CN109946081A - A kind of method for diagnosing faults under variable speed when rolling bearing skidding - Google Patents
A kind of method for diagnosing faults under variable speed when rolling bearing skidding Download PDFInfo
- Publication number
- CN109946081A CN109946081A CN201910297473.5A CN201910297473A CN109946081A CN 109946081 A CN109946081 A CN 109946081A CN 201910297473 A CN201910297473 A CN 201910297473A CN 109946081 A CN109946081 A CN 109946081A
- Authority
- CN
- China
- Prior art keywords
- qhsi
- frequency band
- signal
- fault
- rolling bearing
- 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
Landscapes
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
A kind of method for diagnosing faults under variable speed when rolling bearing skidding, vibration acceleration sensor is adsorbed on the bearing block of tested rolling bearing, carry out high frequency sampling, obtain vibration signal, and utilize key phase synchronous acquisition key signal, by constructing tree-shaped filter group, bandpass filtering is carried out to original signal, then the envelope signal of narrow band signal is obtained by Hilbert transform, recycle envelope signal angularly resampling of the phase information to narrow band signal, finally construct quasi- harmonic wave screening index selection optimal demodulation frequency band, determine the presence or absence of failure and type, the present invention had both considered the military service feature that rolling bearing variable speed easily skids, random strongly disturbing influence is inhibited again, realize the accurate extraction of rolling bearing fault resonance bands.
Description
Technical field
The present invention relates to rolling bearing fault diagnosis technical fields, in particular to a kind of to beat for rolling bearing under variable speed
Method for diagnosing faults when sliding.
Background technique
Rolling bearing is a kind of most widely used universal machine component, its health status directly affects whole set equipment
Running quality is usually associated with weight huge economic loss even casualties, therefore, to rolling once rolling bearing breaks down
Dynamic bearing carries out health monitoring and fault diagnosis is of great significance.Recent decades, the rolling bearing fault based on vibration information
Diagnostic techniques has obtained tremendous development, is broadly divided into spectrum analysis technique, pulse shock diagnostic techniques, resonance demodulation technique and intelligence
Energy diagnostic techniques etc..Wherein, resonance demodulation technique has merged envelope spectrum analysis, to the earlier damages class failure such as rolling bearing into
Row diagnosis when it is more more effective than spectrum analysis and shock pulse technology, can detecte failure whether there is or not and fault type, to failure
Severity also has certain diagnosis capability, therefore is widely used.
The key of resonance demodulation technique is the determination of optimal frequency band, since kurtosis index is to the sensibility of impact, tradition
Spectrum kurtosis method the quality of filter result is measured using the kurtosis value size of filtered signal, by the maximum frequency band of kurtosis value
As resonance bands, but when failure is smaller, impact caused by failure is smaller, is easy by the random strong jamming in signal
It floods, the purpose of fault diagnosis is not achieved in error when causing to determine resonance bands using kurtosis index.
In order to overcome the problems referred above, humorous make an uproar of envelope is proposed for determining resonance bands than index, which can be well
Inhibit random strongly disturbing influence, retention periods component.But the index is insufficient to the sensibility of impact, especially when slight
Caused by failure when cyclical component finite energy, it is easy the cyclical component caused by non-faulting and floods.It makes an uproar in addition, envelope is humorous
Steady revolving speed is depended on than index, and when bearing is in variable speed operating condition, especially bearing generation slippery conditions, failure impact
Periodicity destroyed, this will lead to using envelope it is humorous make an uproar determine resonance bands than index when error, be equally difficult to reach therefore
Hinder the purpose of diagnosis
Summary of the invention
In order to overcome the disadvantages of the above prior art, the object of the present invention is to provide one kind for rolling under variable speed
Method for diagnosing faults when bearing skids, had not only considered the military service feature that rolling bearing variable speed easily skids, but also inhibited random
The accurate extraction of rolling bearing fault resonance bands is realized in strongly disturbing influence.
In order to achieve the above object, the technical scheme adopted by the invention is as follows:
A kind of method for diagnosing faults under variable speed when rolling bearing skidding, comprising the following steps:
Vibration acceleration sensor is adsorbed on the bearing block of rolling bearing by step 1, is carried out high frequency sampling, is shaken
Dynamic signal, and utilize key phase synchronous acquisition key signal；According to sample frequency fs and bearing revolving speed, interception a period of time
Interior vibration signal calculates revolving speed v (t) and phase ph (t) as original signal y (t), while using key signal；
Step 2 calculates each fault signature order of rolling bearing: outer ring fault signature order Ωo, inner ring fault signature
Order Ωi, rolling element fault signature order Ωb；Tree-shaped filter group is constructed, by tree-shaped filter group to original signal y (t)
Frequency band division is carried out, a series of narrow band signal y are obtainedi,j(t), yi,j(t) it is arranged by the i-th row jth corresponding in tree-shaped filter group
Finite impulse response (FIR) (finite impulse response, FIR) band-pass filter obtain, and obtain narrowband letter
Number yi,j(t) the phase ph corresponding toi,j(t), to narrow band signal yi,j(t) it carries out Hilbert transform and goes mean value, obtain envelope
Signal y_hi,j(t), according to narrow band signal yi,j(t) the phase ph corresponding toi,j(t) to envelope signal y_hi,j(t) isogonism is carried out
Resampling is spent, resampling narrow band signal ys is obtainedi,j(t), resampling narrow band signal ys is calculated separatelyi,j(t) kurtosis value Ki,jWith
Quasi- harmonic wave screening index QHSII, j(ΩQHSI)；
Step 3, according to ΩQHSIWith each fault signature order Ω of bearingo, Ωi, ΩbRelationship failure frequency band is divided
Class, if 0.97 Ωo< ΩQHSI1.03 Ω of <o, frequency band belongs to as outer ring fault message frequency band collection, if 0.97 Ωi<
ΩQHSI1.03 Ω of <i, frequency band belongs to as inner ring fault message frequency band collection, if 0.97 Ωb< ΩQHSI1.03 Ω of <b, frequency band
It belongs to as rolling element fault message frequency band collection, otherwise, QHSIi(ΩQHSI)=0, frequency band belong to fault-free information band collection；
Step 4 determines that whether each fault message frequency band integrates as empty set, if non-empty, for each fault message frequency band collection,
It selects the corresponding frequency band of QHSI maximum value as optimal demodulation frequency band, resonance and demodulation is carried out, if certain fault message frequency band collection is
Empty set, then corresponding parts of bearings fault-free, if each fault message frequency band collection is empty set, bearing fault-free.
In the step two, if Ki,j≤ 3, then enable QHSIi(ΩQHSIOtherwise)=0 is calculated by formula (1) and (2)
QHSII, j(ΩQHSI):
QHSI(ΩQHSI)=max (eY(Ω)), (2)
Wherein, M=ε * det Ω, det Ω indicates order resolution ratio, and ε indicates the fuzzy range of order, ε=5~7, X (Ω)
Indicate that order spectrum signal, N (Ω) indicate the estimation noise at order Ω.
The present invention compared with prior art, has the advantages that
A) present invention constructs quasi- harmonic wave screening index QHSI (ΩQHSI), in conjunction with Order Tracking, there can be effect
To variable speed lower bearing skidding bring quasi periodic problem, there is robustness.
B) present invention proposes to utilize the kurtosis value K of signaliScreen QHSIi(ΩQHSI), it is greatly improved computational efficiency.
C) present invention provides different optimal frequency bands for different faults, can effectively solve the problem that combined failure difficulty easy to cause missed diagnosis
Topic.
Detailed description of the invention
Fig. 1 is the flow chart of the method for the present invention.
Fig. 2 is the original signal schematic diagram of the embodiment of the present invention.
Fig. 3 is the tach signal schematic diagram of the embodiment of the present invention.
Fig. 4 is the tree-shaped filter group schematic diagram of building of the embodiment of the present invention.
Fig. 5 is the order spectrum signal X (Ω) and the estimation noise N (Ω) at order Ω of the embodiment of the present invention.
Fig. 6 is the QHSI that outer ring of embodiment of the present invention fault message frequency band is concentrated.
Fig. 7 is the corresponding optimal frequency band of QHSI maximum value that the outer ring fault message frequency band of the embodiment of the present invention is concentrated.
Fig. 8 is the spectrum of envelope order corresponding to the optimal frequency band of outer ring of embodiment of the present invention fault message frequency band concentration.
Fig. 9 is the QHSI that the present invention implements that inner ring fault message frequency band is concentrated.
Figure 10 is the corresponding optimal frequency band of QHSI maximum value that inner ring of embodiment of the present invention fault message frequency band is concentrated.
Figure 11 is the spectrum of envelope order corresponding to the optimal frequency band of inner ring of embodiment of the present invention fault message frequency band concentration.
Figure 12 is the optimal frequency band that the embodiment of the present invention is determined based on kurtosis index.
Figure 13 is the envelope order spectrum that the embodiment of the present invention determines optimal frequency band based on kurtosis index.
Figure 14 is the embodiment of the present invention based on the humorous optimal frequency band determined than index of making an uproar of envelope.
Figure 15 determines that the envelope order of optimal frequency band is composed than index based on humorous make an uproar of envelope for the embodiment of the present invention.
Specific embodiment
The present invention will be described in detail with reference to the accompanying drawings and examples.
By taking the locomotive rolling bearing fault detection testing stand of certain rolling stock section as an example, bearing fault position at outer ring/inner ring,
Fault type is to peel off.Bearing design parameter: certain type angular contact ball bearing, the pitch diameter of bearing are 183.929mm, bearing roller
Number be 19, bearing roller radius be 26mm, 10 ° of contact angle.
As shown in Figure 1, a kind of method for diagnosing faults under variable speed when rolling bearing skidding, comprising the following steps:
Vibration acceleration sensor is adsorbed on the bearing block of tested rolling bearing by step 1, carries out high frequency sampling,
Vibration signal is obtained, and utilizes key phase synchronous acquisition key signal；According to sample frequency fs and bearing revolving speed, interception one
Vibration signal in the section time is as original signal y (t), as shown in Figure 2；Revolving speed v (t) and phase are calculated using key signal simultaneously
Position ph (t), revolving speed v (t) signal are as shown in Figure 3；
Step 2 calculates each fault signature order of rolling bearing: outer ring fault signature order Ωo, inner ring fault signature
Order Ωi, rolling element fault signature order Ωb, as shown in table 1；Tree-shaped filter group is constructed, as shown in figure 4, passing through tree-shaped filter
Wave device group carries out frequency band division to original signal y (t), obtains a series of narrow band signal yi,j(t), yi,j(t) by tree-shaped filter
Finite impulse response (FIR) (finite impulse response, FIR) band-pass filter of corresponding i-th row jth column in group
It obtains, and obtains narrow band signal yi,j(t) the phase ph corresponding toi,j(t), to narrow band signal yi,j(t) Hilbert change is carried out
Mean value is changed and gone, envelope signal y_h is obtainedi,j(t), according to narrow band signal yi,j(t) the phase ph corresponding toi,j(t) envelope is believed
Number y_hi,j(t) angularly resampling is carried out, resampling narrow band signal ys is obtainedi,j(t), resampling narrow band signal is calculated separately
ysi,j(t) kurtosis value Ki,jWith quasi- harmonic wave screening index QHSII, j(ΩQHSI), if Ki,j≤ 3, then enable QHSIi(ΩQHSI)
=0, otherwise QHSI is calculated by formula (1) and (2)I, j(ΩQHSI):
QHSI(ΩQHSI)=max (eY(Ω)), (2)
Wherein, M=ε * det Ω, det Ω indicates order resolution ratio, and ε indicates the fuzzy range of order, generally ε=5~
7, X (Ω) indicate that order spectrum signal, N (Ω) indicate the estimation noise at order Ω, as shown in Figure 5；
1 bearing fault characteristics order of table
Step 3, according to ΩQHSIWith each fault signature order Ω of bearingo, ΩiAnd ΩbRelationship failure frequency band is divided
Class, if 0.97 Ωo< ΩQHSI1.03 Ω of <o, frequency band belongs to as outer ring fault message frequency band collection, if 0.97 Ωi<
ΩQHSI1.03 Ω of <i, frequency band belongs to as inner ring fault message frequency band collection, if 0.97 Ωb< ΩQHSI1.03 Ω of <b, frequency band
It belongs to as rolling element fault message frequency band collection, otherwise, QHSIi(ΩQHSI)=0, frequency band belong to fault-free information band collection；
Step 4 determines that whether each failure frequency band integrates as empty set, if non-empty, for each fault message frequency band collection, selection
The corresponding frequency band of QHSI maximum value is as optimal demodulation frequency band, when in outer ring, fault message frequency band concentrates QHSI to be maximized
ΩQHSI=8.04 ∈ [0.97 Ωo,1.03Ωo], as shown in fig. 6, QHSI maximum value corresponding optimal frequency band such as Fig. 7 institute in Fig. 6
Show, JieDuHuaYu II Decoction band optimal in Fig. 7 is selected to carry out resonance and demodulation, obtained envelope order spectrum is as shown in Figure 8, it can be seen that obvious
Outer ring fault signature；The Ω when inner ring fault message frequency band concentrates QHSI to be maximizedQHSI=11.01 ∈ [0.97 Ωi,1.03
Ωi], as shown in figure 9, the corresponding optimal frequency band of QHSI is as shown in Figure 10 at this time, JieDuHuaYu II Decoction band optimal in Figure 10 is selected to carry out
Resonance and demodulation, obtained envelope order spectrum are as shown in figure 11, it can be seen that apparent inner ring fault signature；And in rolling element failure
Information band concentrates element-free, shows rolling element fault-free.
The optimal frequency band that traditional kurtosis (K) index determines is as shown in figure 12, the envelope order spectrum of optimal frequency band in Figure 12
As shown in figure 13, humorous, in Figure 14 envelope order spectrum more as shown in figure 14 than the resonance bands that (HNR) index determines of making an uproar of traditional envelope
As shown in figure 15, fail to find outer ring/inner ring fault signature.
A kind of method for diagnosing faults under variable speed when rolling bearing skidding proposed by the present invention, it is contemplated that variable speed
With rolling bearing skid influence, overcome traditional kurtosis index and it is humorous make an uproar than the defect of index, be accurately extracted failure spy
Reference breath, has carried out efficient diagnosis to housing washer/inner ring Weak fault, has had good robustness.
Claims (2)
1. a kind of method for diagnosing faults under variable speed when rolling bearing skidding, which comprises the following steps:
Vibration acceleration sensor is adsorbed on the bearing block of rolling bearing by step 1, carries out high frequency sampling, obtains vibration letter
Number, and utilize key phase synchronous acquisition key signal；According to sample frequency fs and bearing revolving speed, intercept in a period of time
Vibration signal calculates revolving speed v (t) and phase ph (t) as original signal y (t), while using key signal；
Step 2 calculates each fault signature order of rolling bearing: outer ring fault signature order Ωo, inner ring fault signature order
Ωi, rolling element fault signature order Ωb；Tree-shaped filter group is constructed, original signal y (t) is carried out by tree-shaped filter group
Frequency band divides, and obtains a series of narrow band signal yi,j(t), yi,j(t) having by the i-th row jth corresponding in tree-shaped filter group column
Limiting impulse response (finite impulse response, FIR), band-pass filter obtains, and obtains narrow band signal yi,j
(t) the phase ph corresponding toi,j(t), to narrow band signal yi,j(t) it carries out Hilbert transform and goes mean value, obtain envelope signal
y_hi,j(t), according to narrow band signal yi,j(t) the phase ph corresponding toi,j(t) to envelope signal y_hi,j(t) it is angularly weighed
Sampling, obtains resampling narrow band signal ysi,j(t), resampling narrow band signal ys is calculated separatelyi,j(t) kurtosis value Ki,jIt is humorous with standard
Wave screening index QHSII, j(ΩQHSI)；
Step 3, according to ΩQHSIWith each fault signature order Ω of bearingo, Ωi, ΩbRelationship failure frequency band is classified, such as
0.97 Ω of fruito< ΩQHSI1.03 Ω of <o, frequency band belongs to as outer ring fault message frequency band collection, if 0.97 Ωi< ΩQHSI<
1.03Ωi, frequency band belongs to as inner ring fault message frequency band collection, if 0.97 Ωb< ΩQHSI1.03 Ω of <b, frequency band belongs to
For rolling element fault message frequency band collection, otherwise, QHSIi(ΩQHSI)=0, frequency band belong to fault-free information band collection；
Step 4 determines that whether each fault message frequency band integrates as empty set, if non-empty, for each fault message frequency band collection, selection
The corresponding frequency band of QHSI maximum value carries out resonance and demodulation as optimal demodulation frequency band, if certain fault message frequency band integrates as empty set,
Then corresponding parts of bearings fault-free, if each fault message frequency band collection is empty set, bearing fault-free.
2. a kind of method for diagnosing faults under variable speed when rolling bearing skidding according to claim 1, feature
It is: in the step two, if Ki,j≤ 3, then enable QHSIi(ΩQHSIOtherwise)=0 calculates QHSI by formula (1) and (2)I, j
(ΩQHSI):
QHSI(ΩQHSI)=max (eY(Ω)), (2)
Wherein, M=ε * det Ω, det Ω indicates order resolution ratio, and ε indicates the fuzzy range of order, and ε=5~7, X (Ω) is indicated
Order spectrum signal, N (Ω) indicate the estimation noise at order Ω.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910297473.5A CN109946081B (en) | 2019-04-15 | 2019-04-15 | Fault diagnosis method for rolling bearing slipping under variable rotating speed |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910297473.5A CN109946081B (en) | 2019-04-15 | 2019-04-15 | Fault diagnosis method for rolling bearing slipping under variable rotating speed |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109946081A true CN109946081A (en) | 2019-06-28 |
| CN109946081B CN109946081B (en) | 2020-09-22 |
Family
ID=67015081
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910297473.5A Active CN109946081B (en) | 2019-04-15 | 2019-04-15 | Fault diagnosis method for rolling bearing slipping under variable rotating speed |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109946081B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111024397A (en) * | 2019-12-20 | 2020-04-17 | 北京航空航天大学 | Rolling bearing slip rate evaluation method based on vibration information demodulation analysis |
| CN111238813A (en) * | 2020-01-19 | 2020-06-05 | 西安交通大学 | Method for extracting fault features of rolling bearing under strong interference |
| CN111521400A (en) * | 2020-06-24 | 2020-08-11 | 北京航空航天大学 | Bearing early fault diagnosis method based on EDM and spectral kurtosis |
| CN113033304A (en) * | 2021-02-18 | 2021-06-25 | 北京科技大学 | Multi-resonance-band amplitude demodulation analysis method for overcoming frequency domain overlapping interference |
| CN114738389A (en) * | 2022-03-29 | 2022-07-12 | 南京航空航天大学 | Intelligent bearing system for slip diagnosis and slip diagnosis prediction method |
| CN117589456A (en) * | 2024-01-19 | 2024-02-23 | 中国航发四川燃气涡轮研究院 | Rolling bearing variable rotation speed fault diagnosis method for enhancing sparse decomposition |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102706555A (en) * | 2012-01-05 | 2012-10-03 | 重庆大学 | Complex analytic optimal wavelet demodulation method |
| CN105181019A (en) * | 2015-09-15 | 2015-12-23 | 安徽精科检测技术有限公司 | Computer program product for early fault early-warning and analysis of rotation type machine |
| US20160069775A1 (en) * | 2013-04-05 | 2016-03-10 | Aktiebolaget Skf | Method, computer program product & system |
| CN107036815A (en) * | 2015-10-20 | 2017-08-11 | 斯凯孚公司 | The method and data processing equipment of Severity are carried out to bearing defect |
| CN107941510A (en) * | 2017-10-19 | 2018-04-20 | 西安交通大学 | Extracting method based on the angularly Rolling Bearing Fault Character of dual sampling |
| CN108801634A (en) * | 2018-03-21 | 2018-11-13 | 昆明理工大学 | The method and its application of bearing fault characteristics frequency are extracted based on singular value decomposition and the frequency band entropy of optimization |
| CN109668733A (en) * | 2018-12-21 | 2019-04-23 | 苏州大学 | Variation nonlinear model decomposes variable speed Method for Bearing Fault Diagnosis |
-
2019
- 2019-04-15 CN CN201910297473.5A patent/CN109946081B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102706555A (en) * | 2012-01-05 | 2012-10-03 | 重庆大学 | Complex analytic optimal wavelet demodulation method |
| US20160069775A1 (en) * | 2013-04-05 | 2016-03-10 | Aktiebolaget Skf | Method, computer program product & system |
| CN105181019A (en) * | 2015-09-15 | 2015-12-23 | 安徽精科检测技术有限公司 | Computer program product for early fault early-warning and analysis of rotation type machine |
| CN107036815A (en) * | 2015-10-20 | 2017-08-11 | 斯凯孚公司 | The method and data processing equipment of Severity are carried out to bearing defect |
| CN107941510A (en) * | 2017-10-19 | 2018-04-20 | 西安交通大学 | Extracting method based on the angularly Rolling Bearing Fault Character of dual sampling |
| CN108801634A (en) * | 2018-03-21 | 2018-11-13 | 昆明理工大学 | The method and its application of bearing fault characteristics frequency are extracted based on singular value decomposition and the frequency band entropy of optimization |
| CN109668733A (en) * | 2018-12-21 | 2019-04-23 | 苏州大学 | Variation nonlinear model decomposes variable speed Method for Bearing Fault Diagnosis |
Non-Patent Citations (2)
| Title |
|---|
| MIAO YONGHAO: "Harmonics-to-noise ratio guided deconvolution and its application for bearing fault detection", 《PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE》 * |
| 陈伟: "行星齿轮箱振动信号特征提取与故障诊断研究", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111024397A (en) * | 2019-12-20 | 2020-04-17 | 北京航空航天大学 | Rolling bearing slip rate evaluation method based on vibration information demodulation analysis |
| CN111024397B (en) * | 2019-12-20 | 2021-05-07 | 北京航空航天大学 | Rolling bearing slip rate evaluation method based on vibration information demodulation analysis |
| CN111238813A (en) * | 2020-01-19 | 2020-06-05 | 西安交通大学 | Method for extracting fault features of rolling bearing under strong interference |
| CN111238813B (en) * | 2020-01-19 | 2021-05-07 | 西安交通大学 | Method for extracting fault features of rolling bearing under strong interference |
| CN111521400A (en) * | 2020-06-24 | 2020-08-11 | 北京航空航天大学 | Bearing early fault diagnosis method based on EDM and spectral kurtosis |
| CN111521400B (en) * | 2020-06-24 | 2021-06-29 | 北京航空航天大学 | Bearing early fault diagnosis method based on EDM and spectral kurtosis |
| CN113033304A (en) * | 2021-02-18 | 2021-06-25 | 北京科技大学 | Multi-resonance-band amplitude demodulation analysis method for overcoming frequency domain overlapping interference |
| CN113033304B (en) * | 2021-02-18 | 2021-10-22 | 北京科技大学 | Multi-resonance-band amplitude demodulation analysis method for overcoming frequency domain overlapping interference |
| CN114738389A (en) * | 2022-03-29 | 2022-07-12 | 南京航空航天大学 | Intelligent bearing system for slip diagnosis and slip diagnosis prediction method |
| CN117589456A (en) * | 2024-01-19 | 2024-02-23 | 中国航发四川燃气涡轮研究院 | Rolling bearing variable rotation speed fault diagnosis method for enhancing sparse decomposition |
| CN117589456B (en) * | 2024-01-19 | 2024-04-02 | 中国航发四川燃气涡轮研究院 | Rolling bearing variable rotation speed fault diagnosis method for enhancing sparse decomposition |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109946081B (en) | 2020-09-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109946081A (en) | A kind of method for diagnosing faults under variable speed when rolling bearing skidding | |
| CN108195587B (en) | Motor rolling bearing fault diagnosis method and system | |
| CN107356432B (en) | Fault Diagnosis of Roller Bearings based on frequency domain window experience small echo resonance and demodulation | |
| CN107941510B (en) | Extracting method based on the angularly Rolling Bearing Fault Character of dual sampling | |
| CN107525674B (en) | Turn frequency estimation method and detection device based on crestal line probability distribution and localised waving | |
| CN109932179A (en) | A kind of rolling bearing fault testing method based on the reconstruct of DS Adaptive spectra | |
| CN104215456B (en) | Plane clustering and frequency-domain compressed sensing reconstruction based mechanical fault diagnosis method | |
| CN105865794B (en) | Engine fire fault diagnosis method based on short time discrete Fourier transform and principal component analysis | |
| CN106441896A (en) | Characteristic vector extraction method for rolling bearing fault mode identification and state monitoring | |
| CN109855874B (en) | Random resonance filter for enhancing detection of weak signals in vibration assisted by sound | |
| CN110160765A (en) | A kind of shock characteristic recognition methods and system based on sound or vibration signal | |
| CN104132250A (en) | Pipeline leakage feature vector extraction method based on improved wavelet packet | |
| Wang et al. | Sparse and low-rank decomposition of the time–frequency representation for bearing fault diagnosis under variable speed conditions | |
| CN101403684A (en) | Diagnosis method for combined failure of rotating machinery based on blind source analysis technology | |
| CN108195584B (en) | A kind of Fault Diagnosis of Roller Bearings based on accuracy spectrogram | |
| CN108760310A (en) | Accidental resonance Fault Diagnosis of Roller Bearings based on novel signal-to-noise ratio index | |
| CN106226078A (en) | A kind of Doppler based on microphone array distorts the bearing calibration of acoustic signal | |
| CN108444704A (en) | A kind of Fault Diagnosis of Rolling Element Bearings method | |
| Hu et al. | Compound fault diagnosis of rolling bearings based on improved tunable Q-factor wavelet transform | |
| CN105388012A (en) | Order tracking method based on nonlinear frequency modulation wavelet transformation | |
| CN102928225A (en) | Self-adaptive resonance demodulation method for fault diagnosis of rolling bearing | |
| CN110163190A (en) | A kind of Fault Diagnosis of Roller Bearings and device | |
| CN109030849A (en) | The monitoring method and device of compressor rotating stall | |
| CN112364706A (en) | Small sample bearing fault diagnosis method based on class imbalance | |
| Wang et al. | Information interval spectrum: a novel methodology for rolling-element bearing diagnosis |
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 |