CN109682600A - A kind of improvement variation mode decomposition diagnostic method for Main Shaft Bearing of Engine fault diagnosis - Google Patents
A kind of improvement variation mode decomposition diagnostic method for Main Shaft Bearing of Engine fault diagnosis Download PDFInfo
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Abstract
The invention discloses a kind of improvement variation mode decomposition diagnostic methods for Main Shaft Bearing of Engine fault diagnosis.Failure original signal is inputted into intrinsic time Scale Decomposition first, signal is broken down into several intrinsic rotational components and a discrepance.Discrepance is filtered out, the key component of completely stick signal while carrying out de-noising to original signal.Secondly, each intrinsic rotational component is done variation mode decomposition, the best component in every group of IMFs is selected respectively and is reconstructed according to kurtosis principle.Finally, reconstruction signal, which is done Hilbert envelope transformation, is diagnosed to be bearing fault type.One aspect of the present invention, to signal noise silencing, improves signal-to-noise ratio using intrinsic time Scale Decomposition；On the other hand, adaptively each intrinsic rotational component is decomposed near respective centre frequency using variation mode decomposition, selects optimal component reconstruction signal.The method has good de-noising ability and complete retention fault information, has stronger fault diagnosis advantage.
Description
Technical field
The invention belongs to automobile failure diagnosis field, in particular to a kind of changing for Main Shaft Bearing of Engine fault diagnosis
Into variation mode decomposition diagnostic method.
Background technique
Engine is referred to as the heart of automobile, every operating index of performance overall effect automobile.It is used in engine
Numerous bearings, for transmission system provide support.Wherein main shaft bearing is the key components and parts of engine, and reliability is to automobile
The comfortableness and security of driving are most important.With the raising of engine performance, the operating condition of main shaft bearing is more and more tighter
It is severe, therefore the probability to break down is also bigger.According to statistics, main shaft bearing be engine breakdown maximum probability component it
One, so being of great importance to the diagnosis of Main Shaft Bearing of Engine failure.
Main Shaft Bearing of Engine failure would generally cause the Local Transient in coupled vibration signal to be impacted.However, in automobile
In operational process, load, friction, the change of speed and work noise, interference of ambient noise etc. typically result in vibration letter
Number have strong unstable state nonlinear characteristic, cyclic breakdown impact is difficult to extract.Wigner-Ville distribution, wavelet analysis etc. are
The effective means of non-stationary signal is handled in modern signal processing method.But for multicomponent data processing, Wigner-Ville divides
There are serious cross jammings for cloth, hinder effective analysis to signal.Wavelet analysis is based on Fourier transformation, window function office
It is sex-limited to be difficult to break through, it can not accurately describe frequency and change with time.Therefore, reliable bearing fault characteristics extracting method is
Mechanical fault diagnosis field needs primary study and overcomes the problems, such as.
Summary of the invention
The purpose of the invention is to overcome shortcoming and defect of the existing technology, and provide a kind of for mobilizing owner
The improvement variation mode decomposition diagnostic method of axle bearing fault diagnosis.This method has good de-noising ability and complete reservation event
Hinder information, there is stronger fault diagnosis advantage.
To achieve the above object, the technical scheme is that including:
S1, Main Shaft Bearing of Engine failure original signal is decomposed using intrinsic time Scale Decomposition, signal is divided
Solution is several intrinsic rotational components and a discrepance, and each intrinsic rotational component includes the information of different frequency range, filters out remnants
, retain intrinsic rotational component；
S2, each intrinsic rotational component is done into variation mode decomposition, obtains several groups intrinsic mode functions, select every group of sheet
The maximum component of kurtosis is reconstructed in sign modular function, obtains the reconstruction signal comprising full failure feature；
S3, reconstruction signal is subjected to envelope demodulation by Hilbert transform, is diagnosed to be Main Shaft Bearing of Engine failure classes
Type.
Further setting is the side decomposed using intrinsic time Scale Decomposition to original signal in the step S1
Method are as follows:
Main Shaft Bearing of Engine failure original signal is decomposed into several intrinsic rotational components by intrinsic time Scale Decomposition
With a monotonic signal, original signal is remembered are as follows: Xt=[x1,x2,...,xn], baseline extraction operator is L, for extracting baseline letter
Number, original signal is isolated a background signal and is once decomposed, and remaining signal is considered as an intrinsic rotation after separation
Component, as a result:
Xt=LXt+(1-L)Xt=Lt+Ht (1)
In formula (1), LtIndicate background signal, HtIndicate intrinsic rotational component；
Intrinsic time Scale Decomposition can be divided into following 3 step:
1.1st step finds signal XtLocal Extremum XkAnd its corresponding time point τk, k expression extreme point number, signal
Baseline extraction operator LtIt is defined as follows:
In formula (2),
In formula (3), t ∈ (αk,αk+1), 0 < α < 1, α is gain control parameter, can control the width of intrinsic rotational component
Degree；
1.2nd step, according to decomposition formula:
Ht=(1-L) Xt=Xt-Lt (4)
By background signal, intrinsic rotational component H is found outt；
1.3rd step, by LtAs original signal, the 1.1st step, the 1.2nd step, repetitive assignment, until obtaining a list are recycled
Until the background signal of tune, multiple intrinsic time Scale Decomposition formula is as follows:
In formula (5),For+1 layer of kth intrinsic rotational component,For+1 layer of background signal of kth,For monotonic trend point
Amount filters out discrepance on the basis of above decompose, and retains a series of signal of intrinsic rotational components as next step processing.
Further setting is the method that the variation mode decomposition passed through in the step S2 solves intrinsic mode functions are as follows:
Setting original signal x (t) is made of the component of limited different center frequency, finite bandwidth, variation mode point
Solution is exactly with this condition, to constantly update centre frequency and bandwidth, seek the smallest mode function of the sum of each component bandwidth:
{uk(t) }={ u1(t),u2(t),...,uk(t)} (6)
In formula (6), k indicates that the number of mode function, specific decomposition step are as follows:
2.1st step, to each mode function uk(t) Hilbert envelope transformation is done, corresponding analytic signal is obtained:
In formula (7), t indicates the time, and δ (t) indicates dirichlet function；
2.2nd step pre-estimates the centre frequency of each analytic signalBy the spectrum modulation of each mode to corresponding base
On frequency band, it may be assumed that
In formula (8), { ωk}={ ω1,ω2,...,ωkIndicate each mode function uk(t) centre frequency；
2.3rd step calculates the norm squared L of above-mentioned demodulated signal2, estimate the bandwidth of each mode function, introduce constraint item
Part, corresponding Variation Model are as follows:
To solve constraint variation problem, secondary penalty factor α and Lagrange multiplier operator λ (t) is introduced, by restricted problem
Becoming unconstrained problem, α ensure that reconstruction accuracy of the signal under Gaussian noise, and λ (t) ensure that the stringency of constraint condition,
Extend Lagrangian formulation are as follows:
By multiplication operator alternating direction method, alternating, iteration updateλn+1, solve Lagrange extension
The saddle point of expression formula, i.e. iteration stopping condition finally obtain K mutually independent mode functions.
Innovation Mechanism of the invention is:
Intrinsic time Scale Decomposition (Intrinsic Time-scale Decomposition, ITD) is with higher to be torn open
Efficiency and frequency resolution are solved, complicated non-stationary signal can be resolved into a series of intrinsic rotational component (Proper
Rotation Components, PRC), to accurately extract the dynamic characteristic of signal.Variation mode decomposition (Variational
Mode Decomposition, VMD) optimal solution that Variation Model is searched by the method for iteration, determine each intrinsic mode functions
The centre frequency and bandwidth of (Intrinsic Mode Function, IMF), adaptively realize the subdivision of each component.Through ITD
Improved variation mode decomposition can efficiently separate fault message and interference information, and best heavy according to the selection of kurtosis principle
Structure, final Accurate Diagnosis fault type.Technical solution of the present invention accurately extracts the Weak fault in faulty bearings vibration signal
Feature proposes a kind of improvement variation mode decomposition diagnostic method for Main Shaft Bearing of Engine fault diagnosis.This related side
Face research, there is no report at present.
The invention has the advantages that
On the one hand the method for the present invention utilizes intrinsic time Scale Decomposition, carry out de-noising to primary fault signal, improve noise
Than tentatively extracting fault message；On the other hand, using variation mode decomposition adaptively by signal decomposition to different frequency bands,
Further purify fault message.Fault type is determined finally, demodulating by Hilbert envelope.The combined method overcomes tradition
Method is difficult to the drawbacks of diagnosing early stage bearing Weak fault, is efficiently and accurately applied to Main Shaft Bearing of Engine initial failure and examines
It is disconnected.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention, for those of ordinary skill in the art, without any creative labor, according to
These attached drawings obtain other attached drawings and still fall within scope of the invention.
Flow chart Fig. 1 of the invention；
The solution flow chart of the variation mode decomposition of Fig. 2 embodiment of the present invention；
Outer ring original signal time-domain diagram and envelope spectrogram in case study on implementation 1 Fig. 3 of the invention；
Improved variation mode decomposition reconstruction signal time-domain diagram and envelope spectrum in case study on implementation 1 Fig. 4 of the invention
Outer ring original signal time-domain diagram and envelope spectrogram in case study on implementation 2 Fig. 5 of the invention；
Improved variation mode decomposition reconstruction signal time-domain diagram and envelope spectrum in case study on implementation 2 Fig. 6 of the invention.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, the present invention is made into one below in conjunction with attached drawing
Step ground detailed description.
Noun explanation:
For the ease of the statement of some technical characteristics, the present embodiment some technical characteristics are indicated using English shorthand way,
Concret moun control is described as follows:
Intrinsic time Scale Decomposition (Intrinsic Time-scale Decomposition, ITD)
Intrinsic rotational component (Proper Rotation Components, PRC)
Intrinsic rotation (Proper Rotation, PR)
Variation mode decomposition (Variational Mode Decomposition, VMD)
Intrinsic mode functions (Intrinsic Mode Function, IMF).
As shown in Figure 1, technical solution of the present invention includes:
S1, Main Shaft Bearing of Engine failure original signal is decomposed using intrinsic time Scale Decomposition, signal is divided
Solution is several intrinsic rotational components and a discrepance, and each intrinsic rotational component includes the information of different frequency range, filters out remnants
, retain intrinsic rotational component；
S2, each intrinsic rotational component is done into variation mode decomposition, obtains several groups intrinsic mode functions, select every group of sheet
The maximum component of kurtosis is reconstructed in sign modular function, obtains the reconstruction signal comprising full failure feature；
S3, reconstruction signal is subjected to envelope demodulation by Hilbert transform, is diagnosed to be Main Shaft Bearing of Engine failure classes
Type.
For in the embodiment of the present invention, 1, using intrinsic time Scale Decomposition original signal is decomposed.
Bearing fault signal decomposition is several intrinsic rotational components and a monotonic signal by ITD.Remember original signal are as follows:
Xt=[x1,x2,...,xn], baseline extraction operator is L, for extracting background signal.Original signal isolates a background signal
Once to be decomposed, remaining signal can regard an intrinsic rotational component as after separation, as a result:
Xt=LXt+(1-L)Xt=Lt+Ht (1)
In formula (1), LtIndicate background signal, HtIndicate PRC component.
Intrinsic time Scale Decomposition can be divided into following 3 step:
Step 1 finds signal XtLocal Extremum XkAnd its corresponding time point τk, k expression extreme point number.Signal
Baseline extraction operator LtIt is defined as follows:
In formula (2),
In formula (3), t ∈ (αk,αk+1), 0 < α < 1, α is gain control parameter, can control the amplitude of PR component.
Step 2, according to decomposition formula:
Ht=(1-L) Xt=Xt-Lt (4)
By background signal, intrinsic rotational component H is found outt。
Step 3, by LtAs original signal, step 1, step 2, repetitive assignment, until obtaining a dull base are recycled
Until line signal.Multiple ITD formula is as follows:
In formula (5),For+1 layer of PR component of kth,For+1 layer of background signal of kth,For monotonic trend component.?
On the basis of the above decomposition, discrepance is filtered out, retains a series of signal of intrinsic rotational components as next step processing.
2, using variation mode decomposition into extract fault characteristic signals
Variation mode decomposition is based on the adaptive of the classical theories such as Wiener filtering, Hilbert transformation and heterodyne demodulation
Decomposition method, essence are to find Variation Model optimal solution by iteration, determine the centre frequency and bandwidth of each component.
Assuming that original signal x (t) is made of the component of limited different center frequency, finite bandwidth, VMD decomposition is exactly
With this condition, centre frequency and bandwidth are constantly updated, the smallest mode function of the sum of each component bandwidth is sought:
{uk(t) }={ u1(t),u2(t),...,uk(t)} (6)
In formula (6), k indicates the number of mode function.Specific decomposition step is as follows:
Step 1, to each mode function uk(t) Hilbert envelope transformation is done, corresponding analytic signal is obtained:
In formula (7), t indicates the time, and δ (t) indicates dirichlet function.
Step 2 pre-estimates the centre frequency of each analytic signalBy the spectrum modulation of each mode to corresponding fundamental frequency
It takes, it may be assumed that
In formula (8), { ωk}={ ω1,ω2,...,ωkIndicate each mode function uk(t) centre frequency.
Step 3 calculates the norm squared L of above-mentioned demodulated signal2, estimate the bandwidth of each mode function, introduce constraint condition,
Corresponding Variation Model are as follows:
To solve constraint variation problem, secondary penalty factor α and Lagrange multiplier operator λ (t) is introduced, by restricted problem
Become unconstrained problem.α ensure that reconstruction accuracy of the signal under Gaussian noise, and λ (t) ensure that the stringency of constraint condition.
Extend Lagrangian formulation are as follows:
By multiplication operator alternating direction method, alternating, iteration updateλn+1, solve Lagrange extension
The saddle point of expression formula, i.e. iteration stopping condition finally obtain K mutually independent mode functions.The solution process of Variation Model
As shown in Figure 2.So far, each intrinsic rotational component is decomposed into corresponding intrinsic mode function by variation mode decomposition, is calculated every
The kurtosis of a mode function selects the maximum intrinsic mode function of kurtosis in each group to be reconstructed.
3, it is diagnosed to be bearing fault
Hilbert envelope transformation is done to the reconstruction signal that previous step obtains, reads the fault signature frequency on envelope spectrogram
Rate, last diagnostic go out bearing fault.
In order to verify the validity of improved variation mode decomposition method for diagnosing faults, using certain Main Shaft Bearing of Engine outside
Circle and inner ring fault-signal are verified:
Case study on implementation 2: Main Shaft Bearing of Engine outer ring fault diagnosis
The frequency of the bearing outer ring failure can be calculated according to bearing outer ring fault characteristic frequency formula:
In formula (11), fsIndicate sample frequency, n indicates rolling element number, d rolling element diameter, and D indicates pitch diameter, α table
Show bearing contact angle.This experiment bearing designation is ER-12K, and outer ring has early stage pitting fault.In signal acquisition process, sampling
Frequency is 25600Hz；Bearing is unloaded, speed of service 2400r/min；Sampling number is 32768.Bearing roller number is
8, rolling element diameter is 7.9375mm, pitch diameter 33.4772mm.Sample N=12288 is chosen as original signal, is calculated
Its theoretical fault characteristic frequency is 121.5Hz.
Time domain waveform and the Hilbert envelope spectrogram of original signal are as shown in figure 3, time domain waveform can't see apparent week
The impact of phase property；Envelope spectrogram can not judge fault message in a jumble.Therefore, 4 layers of intrinsic time Scale Decomposition are done to original signal, obtained
To 3 intrinsic rotational components and 1 trend signal.Trend signal is filtered out, using each component as original signal, is input to variation mould
In type, Decomposition order 4.By variation mode decomposition, signal is broken down into 3 groups of totally 12 intrinsic mode functions.Calculate each mould
The maximum kurtosis value of the kurtosis value of state function, first group of mode function is 6.25, and the maximum kurtosis value of second group of mode function is
5.4, the maximum kurtosis value of third group mode function is 4.65.Each group maximum kurtosis component is reconstructed, its waveform diagram is drawn and is wished
That Bert envelope spectrogram from time domain waveform as shown in figure 4, can significantly see regular cyclic breakdown impact, from it
Failure-frequency 114.6Hz and one frequency multiplication 227.1Hz, frequency tripling 343.8Hz, quadruple can be significantly found in envelope spectrum
460.4Hz, to be diagnosed to be outer ring failure.Experimental result fault characteristic frequency 114.6Hz and theoretical value 121.5Hz slightly goes out
Enter, the reason is that there are small sliding in rolling element and outer ring.
Case study on implementation 2: Main Shaft Bearing of Engine inner ring fault diagnosis
The frequency of the bearing inner race failure can be calculated according to bearing inner race fault characteristic frequency formula:
In formula (11), fsIndicate sample frequency, n indicates rolling element number, d rolling element diameter, and D indicates pitch diameter, α table
Show bearing contact angle.This experiment uses ER-12K bearing, and inner ring has early stage pitting fault.In signal acquisition process, sampling frequency
Rate is 25600Hz；Bearing is unloaded, speed of service 1800r/min；Sampling number is 32768.Bearing roller number is 8,
Rolling element diameter is 7.9375mm, pitch diameter 33.4772mm.Sample N=12288 is chosen as original signal, calculates it
Theoretical fault characteristic frequency is 148.5Hz.
Time domain waveform and the Hilbert envelope spectrogram of original signal be can't see bright as shown in figure 5, time domain waveform is more mixed and disorderly
Aobvious periodic shock；Envelope spectrogram, which can only see, to be turned frequency and can not judge fault message.4 layers of intrinsic time are done to original signal
Scale Decomposition obtains 3 intrinsic rotational components and 1 trend signal.Trend signal is filtered out, each intrinsic rotational component is retained, then
It is entered into Variation Model, decomposition scale 4.By variation mode decomposition, signal is broken down into 3 groups of totally 12 variation moulds
State function equally calculates the kurtosis value of each modal components, and the maximum kurtosis value of first group of mode function is 5.27, second group of mode
The maximum kurtosis value of function is 7.95, and the maximum kurtosis value of third group mode function is 4.65.By maximum point of kurtosis in each group
Amount reconstruct, draws its waveform diagram and Hilbert envelope spectrogram as shown in fig. 6, significantly can see there are rule from time domain waveform
The cyclic breakdown of rule impacts, and can significantly find failure-frequency 150Hz from its envelope spectrum, and secondly frequency multiplication 297.9Hz,
Frequency tripling 445.8Hz, quadruple 595.8Hz, to be diagnosed to be inner ring failure.
Those of ordinary skill in the art will appreciate that implement the method for the above embodiments be can be with
Relevant hardware is instructed to complete by program, the program can be stored in a computer readable storage medium,
The storage medium, such as ROM/RAM, disk, CD.
The above disclosure is only the preferred embodiments of the present invention, cannot limit the right model of the present invention with this certainly
It encloses, therefore equivalent changes made in accordance with the claims of the present invention, is still within the scope of the present invention.
Claims (3)
1. a kind of improvement variation mode decomposition diagnostic method for Main Shaft Bearing of Engine fault diagnosis, it is characterised in that including
Have:
S1, Main Shaft Bearing of Engine failure original signal is decomposed using intrinsic time Scale Decomposition, signal is broken down into
Several intrinsic rotational components and a discrepance, each intrinsic rotational component include the information of different frequency range, filter out discrepance, protect
Stay intrinsic rotational component；
S2, each intrinsic rotational component is done into variation mode decomposition, obtains several groups intrinsic mode functions, select every group of eigen mode
The maximum component of kurtosis is reconstructed in function, obtains the reconstruction signal comprising full failure feature；
S3, reconstruction signal is subjected to envelope demodulation by Hilbert transform, is diagnosed to be Main Shaft Bearing of Engine fault type.
2. a kind of improvement variation mode decomposition for Main Shaft Bearing of Engine fault diagnosis according to claim 1 diagnoses
Method, it is characterised in that: the method that original signal is decomposed using intrinsic time Scale Decomposition in the step S1 are as follows:
Main Shaft Bearing of Engine failure original signal is decomposed into several intrinsic rotational components and one by intrinsic time Scale Decomposition
A monotonic signal remembers original signal are as follows: Xt=[x1,x2,...,xn], baseline extraction operator is L, former for extracting background signal
Beginning Signal separator goes out a background signal and is once decomposed, and remaining signal is considered as an intrinsic rotational component after separation,
As a result:
Xt=LXt+(1-L)Xt=Lt+Ht (1)
In formula (1), LtIndicate background signal, HtIndicate intrinsic rotational component；
Intrinsic time Scale Decomposition can be divided into following 3 step:
1.1st step finds signal XtLocal Extremum XkAnd its corresponding time point τk, k expression extreme point number, the base of signal
Line drawing operator LtIt is defined as follows:
In formula (2),
In formula (3), t ∈ (αk,αk+1), 0 < α < 1, α is gain control parameter, can control the amplitude of intrinsic rotational component；
1.2nd step, according to decomposition formula:
Ht=(1-L) Xt=Xt-Lt (4)
By background signal, intrinsic rotational component H is found outt；
1.3rd step, by LtAs original signal, the 1.1st step, the 1.2nd step, repetitive assignment, until obtaining a dull base are recycled
Until line signal, multiple intrinsic time Scale Decomposition formula is as follows:
In formula (5),For+1 layer of kth intrinsic rotational component,For+1 layer of background signal of kth,For monotonic trend component,
On the basis of above decompose, discrepance is filtered out, retains a series of signal of intrinsic rotational components as next step processing.
3. a kind of improvement variation mode decomposition for Main Shaft Bearing of Engine fault diagnosis according to claim 2 diagnoses
Method, it is characterised in that the method that the variation mode decomposition passed through in the step S2 solves intrinsic mode functions are as follows:
Setting original signal x (t) is made of the component of limited different center frequency, finite bandwidth, and variation mode decomposition is just
It is with this condition, to constantly update centre frequency and bandwidth, seek the smallest mode function of the sum of each component bandwidth:
{uk(t) }={ u1(t),u2(t),...,uk(t)} (6)
In formula (6), k indicates that the number of mode function, specific decomposition step are as follows:
2.1st step, to each mode function uk(t) Hilbert envelope transformation is done, corresponding analytic signal is obtained:
In formula (7), t indicates the time, and δ (t) indicates dirichlet function；
2.2nd step pre-estimates the centre frequency of each analytic signalBy the spectrum modulation of each mode to corresponding Base Band
On, it may be assumed that
In formula (8), { ωk}={ ω1,ω2,...,ωkIndicate each mode function uk(t) centre frequency；
2.3rd step calculates the norm squared L of above-mentioned demodulated signal2, estimate the bandwidth of each mode function, introduces constraint condition, it is right
The Variation Model answered are as follows:
To solve constraint variation problem, secondary penalty factor α and Lagrange multiplier operator λ (t) is introduced, restricted problem is become
Unconstrained problem, α ensure that reconstruction accuracy of the signal under Gaussian noise, and λ (t) ensure that the stringency of constraint condition, extension
Lagrangian formulation are as follows:
By multiplication operator alternating direction method, alternating, iteration updateλn+1, solve Lagrange extension expression
The saddle point of formula, i.e. iteration stopping condition finally obtain K mutually independent mode functions.
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| CN113405823B (en) * | 2021-05-17 | 2022-05-20 | 同济大学 | Rotary machine fault diagnosis method based on iterative expansion eigenmode decomposition |
| CN114137338A (en) * | 2021-11-19 | 2022-03-04 | 神华准格尔能源有限责任公司 | Equipment running state monitoring method, system and storage medium |
| CN114137338B (en) * | 2021-11-19 | 2023-12-01 | 神华准格尔能源有限责任公司 | Equipment running state monitoring method, system and storage medium |
| CN114802343A (en) * | 2022-03-21 | 2022-07-29 | 北京全路通信信号研究设计院集团有限公司 | Steel rail damage monitoring method and system |
| CN114802343B (en) * | 2022-03-21 | 2024-01-19 | 北京全路通信信号研究设计院集团有限公司 | Rail damage monitoring method and system |
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