CN106203325A - Based on strengthening the Weak fault signal characteristic extracting methods cascading multistable accidental resonance - Google Patents
Based on strengthening the Weak fault signal characteristic extracting methods cascading multistable accidental resonance Download PDFInfo
- Publication number
- CN106203325A CN106203325A CN201610531414.6A CN201610531414A CN106203325A CN 106203325 A CN106203325 A CN 106203325A CN 201610531414 A CN201610531414 A CN 201610531414A CN 106203325 A CN106203325 A CN 106203325A
- Authority
- CN
- China
- Prior art keywords
- multistable
- stochastic resonance
- resonance system
- signal
- strengthening
- 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.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
A kind of based on strengthening the Weak fault signal characteristic extracting methods cascading multistable accidental resonance, first multiple multistable systems are cascaded on the basis of multistable stochastic resonance system by described method, then drive signal the second round by matching to the characteristic time of cascade multistable system application time respectively at different levels yardstick with multistable system, make multistable system produces the new phenomenon that accidental resonance is strengthened.The inventive method enhances the ability to Detection of Weak Signals, overcomes small-signal under strong noise background to be difficult to the problem extracted, and makes the Weak fault information flooded by noise be amplified, and achieves the diagnosis of bearing mechanical fault.
Description
Technical field
The present invention relates to mechanical fault diagnosis field, particularly relate to a kind of based on strengthening the multistable accidental resonance of cascade
Weak fault signal characteristic extracting methods.
Background technology
Along with modern production science and technology development, plant equipment maximizes increasingly, high speed, automatization, intellectuality.
The most complicated at equipment the Nomenclature Composition and Structure of Complexes, while function is more improved, the contact between each parts is tightr, dynamic row
For more complicated, thus the frequency broken down and diagnosis difficulty gradually step up.Heavy mechanical equipment be power, machine-building,
The nucleus equipment in the fields such as oil, metallurgy, generating, chemical industry, once one of them parts occurs that inefficacy or fault will be led
Cause whole system cannot be properly functioning, the lighter affects production, causes huge economic loss, heavy then may cause casualties,
Major accident occurs.Therefore, the generation to avoiding catastrophe failure of the mechanical fault features it is diagnosed to be, it is ensured that plant equipment
Properly functioning have major and immediate significance.But, the feature of initial failure itself is the faintest, it is achieved initial failure is faint
The extraction of feature is extremely challenging.Existing Weak fault signal characteristic extracting methods is to examine from the angle eliminating noise mostly
Survey fault signature, but for Weak characteristic signal extraction under strong noise background, although noise reduction simply is to a certain extent
Reducing noise, but also weaken characteristic signal, this makes to measure signal and is difficult to detection in the case of signal to noise ratio is extremely low.
Accidental resonance is a kind of to utilize noise to strengthen the new theory of small-signal, and it is to cause faint for medium with noise
Periodic signal and non-linear synergistic non-linear phenomena, as a kind of Weak characteristic extracting method, be applied to faint letter
Number detection field.Compared with traditional method, it strengthens Weak characteristic while attenuating noise, improves signal to noise ratio, it is achieved faint
The detection of signal.But, between input signal, noise and nonlinear system, only meet certain matching condition, could produce
Raw Stochastic Resonance Phenomenon, it is achieved the extraction of Weak characteristic.
Reality is when the small-signal that process signal to noise ratio is extremely low, and the effect of general random resonance can not reach our pre-
Phase target, can affect to the accuracy of diagnostic result.
Summary of the invention
Present invention aim at providing a kind of and amplify the multistable based on strengthening cascade of Weak fault information, conveniently extraction signal
The Weak fault signal characteristic extracting methods of accidental resonance.
For achieving the above object, have employed techniques below scheme: the invention mainly comprises a kind of enhancing cascade multistable at random
Resonator system, the described enhancing multistable stochastic resonance system of cascade is sequentially connected with by multiple multistable stochastic resonance system and forms, and front
The output of the multistable stochastic resonance system of one-level is as the input of the multistable stochastic resonance system of next stage, and afterbody is multistable the most altogether
Vibrating system is output as strengthening the output cascading multistable stochastic resonance system；Described method is multistable based on described enhancing cascade
Stochastic resonance system, applies a time to the at different levels multistable stochastic resonance system strengthening the multistable stochastic resonance system of cascade respectively
The second round that yardstick and the characteristic time strengthening the multistable stochastic resonance system of cascade match drives signal, makes enhancing cascade many
Steady stochastic resonance system produces the new phenomenon that accidental resonance is strengthened.
Specifically comprising the following steps that of described method
Step 1, calculates the fault characteristic frequency treating diagnostic machine tool equipment, extracts and treats that the vibration data of diagnostic machine tool equipment is made
For strengthening the input cascading multistable stochastic resonance system, strengthen the output cascading multistable stochastic resonance system and tie as vibration data
Really；The vibration data result extracted is carried out Fourier transformation, obtains the frequency spectrum of vibration signal, and determine in frequency spectrum contained
Frequency content；
Step 2, carries out optimal choice to the parameter of all of multistable stochastic resonance system, is introduced by optimal vibration signal
Strengthen and cascade multistable stochastic resonance system；
Step 3, applies coupling to strengthening the every one-level multistable stochastic resonance system input cascading multistable stochastic resonance system
The second drive cycle signal, make enhancing cascade in multistable stochastic resonance system and produce the new phenomenon that accidental resonance is strengthened；
Step 4, for making the process of extraction characteristic signal definitely, cascades each of multistable stochastic resonance system to strengthening
The multistable stochastic resonance system output signal of level carries out Fourier transformation, and obtains the frequency spectrum of each output signal, observes every one-level
In the frequency spectrum of multistable stochastic resonance system output signal, the most faulty characteristic frequency composition, judges this plant equipment according to result
Whether there is fault and the position broken down.
Further, in step 2, described multistable stochastic resonance system by Langevin equation dx/dt=-dU (x)/
Dx+s (t)+η (t) is described；
In formula,Potential function U (x) of multistable stochastic resonance system be by systematic parameter b, c,
The value of d together decides on, and when b, c, d value difference, this potential function is probably three steady, bistables and monostable；S (t) is faint letter
Number；D is noise intensity, ε (t) be average be 0, variance be the white noise of 1；Regulation systematic parameter b, c, d, make
Systematic parameter b, c, d reach with inputted vibration signal most preferably to mate and with Stochastic Resonance Phenomenon occur.
Further, in step 3, to strengthening the multistable stochastic resonance system of every one-level cascading multistable stochastic resonance system
Input applies the two driving signal of coupling, and the corresponding kinetics equation of the multistable stochastic resonance system of every one-level becomes dx/dt=-
dU(x)/dx+s(t)+s1(t)+η(t)；
In formula, s1T () is the second drive cycle signal that system adds.
Compared with prior art, present invention have the advantage that and utilize multistable stochastic resonance system to time domain waveform noise reduction
Good characteristic, to have noise cancellation signal carry out cascade accidental resonance output, energy gradually by high frequency while low-frequency transfer, make letter
Number obtain noise reduction, make the phenomenon producing accidental resonance in multistable system be strengthened, and then achieve having of bearing mechanical fault
Effect diagnosis.Overcome the problem that under strong noise background, weak signal extraction is difficult, make the Weak fault information flooded by noise be able to
Amplify, significant to the Incipient Fault Diagnosis of plant equipment.
Accompanying drawing explanation
Fig. 1 is the flow chart of the inventive method.
Fig. 2 is time domain waveform and the spectrogram of primary signal.
Fig. 3 is that primary signal directly carries out two-stage cascade result figure.
When Fig. 4 is B=0.04, C=0, two-level concatenation multistable stochastic resonance system output result figure.
When Fig. 5 is B=0.04, C=0.08, two-level concatenation multistable stochastic resonance system output result figure.
Detailed description of the invention
The present invention will be further described below in conjunction with the accompanying drawings:
The invention mainly comprises a kind of enhancing and cascade multistable stochastic resonance system, described enhancing cascades multistable accidental resonance system
System is sequentially connected with by multiple multistable stochastic resonance system and forms, and the output of the multistable stochastic resonance system of previous stage is as next stage
The input of multistable stochastic resonance system, the multistable stochastic resonance system of afterbody is output as strengthening and cascades multistable accidental resonance system
The output of system；Described method is to cascade multistable stochastic resonance system based on described enhancing, gives and strengthens the multistable accidental resonance of cascade
The at different levels multistable stochastic resonance system of system applies a time scale respectively and strengthens the spy cascading multistable stochastic resonance system
The second round that the time of levying matches drives signal, makes enhancing cascade in multistable stochastic resonance system and produces accidental resonance reinforcement
New phenomenon.
As it is shown in figure 1, the specifically comprising the following steps that of described method
Step 1, calculates the fault characteristic frequency treating diagnostic machine tool equipment, extracts and treats that the vibration data of diagnostic machine tool equipment is made
For strengthening the input cascading multistable stochastic resonance system, strengthen the output cascading multistable stochastic resonance system and tie as vibration data
Really；The vibration data result extracted is carried out Fourier transformation, obtains the frequency spectrum of vibration signal, and determine frequency contained in frequency spectrum
Rate composition；
Step 2, carries out optimal choice to the parameter of all of multistable stochastic resonance system, is introduced by optimal vibration signal
Strengthen and cascade multistable stochastic resonance system；Multistable stochastic resonance system passes through Langevin equation dx/dt=-dU (x)/dx+s
T ()+η (t) is described；
In formula,Potential function U (x) of multistable stochastic resonance system is by systematic parameter b, c, d
Value together decide on, when b, c, d value difference, this potential function is probably three steady, bistables and monostable；S (t) is faint letter
Number；D is noise intensity, ε (t) be average be 0, variance be the white noise of 1；Regulation systematic parameter b, c, d, make
Systematic parameter b, c, d reach with inputted vibration signal most preferably to mate and with Stochastic Resonance Phenomenon occur.
As a example by selecting two-stage cascade multistable system, its Langevin equation is:
In formula, x1T () is the output signal of first order cascade system, x2T () is the output signal of second level cascade system.
Step 3, applies coupling to strengthening the every one-level multistable stochastic resonance system input cascading multistable stochastic resonance system
Two driving signal, the corresponding kinetics equation of the multistable stochastic resonance system of every one-level becomes dx/dt=-dU (x)/dx+s
(t)+s1(t)+η(t)；
In formula, s1T () is the second drive cycle signal that system adds.
Then the Langevin equation of the multistable stochastic resonance system of two-stage cascade becomes:
In formula, x1(t) and x2T () is the first order and the output signal of secondary cascade multistable system respectively.Asin(2πf0) be
Make the period 1 signal of system generation accidental resonance, Bsin (2 π f1T) it is that the second drive cycle that first order system adds is believed
Number, Csin (2 π f2T) it is the second drive cycle signal of adding of second level system.The value mating B, C respectively makes the multistable system of cascade
System produces the phenomenon that accidental resonance is strengthened；
Step 4, for making the process of extraction characteristic signal definitely, cascades each of multistable stochastic resonance system to strengthening
The multistable stochastic resonance system output signal of level carries out Fourier transformation, and obtains the frequency spectrum of each output signal, observes every one-level
In the frequency spectrum of multistable stochastic resonance system output signal, the most faulty characteristic frequency composition, judges this plant equipment according to result
Whether there is fault and the position broken down.
Embodiment one:
Rolling bearing is typically made up of inner ring, outer ring, rolling element and retainer four part, if rolling bearing occurs event
Barrier, its failure-frequency is predictable.Assuming that the outer ring of rolling bearing is fixed, inner ring rotates with working shaft, and working shaft rotating speed is N
(r/min), bearing pitch diameter is D (mm), a diameter of d of rolling element (mm), and contact angle is β (rad), and rolling element number is n, then it is not
As follows with fault characteristic frequency:
Characteristic frequency when bearing outer ring is defective:
Characteristic frequency when bearing inner race is defective:
Characteristic frequency when single rolling element is defective:
Mill frequency is touched in retainer and outer ring:
Being the bearing of SKF6205 for model, rotating speed is 1797r/min (29.95Hz), and sample frequency is 12kHz.This axle
The pitch diameter held is 39.04mm, a diameter of 7.94mm of rolling element, and rolling element number is 9, and contact angle is 0 °.
For the rolling bearing of this model, as shown in table 1 through can be calculated its fault characteristic frequency.
| Inner ring failure-frequency | 162Hz |
| Outer ring failure-frequency | 107Hz |
| Rolling element failure-frequency | 141Hz |
| Retainer failure-frequency | 12Hz |
Table 1 bearing fault characteristics frequency
First, bearing signal is acquired, such as Fig. 2, for time domain waveform and the spectrogram of this bearing inner race fault-signal.
There is the vibration of obvious periodic impulse from the time domain waveform of Fig. 2 (a) visible signal, and in spectrogram 2 (b), spectrum energy divides
Cloth is in a frequency range the widest, and has big spectral peak group in the range of 1kHz～2kHz, and this is the one of parts of bearings
The vibration of rank natural mode of vibration, but can't see obvious vibration performance in the low-frequency range of original spectrum.
The output that Fig. 3 is primary signal after cascade multistable system, wherein 1 grade, the cascade parameter of 2 grades is: b1=b2
=0.52, c1=c2=-0.33, d1=d2=0.05, double sampling frequency is 6Hz.From Fig. 3 (b) and 3 (d) it can be seen that
After cascade multistable system, in original spectrum, the HFS of single order principal oscillation mode is by completely " filtering ", occurs bright in frequency spectrum
Aobvious characteristic frequency fc, after 2 grades of cascade multistable systems, due to high-frequency energy constantly to low-frequency transfer, Fig. 3
D () stays characteristic frequency f clearlyc, in frequency spectrum, the amplitude of characteristic frequency becomes 0.08164 from 0.05793, bright
Aobvious increase.After this explanation multistable system is cascaded in series for, primary signal is reached good noise reduction and shaping effect.
Fig. 4 and Fig. 5 is that primary signal is through cascade multistable system strengthened output spectrum figure, wherein 1 grade, 2 grades of cascades
Parameter is for keeping constant, and double sampling frequency is still 6Hz.Fig. 4 is at B=0.04, and during C=0, i.e. first order cascade system adds
X after two driving signal2The spectrogram of (t).Wherein, the second driving signal frequency of addition is f1=0.65HZ.Fig. 5 is at B=
When 0.04, C=0.08, i.e. first order cascade system and second level cascade system is simultaneously introduced x after two driving signal2(t)
Spectrogram.Wherein, the second driving signal frequency added in the cascade system of the second level is f2=0.88HZ.In the diagram, x2(t)
Spectrum amplitude at fcPlace occurs that peak value is 0.1152, increases 0.033 than Fig. 3.Comparison diagram 4 and Fig. 3, illustrated for the second driving week
Phase signal can strengthen the effect of accidental resonance.In Figure 5, x2T the spectrum amplitude of () is at fcThe peak value at place is 0.12, Amplitude Ration
Fig. 4 is big, and accidental resonance has been carried out enhancing again.Rolling bearing experiment demonstrates addition the second drive cycle signal can be had
Effect strengthens the effect of accidental resonance.
In sum, utilize the method strengthening the multistable stochastic resonance system of cascade to extract feature small-signal, removing
Optimize the effect of accidental resonance while high-frequency noise, and then realize the efficient diagnosis to mechanical breakdown.This method overcomes
The problem that under strong noise background, weak signal extraction is difficult, makes the Weak fault information flooded by noise be amplified, to rolling
The Incipient Fault Diagnosis of bearing is significant.
Embodiment described above is only to be described the preferred embodiment of the present invention, the not model to the present invention
Enclose and be defined, on the premise of designing spirit without departing from the present invention, the those of ordinary skill in the art technical side to the present invention
Various deformation that case is made and improvement, all should fall in the protection domain that claims of the present invention determines.
Claims (4)
1., based on strengthening the Weak fault signal characteristic extracting methods cascading multistable accidental resonance, mainly include a kind of enhancing
Cascade multistable stochastic resonance system, it is characterised in that: described enhancing cascades multistable stochastic resonance system by multiple multistable random common
Vibrating system is sequentially connected with composition, and the output of the multistable stochastic resonance system of previous stage is as the multistable stochastic resonance system of next stage
Input, the multistable stochastic resonance system of afterbody is output as strengthening the output cascading multistable stochastic resonance system；Described method
To cascade multistable stochastic resonance system based on described enhancing, give strengthen cascade multistable stochastic resonance system at different levels multistable at random
Resonator system apply respectively a time scale with strengthen cascade multistable stochastic resonance system characteristic time match second
Periodic drive signal, makes enhancing cascade in multistable stochastic resonance system and produces the new phenomenon that accidental resonance is strengthened.
It is the most according to claim 1 based on strengthening the Weak fault signal characteristic extracting methods cascading multistable accidental resonance,
It is characterized in that, specifically comprising the following steps that of described method
Step 1, calculates the fault characteristic frequency treating diagnostic machine tool equipment, extracts and treats that the vibration data of diagnostic machine tool equipment is as increasing
Intensity level joins the input of multistable stochastic resonance system, strengthens and cascades the output of multistable stochastic resonance system as vibration data result；
The vibration data result extracted is carried out Fourier transformation, obtains the frequency spectrum of vibration signal, and determine frequency contained in frequency spectrum
Composition；
Step 2, carries out optimal choice to the parameter of all of multistable stochastic resonance system, is introduced by optimal vibration signal and strengthens
Cascade multistable stochastic resonance system；
Step 3, applies the of coupling to strengthening the every one-level multistable stochastic resonance system input cascading multistable stochastic resonance system
Two drive cycle signals, make enhancing cascade in multistable stochastic resonance system and produce the new phenomenon that accidental resonance is strengthened；
Step 4, for making the process of extraction characteristic signal definitely, to strengthening, the every one-level cascading multistable stochastic resonance system is many
Steady stochastic resonance system output signal carries out Fourier transformation, and obtains the frequency spectrum of each output signal, observes every one-level multistable
In the frequency spectrum of stochastic resonance system output signal, whether the most faulty characteristic frequency composition, judge this plant equipment according to result
There is fault and the position broken down.
It is the most according to claim 2 based on strengthening the Weak fault signal characteristic extracting methods cascading multistable accidental resonance,
It is characterized in that: in step 2, described multistable stochastic resonance system passes through Langevin equation dx/dt=-dU (x)/dx+s
T ()+η (t) is described；
In formula,Potential function U (x) of multistable stochastic resonance system is by systematic parameter b, the taking of c, d
Value together decides on, and when b, c, d value difference, this potential function is probably three steady, bistables and monostable；S (t) is small-signal；D is noise intensity, ε (t) be average be 0, variance be the white noise of 1；Regulation systematic parameter b, c, d, make be
System parameter b, c, d reach with inputted vibration signal most preferably to mate and with Stochastic Resonance Phenomenon occur.
It is the most according to claim 2 based on strengthening the Weak fault signal characteristic extracting methods cascading multistable accidental resonance,
It is characterized in that: in step 3, execute strengthening the every one-level multistable stochastic resonance system input cascading multistable stochastic resonance system
Add the two driving signal of coupling, the corresponding kinetics equation of the multistable stochastic resonance system of every one-level become dx/dt=-dU (x)/
dx+s(t)+s1(t)+η(t)；
In formula, s1T () is the second drive cycle signal that system adds.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610531414.6A CN106203325A (en) | 2016-07-07 | 2016-07-07 | Based on strengthening the Weak fault signal characteristic extracting methods cascading multistable accidental resonance |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610531414.6A CN106203325A (en) | 2016-07-07 | 2016-07-07 | Based on strengthening the Weak fault signal characteristic extracting methods cascading multistable accidental resonance |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106203325A true CN106203325A (en) | 2016-12-07 |
Family
ID=57472726
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610531414.6A Pending CN106203325A (en) | 2016-07-07 | 2016-07-07 | Based on strengthening the Weak fault signal characteristic extracting methods cascading multistable accidental resonance |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106203325A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108388232A (en) * | 2018-03-20 | 2018-08-10 | 江南大学 | A kind of operational mode fault monitoring method of crude oil desalting process |
| CN110319357A (en) * | 2018-03-30 | 2019-10-11 | 中国科学院声学研究所 | A kind of gas pipe leakage detection positioning system and method injected using sound |
| CN111339723A (en) * | 2020-02-25 | 2020-06-26 | 燕山大学 | Novel second-order multistable stochastic resonance circuit |
| CN112785377A (en) * | 2021-01-22 | 2021-05-11 | 华南理工大学 | Data distribution-based order completion period prediction model construction method and prediction method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101191804A (en) * | 2007-12-03 | 2008-06-04 | 中国人民解放军国防科学技术大学 | Self-adapting random resonant weak signal detection method |
| CN102778356A (en) * | 2012-08-06 | 2012-11-14 | 西安交通大学 | Enhanced stochastic resonance system and diagnostic method for machinery fault based on system |
| CN105181334A (en) * | 2015-09-21 | 2015-12-23 | 燕山大学 | Rolling bearing fault detection method based on cascade multistable stochastic resonance and empirical mode decomposition (EMD) |
-
2016
- 2016-07-07 CN CN201610531414.6A patent/CN106203325A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101191804A (en) * | 2007-12-03 | 2008-06-04 | 中国人民解放军国防科学技术大学 | Self-adapting random resonant weak signal detection method |
| CN102778356A (en) * | 2012-08-06 | 2012-11-14 | 西安交通大学 | Enhanced stochastic resonance system and diagnostic method for machinery fault based on system |
| CN105181334A (en) * | 2015-09-21 | 2015-12-23 | 燕山大学 | Rolling bearing fault detection method based on cascade multistable stochastic resonance and empirical mode decomposition (EMD) |
Non-Patent Citations (1)
| Title |
|---|
| 曲媛等: "级联双稳系统随机共振的加强", 《中国科学：物理学 力学 天文学》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108388232A (en) * | 2018-03-20 | 2018-08-10 | 江南大学 | A kind of operational mode fault monitoring method of crude oil desalting process |
| CN108388232B (en) * | 2018-03-20 | 2020-07-24 | 江南大学 | Method for monitoring operation mode fault in crude oil desalting process |
| CN110319357A (en) * | 2018-03-30 | 2019-10-11 | 中国科学院声学研究所 | A kind of gas pipe leakage detection positioning system and method injected using sound |
| CN111339723A (en) * | 2020-02-25 | 2020-06-26 | 燕山大学 | Novel second-order multistable stochastic resonance circuit |
| CN112785377A (en) * | 2021-01-22 | 2021-05-11 | 华南理工大学 | Data distribution-based order completion period prediction model construction method and prediction method |
| CN112785377B (en) * | 2021-01-22 | 2022-05-24 | 华南理工大学 | Data distribution-based order completion period prediction model construction method and prediction method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106203325A (en) | Based on strengthening the Weak fault signal characteristic extracting methods cascading multistable accidental resonance | |
| Li et al. | Rolling bearing fault diagnosis based on time-delayed feedback monostable stochastic resonance and adaptive minimum entropy deconvolution | |
| CN105181334A (en) | Rolling bearing fault detection method based on cascade multistable stochastic resonance and empirical mode decomposition (EMD) | |
| CN110146291A (en) | A kind of Rolling Bearing Fault Character extracting method based on CEEMD and FastICA | |
| CN105825197A (en) | Linearization potential well wall path expansion stochastic resonance weak feature extraction method | |
| CN102778356B (en) | Enhanced stochastic resonance system and diagnostic method for machinery fault based on system | |
| Sun et al. | Online machining chatter forecast based on improved local mean decomposition | |
| CN106525426B (en) | Weak signal enhancement detection method based on complementary stochastic resonance filter | |
| CN106441889B (en) | Weak signal detection method based on self-adaptive stochastic resonance filter | |
| Xu et al. | An enhanced multipoint optimal minimum entropy deconvolution approach for bearing fault detection of spur gearbox | |
| CN110987433B (en) | Bearing fault early warning method based on high-frequency signal characteristic amplitude | |
| Xu et al. | Incipient fault diagnosis of planetary gearboxes based on an adaptive parameter-induced stochastic resonance method | |
| Chen et al. | A signal based triangular structuring element for mathematical morphological analysis and its application in rolling element bearing fault diagnosis | |
| Chen et al. | An adversarial learning framework for zero-shot fault recognition of mechanical systems | |
| Jiang et al. | A novel rolling-element bearing faults classification method combines lower-order moment spectra and support vector machine | |
| CN104281775B (en) | A kind of method for detecting weak signals that multistable stochastic resonance system is compensated based on parameter | |
| Luo et al. | Fault diagnosis of rolling element bearing using an adaptive multiscale enhanced combination gradient morphological filter | |
| Aijun et al. | A novel approach of impulsive signal extraction for early fault detection of rolling element bearing | |
| Gu et al. | Weak fault feature extraction of rolling element bearings based on ensemble tunable Q-factor wavelet transform and non-dominated negentropy | |
| Jia et al. | Study on Fault Feature Extraction of Rolling Bearing Based on Improved WOA‐FMD Algorithm | |
| CN115712822A (en) | Bearing fault diagnosis method, system and medium based on CEEMDAN-OMEDA | |
| Peng et al. | Cyclic band Box-Cox sparse measures based blind filtering and its application to bearing fault diagnosis | |
| Yuan et al. | Gearbox fault diagnosis of rolling mills using multiwavelet sliding window neighboring coefficient denoising and optimal blind deconvolution | |
| He et al. | Application of minimum entropy deconvolution on enhancement of gear tooth fault detection | |
| Li et al. | A new method of feature extraction for gearbox vibration signals |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20161207 |
|
| WD01 | Invention patent application deemed withdrawn after publication |