CN106997458A - A kind of equipment vibrating signal feature extracting method based on EEMD CWD - Google Patents
A kind of equipment vibrating signal feature extracting method based on EEMD CWD Download PDFInfo
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- CN106997458A CN106997458A CN201710160030.2A CN201710160030A CN106997458A CN 106997458 A CN106997458 A CN 106997458A CN 201710160030 A CN201710160030 A CN 201710160030A CN 106997458 A CN106997458 A CN 106997458A
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- 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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M7/00—Vibration-testing of structures; Shock-testing of structures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
The invention discloses a kind of equipment vibrating signal feature extracting method based on EEMD CWD, including collecting device vibration signal；EEMD decomposition is carried out to vibration signal, one group of IMF component is obtained；According to kurtosis criterion, preferred is implemented to IMF components；Analyzed by CWD so as to extract the steps such as the fault characteristic information of equipment.The present invention introduces EEMD methods, is added in original signal after white noise, this statistical property is distributed using white noise frequency-flat, can eliminate intermittency in original signal, and then realize the suppression to modal overlap；And vibration signal can be decomposed as some frequency contents single IMF components relatively, CWD technical Analysis is carried out for specific IMF, the effect for reducing frequency alias and interference is can reach.
Description
Technical field
The invention belongs to maintenance of equipment field, more particularly to a kind of equipment vibrating signal feature extraction based on EEMD-CWD
Method.
Background technology
It is the process that useful information is extracted in slave unit primary signal that vibration equipment information characteristics, which are extracted, it is therefore an objective to extracted
The characteristic value of equipment health status information can be reflected by going out.Therefore, the extraction of equipment vibrating signal feature, be for monitoring device
No working healthily important in inhibiting.Have much on vibration equipment information characteristics extracting method, but all more or less exist
Some problems.
Prior art one：
In recent years, Hilbert-Huang transform (Hilbert-Huang Transform, HHT) is in signal characteristic abstraction field
It is widely used.HHT main contents include two parts, and Part I is empirical mode decomposition (Empirical Mode
Decomposition, EMD)；Part II is Hilbert analysis of spectrums (Hilbert Spectrum Analysis, HSA).Base
This process is：First with EMD methods by the signal decomposition of collection be some intrinsic mode functions (Intrinsic Mode
Function, IMF), IMF components contain the key character of primary signal；Then, Hilbert conversion is carried out to each IMF,
Corresponding Hilbert spectrums are obtained, each IMF will be represented in united time-frequency domain；Finally, all IMF Hilbert is collected
The Hilbert spectrums of signal can be obtained by composing, and realize the extraction of signal characteristic.
The Shortcomings of prior art one：
1. the EMD methods used have two features for entering wave filter group in itself, but are asked because it can frequently result in modal overlap
Topic so that features described above is destroyed, causes IMF to meet completely orthogonal；
The problem of 2.HHT has end effect, makes decomposable process introduce new error.
Prior art two：
In order to solve the problems, such as the time frequency processing of vibration signal, researchers successively propose the distribution of many time-frequency energy
The distribution of form, such as Kirkood, Page distributions and Wigner-Ville distribution (WVD).Cohen gives the system of time-frequency distributions
One form, is referred to as Koln class (Cohen classes), and establishes the contact between each time-frequency distributions, is expressed by Uniform Formula.When
When Cohen classes kernel function is exponential type, formula be Qiao-WILLIAMS-DARLING Ton distribution (Choi-Williams Distribution,
CWD).CWD methods are typical Time-Frequency Analysis Methods, and it can suppress cross-interference terms influence to a certain extent.
The deficiency of prior art two：
For frequency content is than more rich equipment vibrating signal, time domain and the frequency domain character change of its vibration signal
Process is complex, and CWD methods are during analyzing the time-frequency domain signal of universe, computationally intensive, Selection of kernel function
Difficulty, causes its method underaction；If CWD methods are analyzed short signal, its suppression energy to signal cross
By kernel function, this body structure is influenceed power, causes its inhibition to weaken significantly.
The content of the invention
The purpose of the present invention is：The present invention provides a kind of equipment vibrating signal feature extracting method based on EEMD-CWD,
By introducing integrated Empirical mode decomposition (Ensemble empirical mode decomposition, EEMD) and combining
CWD analysis methods, the problems such as solving modal overlap and the unobvious inhibition that above-mentioned prior art is present.
The technical scheme is that：A kind of equipment vibrating signal feature extracting method based on EEMD-CWD, including with
Lower step：
Step 1：Collecting device vibration signal；
Step 2：EEMD decomposition is carried out to equipment vibrating signal, one group of IMF component is obtained；
Step 3：According to kurtosis criterion, preferred is implemented to IMF components；
Step 4：By CWD analyses come the fault characteristic information of extraction equipment.
Further, the step 2, comprises the following steps：
Step 2.1：Random Gaussian white noise sequence x is introduced in the original vibration signal x (t) of collectionm(t)=x (t)+
k·nm(t), k is the white noise amplitude coefficient added；
Step 2.2：Calculate all maximum of vibration signal x (t) and minimum point for adding white noise；
Step 2.3：According to above-mentioned maximum and minimum point, by cubic spline interpolation method, go out to construct x (t) one by one
Upper and lower envelope u (t) and v (t)；
Step 2.4：The local mean value of the signal is solved according to m (t)=(u (t)+v (t))/2；
Step 2.5：H (t) is calculated according to h (t)=x (t)-m (t), judges whether h (t) is met as the basic of IMF components
Condition, if it is satisfied, then obtaining first IMF components c1(t), otherwise repeat the above steps 2.1-2.4, until meeting IMF points
Amount condition；
Step 2.6：Subtract c using x (t)1(t) r (t) is obtained, judges whether r (t) needs further decomposition, if desired for decomposition
X (t) then is substituted with r (t), continues repeat step 2.1-2.5, otherwise decomposable process terminates；
Step 2.7：Mutually different white noise sequence is added each time, then repeat step 2.1-2.6；
Step 2.8：The IMF component averages after decomposing are calculated, each IMF components average after decomposition are regard as final calculating
As a result.
Further, the step 3, comprises the following steps：
Step 3.1：According to kurtosis calculation formula, the kurtosis value of each IMF component is calculated：μ is letter
Number x average；σ is signal x standard deviation.
Step 3.2：All kurtosis numerical value calculated are ranked up, the larger IMF components of kurtosis value are chosen.
Further, the step 4, comprises the following steps：
Step 4.1：Selection CWD distribution kernel function be：A is attenuation coefficient, it and cross term
The enough proportional relations of amplitude；
Step 4.2：The IMF components preferably gone out to the step 2 carry out CWD processing, obtain signalWill be eachIt is overlapped, obtains signal x (t) Choi-Williams distributions, i.e.,
Step 4.2：AnalysisAbnormality, change special frequency band the most obvious when determining device fails,
The energy value of the special frequency band changes most sensitive to equipment fault, then extracts above-mentioned special frequency band according to time series order
Energy value, just obtained representing the vibration signal characteristics of institute's analytical equipment.
Further, judge that the primary condition for meeting IMF components is in the step 2.5：Function is in whole time model
The number for enclosing interior Local Extremum and zero crossing must be equal, or at most difference one；And local maxima is put at any time
The envelope of value and the envelope of local minimum are averagely necessary for zero.
Compared with the prior art, beneficial effects of the present invention are embodied in：
1. introducing EEMD methods, added in original signal after white noise, be distributed this using white noise frequency-flat and unify
Characteristic is counted, intermittency in original signal can be eliminated, and then realize the suppression to modal overlap.
2.EEMD methods can decompose vibration signal as some frequency contents single IMF components relatively, for specific
IMF carry out CWD technical Analysis, can reach reduce frequency alias and interference effect.
Brief description of the drawings
Fig. 1 signal characteristic extracting methods flow charts of the present invention；
Fig. 2 gear box structure schematic diagrames of the embodiment of the present invention；
Each moment original vibration signal figure of Fig. 3 embodiment of the present invention passage 1；
Vibration signal EEMD analysis results during Fig. 4 gear-box t=200h of the embodiment of the present invention；
Fig. 5 gear-boxes of embodiment of the present invention t=1h EEMD-CWD analysis results；
Fig. 6 gear-boxes of embodiment of the present invention t=200h EEMD-CWD analysis results.
Reference：
In Fig. 3, S1, S2, S3, S4 represent sensor arrangement point position respectively.
Embodiment
Below in conjunction with the drawings and specific embodiments of the present invention, technical scheme is carried out clearly and completely
Description.Obviously, described embodiment is a part of embodiment of the invention, rather than whole embodiments, protection of the invention
Scope is not limited only to the limitation of following embodiments.
The present embodiment is extracted by the vibration signal characteristics to gear-box, to state technical scheme.
Equipment includes in embodiment：One electromagnetic varispeed motor, a speed and torque sensor, two grades of three axles
Uni-drive gear box, a computer, four piezoelectric acceleration transducers, the data acquisition of data collecting card and Labview softwares
System and an air-cooled magnetic powder brake that load is provided to gear-box.
Power source used in experimental facilities and experiment is model YCT180-4A type electromagnetic adjustable speed motors, and magnetic powder brake is type
Number air-cooled magnetic powder brake of FZ200.K/F types, gear-box used is as shown in Fig. 2 major parameter is shown in Table 1.
The gear-box major parameter of table 1
Step 1：Collecting device vibration signal
It is two grades of helical teeth roller boxs to test gear-box, and rated speed is 1450r/min, and nominal transmission power is 0.75kW；It is logical
4 vibration acceleration sensors crossed on casing, the different vibration signal in 4 tunnels can be gathered simultaneously.By the life-cycle
Experiment finds that after this tested gear-box works about 450 hours, there occurs major failure form is gear teeth face heavy wear
Failure.
The vibration signal of gear-box is acquired in experimentation, sample frequency is 20kHz, sampled 1 time per hour, tired out
Meter sampling 442 hours.Fragmentary data collection, the i.e. state of wear only to the part time of running are carried out to gear teeth wear extent simultaneously
Detected, the wear data of acquisition is shown in Table 2, and the wherein part primary signal of passage 1 is as shown in Figure 3.
The different detection time gear wear amounts of table 2
Step 2：EEMD decomposition is carried out to equipment vibrating signal, one group of IMF component is obtained.
By taking t=200h as an example：
Step 2.1：Random Gaussian white noise sequence is introduced in original vibration signal x (200)；
Step 2.2：Calculate all maximum of vibration signal x (200) and minimum point for adding white noise；
Step 2.3：According to above-mentioned maximum and minimum point, by cubic spline interpolation method, go out to construct x one by one
(200) upper and lower envelope u (200) and v (200)；
Step 2.4：The local mean value of the signal is solved according to m (200)=(u (200)+v (200))/2；
Step 2.5：H (200) is calculated according to h (200)=x (200)-m (200), c is judged1(200) whether meet above-mentioned
IMF component conditions：Local Extremum and the number of zero crossing must be equal in whole time range for function, or at most differ one
It is individual；And the envelope of point local maximum and the envelope of local minimum are averagely necessary for zero at any time.If it is satisfied, then
Obtain first IMF components c1(200), otherwise repeat the above steps 2.1-2.4, until meeting IMF component conditions；
Step 2.6：Subtract c using x (200)1(200) r (200) is obtained, judges whether r (200) needs further decomposition, such as
Need to decompose and then substitute x (200) with r (200), continue the 2.1-2.5 that repeats the above steps, otherwise decomposable process terminates；
Step 2.7：Mutually different white noise sequence is added each time, then repeat step 2.1-2.6；
Step 2.8：The IMF component averages after decomposing are calculated, each IMF components average after decomposition are regard as final calculating
As a result, the EEMD analysis results (Fig. 4) of t=200h moment gear-boxes vibration signal.In Fig. 4, c1~c7Vibrate and accelerate for gear-box
Each rank IMF components (frequency content is arranged in order from high to low) obtained after degree signal decomposition, c8It is then the remnants of signal decomposition
Component.
Step 3：According to kurtosis criterion, preferred is implemented to IMF components；
Step 3.1：According to kurtosis calculation formula, the kurtosis value of each IMF component is calculated：μ is letter
Number x average；σ is signal x standard deviation, calculates the c obtained after EEMD is decomposed1~c8The kurtosis value such as table of rank IMF components
Shown in 3.
Vibration signal IMF kurtosis values during 3 gear-box t=200h of table
Step 3.2：All kurtosis numerical value calculated are ranked up, the larger IMF components of kurtosis value are chosen.
Step 4：By CWD analyses come the fault characteristic information of extraction equipment.
Step 4.1：Selection CWD distribution kernel function be：A is attenuation coefficient, it and cross term
The enough proportional relations of amplitude；
Step 4.2：The IMF components preferably gone out to the step 2 carry out CWD processing, obtain signal It is overlapped, obtains signal x (t) Choi-Williams distributions, i.e.,Choose the larger c of kurtosis value1And c2Enter
Row CWD is analyzed, and overlay analysis result, finally obtains the result of vibration signal under gear-box each state, such as Fig. 5, Fig. 6
It is shown.
Step 4.2：AnalysisAbnormality, change special frequency band the most obvious when determining device fails,
Compare Fig. 5 and Fig. 6 to understand：The amplitude of the signal Choi-Williams spectrums of the vibration signal in t=200h of gear-box shown in Fig. 6
It increased in 0.17kHz and 0.33kHz the two frequency ranges, the energy value of the frequency band can be moved back as gear-box is indicated
The characteristic value of change state.The energy value of the special frequency band changes most sensitive to equipment fault, then according to time series order
The energy value of above-mentioned special frequency band is extracted, has just obtained representing the vibration signal characteristics of institute's analytical equipment.As can be seen that utilizing this
After technology is handled primary signal, the difference between different conditions lower tooth roller box vibration signal characteristics has obtained prominent aobvious
Show, be conducive to recognizing the working condition of gear-box exactly, so as to realize the status information feature extraction of gear-box.
Claims (5)
1. a kind of equipment vibrating signal feature extracting method based on EEMD-CWD, it is characterised in that comprise the following steps：
Step 1：Collecting device vibration signal；
Step 2：EEMD decomposition is carried out to equipment vibrating signal, one group of IMF component is obtained；
Step 3：According to kurtosis criterion, preferred is implemented to IMF components；
Step 4：By CWD analyses come the fault characteristic information of extraction equipment.
2. a kind of equipment vibrating signal feature extracting method based on EEMD-CWD according to claim 1, its feature exists
In the step 2 comprises the following steps：
Step 2.1：Random Gaussian white noise sequence x is introduced in the original vibration signal x (t) of collectionm(t)=x (t)+knm
(t), k is the white noise amplitude coefficient added；
Step 2.2：Calculate all maximum of vibration signal x (t) and minimum point for adding white noise；
Step 2.3：According to above-mentioned maximum and minimum point, by cubic spline interpolation method, go out to construct x (t) one by one upper and lower
Envelope u (t) and v (t)；
Step 2.4：The local mean value of the signal is solved according to m (t)=(u (t)+v (t))/2；
Step 2.5：H (t) is calculated according to h (t)=x (t)-m (t), judges whether h (t) meets the basic bar as IMF components
Part, if it is satisfied, then obtaining first IMF components c1(t), otherwise repeat the above steps 2.1-2.4, until meeting IMF components
Condition；
Step 2.6：Subtract c using x (t)1(t) r (t) is obtained, judges whether r (t) needs further decomposition, r is then used if desired for decomposing
(t) x (t) is substituted, continues the 2.1-2.5 that repeats the above steps, otherwise decomposable process terminates；
Step 2.7：Mutually different white noise sequence is added each time, then repeat step 2.1-2.6；
Step 2.8：The IMF component averages after decomposing are calculated, each IMF components average after decomposition are regard as final calculating knot
Really.
3. a kind of equipment vibrating signal feature extracting method based on EEMD-CWD according to claim 1, its feature exists
In the step 3 comprises the following steps：
Step 3.1：According to kurtosis calculation formula, the kurtosis value of each IMF component is calculated：μ is signal x
Average；σ is signal x standard deviation.
Step 3.2：All kurtosis numerical value calculated are ranked up, the larger IMF components of kurtosis value are chosen.
4. a kind of equipment vibrating signal feature extracting method based on EEMD-CWD according to claim 1, its feature exists
In the step 4 comprises the following steps：
Step 4.1：Selection CWD distribution kernel function be：A is attenuation coefficient, and it is with cross term amplitude
Enough proportional relations；
Step 4.2：The IMF components preferably gone out to the step 2 carry out CWD processing, obtain signalWill be eachEnter
Row superposition, obtains signal x (t) Choi-Williams distributions, i.e.,
Step 4.2：AnalysisAbnormality, change special frequency band the most obvious, the spy when determining device fails
The energy value for determining frequency band changes most sensitive to equipment fault, and the energy of above-mentioned special frequency band is then extracted according to time series order
Value, has just obtained representing the vibration signal characteristics of institute's analytical equipment.
5. a kind of equipment vibrating signal feature extraction side based on EEMD-CWD according to Claims 1-4 any one
Method, it is characterised in that judge that the primary condition for meeting IMF components is in the step 2.5：Function office in whole time range
The number of portion's extreme point and zero crossing must be equal, or at most difference one；And the bag of local maximum is put at any time
Network and the envelope of local minimum are averagely necessary for zero.
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| CN109080661A (en) * | 2018-07-27 | 2018-12-25 | 广州地铁集团有限公司 | It is a kind of that fault detection method is ground based on the track wave of EEMD Energy-Entropy and WVD |
| CN109342091A (en) * | 2018-08-31 | 2019-02-15 | 南京理工大学 | Vibration fault extracting method based on self-adaptive harmonics detection and improvement EMD |
| CN109374119A (en) * | 2018-09-29 | 2019-02-22 | 国网山西省电力公司阳泉供电公司 | Transformer vibration signal Characteristic Extraction method |
| CN109682958A (en) * | 2018-09-21 | 2019-04-26 | 深圳沃德生命科技有限公司 | A kind of acceleration transducer signals compensation method for thrombelastogram instrument |
| CN109883704A (en) * | 2019-03-11 | 2019-06-14 | 鲁东大学 | A kind of extracting method of the Rolling Bearing Fault Character based on EEMD and K-GDE |
| CN110688981A (en) * | 2019-10-14 | 2020-01-14 | 江西理工大学 | Modal aliasing elimination method for denoising vibration signal |
| CN110763445A (en) * | 2019-10-12 | 2020-02-07 | 西人马(西安)测控科技有限公司 | Brake monitoring method, device, equipment and storage medium |
| CN111243121A (en) * | 2020-01-14 | 2020-06-05 | 广东寰球智能科技有限公司 | Quality monitoring method and device for metal cover and computer readable storage medium |
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| CN109883704A (en) * | 2019-03-11 | 2019-06-14 | 鲁东大学 | A kind of extracting method of the Rolling Bearing Fault Character based on EEMD and K-GDE |
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| CN110688981B (en) * | 2019-10-14 | 2023-06-09 | 江西理工大学 | Modal aliasing elimination method for denoising vibration signal |
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