vibration feature extraction method based on sparse decomposition and order tracking for gearbox fault diagnosis | |
| Li et al. | Diagonal slice spectrum assisted optimal scale morphological filter for rolling element bearing fault diagnosis | |
| Wang et al. | Rolling element bearing fault diagnosis via fault characteristic order (FCO) analysis | |
| Li et al. | An enhanced rolling bearing fault detection method combining sparse code shrinkage denoising with fast spectral correlation | |
| CN108151869B (en) | Mechanical vibration characteristic index extraction method, system and device | |
| Chen et al. | A performance enhanced time-varying morphological filtering method for bearing fault diagnosis | |
| CN104375111B (en) | The method that quick high accuracy refinement correction is carried out to intensive spectrum | |
| CN110046476B (en) | Ternary binary fractal wavelet sparse diagnosis method for rolling bearing faults | |
| CN103454495B (en) | Self-adaptation high-precision fast spectral analytical approach | |
| CN105092241A (en) | Method and system for diagnosing local fault of gear | |
| CN110084208B (en) | Calculation order tracking method capable of adaptively reducing noise and avoiding order aliasing | |
| CN108731945B (en) | Method for extracting fault signal characteristic information of aircraft engine rotor system | |
| CN103499445A (en) | Time-frequency slice analysis-based rolling bearing fault diagnosis method | |
| CN104006962A (en) | Gear fault feature extraction method and system | |
| JP2000505196A (en) | Dynamic atypical clock for resampling and machine signal processing | |
| CN111896260B (en) | NGAs synchronous optimization wavelet filter and MCKD bearing fault diagnosis method | |
| CN110806315A (en) | Gearbox composite fault diagnosis method based on inversion editing | |
| Duan et al. | Time-varying filtering for nonstationary signal analysis of rotating machinery: Principle and applications | |
| Yu et al. | Adaptive multiple second-order synchrosqueezing wavelet transform and its application in wind turbine gearbox fault diagnosis | |
| Zhao et al. | Vold-Kalman generalized demodulation for multi-faults detection of gear and bearing under variable speeds | |
| CN112051064B (en) | Method and system for extracting fault characteristic frequency of rotary mechanical equipment | |
| CN117571316A (en) | Composite fault diagnosis method and system | |
| CN113899548A (en) | Rolling bearing fault diagnosis method based on harmonic kurtosis spectrum |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |