CN111881736A - Rolling bearing early fault diagnosis method based on bandwidth Fourier decomposition - Google Patents

Rolling bearing early fault diagnosis method based on bandwidth Fourier decomposition Download PDF

Info

Publication number
CN111881736A
CN111881736A CN202010560719.6A CN202010560719A CN111881736A CN 111881736 A CN111881736 A CN 111881736A CN 202010560719 A CN202010560719 A CN 202010560719A CN 111881736 A CN111881736 A CN 111881736A
Authority
CN
China
Prior art keywords
bandwidth
signal
fault
rolling bearing
frequency
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
Application number
CN202010560719.6A
Other languages
Chinese (zh)
Inventor
邓艾东
邓敏强
朱静
史曜炜
刘洋
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Southeast University
Original Assignee
Southeast University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Southeast University filed Critical Southeast University
Priority to CN202010560719.6A priority Critical patent/CN111881736A/en
Publication of CN111881736A publication Critical patent/CN111881736A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/04—Bearings
    • G01M13/045—Acoustic or vibration analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
    • 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)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Data Mining & Analysis (AREA)
  • Pure & Applied Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Acoustics & Sound (AREA)
  • Artificial Intelligence (AREA)
  • Algebra (AREA)
  • Signal Processing (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Abstract

The invention relates to a rolling bearing early fault diagnosis method based on bandwidth Fourier decomposition. After the original vibration signal is decomposed into a bandwidth modal function, an envelope spectrum of the bandwidth modal function is solved through Hilbert transformation, and a fault characteristic frequency ratio of the bandwidth modal function is calculated. And finally, selecting a bandwidth modal function with the maximum fault characteristic frequency ratio and analyzing the envelope spectrum characteristic of the bandwidth modal function so as to realize effective diagnosis of the early fault of the rolling bearing. The method has the characteristics of high calculation speed and high fault identification precision, and can be effectively applied to diagnosis of early weak faults of the rolling bearing.

Description

Rolling bearing early fault diagnosis method based on bandwidth Fourier decomposition
Technical Field
The invention relates to a rolling bearing early fault diagnosis method based on bandwidth Fourier decomposition, and belongs to the technical field of measurement.
Background
The rolling bearing is one of the most widely used parts in industrial production and plays a role of a joint in a rotary machine. Due to the effects of alternating load, abrasion, chemical erosion and the like, the rolling bearing is one of the most prone parts to failure, and effective diagnosis of early failure of the rolling bearing has important practical significance for timely finding and eliminating potential safety hazards of equipment and improving reliability and economy of operation. At present, a large amount of research work is carried out on the fault diagnosis problem of the rolling bearing by scholars at home and abroad, and then deep research is not yet carried out on the effective diagnosis problem of early weak faults in the natural degradation process of the rolling bearing.
Disclosure of Invention
The invention aims to solve the problems and provides a rolling bearing early fault diagnosis method based on bandwidth Fourier decomposition, which can improve the rolling bearing early fault diagnosis efficiency and identification precision and meet the requirements of engineering application.
In order to achieve the purpose, the technical scheme adopted by the invention for solving the technical problems is as follows:
a rolling bearing early fault diagnosis method based on bandwidth Fourier decomposition comprises the following steps:
the first step is as follows: adaptively decomposing the original vibration signal into a bandwidth modal function through bandwidth Fourier decomposition;
the second step is that: solving the envelope spectrum of each bandwidth mode function through Hilbert transformation, and calculating fault characteristic frequency ratios corresponding to different fault types;
the third step: selecting a bandwidth modal function with the largest fault characteristic frequency ratio as effective components corresponding to different fault types, and realizing effective diagnosis of early weak faults of the rolling bearing through envelope analysis of the effective components;
as a further preferred embodiment of the present invention, in the first step, the objective of the bandwidth fourier decomposition is to transform the time domain signal F (t) into the frequency domain signal F (ω) by fourier transform, and then adaptively decompose the frequency domain signal F (t) into K sparse signals of { ω { m } in the frequency domainkIs equal toNarrowband sub-signal of heart { Uk(ω) }, then inverse Fourier transform will { U }k(ω) } into a bandwidth mode function uk(t) }. Wherein, the sparsity of the narrow-band sub-signal is realized by optimizing the total bandwidth thereof, and the constraint optimization problem L of the structure thereof0({Uk(ω)},{ωk}) is:
Figure BDA0002546195080000011
Figure BDA0002546195080000012
constrained optimization problem L by Lagrange multiplier method0({Uk(ω)},{ωk}) into an unconstrained extremum solving problem L1({Uk(ω)},{ωk})：
Figure BDA0002546195080000021
By making a pair L1({Uk(ω)},{ωkAnd) solving a saddle point to obtain an optimized decomposition result of the frequency domain signal F (omega). Wherein, UkThe explicit solution of (ω) is:
Figure BDA0002546195080000022
narrow band signal center frequency omegakThe implicit solution of (c) is:
Figure BDA0002546195080000023
as a further preferred embodiment of the invention, in the first step the bandwidth Fourier decomposition is performed by applying a Fourier transform to the centre frequency { omega } of the narrowband signalkIterative optimization of time domain signal f (t) is decomposed into K bandwidth mode functions { u }k(t), the specific calculation flow is as follows:
(1) converting the time domain signal F (t) into a frequency domain signal F (ω) by fourier transformation;
(2) narrow band sub-signal U in Nyquist frequency rangek(ω) } center frequency ωkInitializing a circuit in a power spectrum | F (ω) < >2At the maximum of;
(3) according to
Figure BDA0002546195080000024
Calculating a narrowband sub-signal { Uk(ω) } analytic solution;
(4) according to
Figure BDA0002546195080000025
Iteratively updating tape signals { U }k(ω) } center frequency ωk}；
(5) Repeating the step (3) and the step (4) until the center frequency { omega }kConverge to a stable value
(6) By inverse Fourier transforming the frequency domain signal { Uk(ω) } into a time-domain signal { u }k(t)}。
As a further preferred aspect of the present invention, in the first step, the number K of center frequencies is determined according to a condition whether a component obtained by superimposing two adjacent bandwidth mode functions satisfies a narrowband signal, and the specific method includes:
(1) let K be 2;
(2) performing a bandwidth Fourier decomposition on the original signal f (t);
(3) judgment of
Figure BDA0002546195080000031
If not, executing the step (4); if yes, indicating that excessive decomposition occurs, and enabling K to be K-1 to finish circulation;
(4) making K equal to K +1, performing step (2);
as a further preferred embodiment of the present invention, in the second step, the calculation formula of the fault characteristic frequency ratio FCFR of the bandwidth modal function envelope spectrum is:
Figure BDA0002546195080000032
wherein: f. ofcDetermining the fault characteristic frequency by the structural parameters and the operating rotating speed of the bearing; y (omega) is the envelope spectrum of each bandwidth mode function. The FCFR represents the proportion of fault features, and in order to reduce the influence of noise, the FCFR in a frequency range which is 3 times of the fault features in an envelope spectrum is only calculated.
As a further preferable aspect of the present invention, in the third step: the selected effective components are respectively corresponding to the faults of each type, and the unknown faults of the bearing are determined through envelope analysis of the effective components.
Compared with the prior art, the invention has the following beneficial effects:
the invention has the characteristics of high calculation speed and high diagnosis precision;
the invention can effectively extract weak fault characteristic information in the vibration signal;
the invention can accurately identify the early failure of the rolling bearing in the natural degradation process.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a simulated signal time domain diagram of a preferred embodiment of the present invention;
FIG. 2 is a simulated signal Fourier spectrum of a preferred embodiment of the present invention;
FIG. 3 is a simulation signal bandwidth Fourier decomposition result of the preferred embodiment of the present invention;
FIG. 4 is a spectrum of the envelope of the active component in the result of a simulated signal bandwidth Fourier decomposition according to a preferred embodiment of the present invention;
FIG. 5 is a schematic illustration of a test stand of a preferred embodiment of the present invention;
FIG. 6 is a root mean square variation trend of the vibration signal during bearing degradation in accordance with a preferred embodiment of the present invention;
FIG. 7 is a time domain plot of the vibration signal of the rolling bearing of the preferred embodiment of the present invention;
FIG. 8 is a time domain diagram of the bandwidth Fourier decomposition result of the vibration signal of the rolling bearing according to the preferred embodiment of the invention;
FIG. 9 is a spectrum of the envelope of the active component in the result of the broad Fourier decomposition of the vibration signal in accordance with the preferred embodiment of the present invention;
FIG. 10 is a spectrum of the envelope of the active component of the result of the empirical mode decomposition of a vibration signal according to the preferred embodiment of the present invention;
FIG. 11 is the envelope spectrum of the effective component in the result of the vibration signal variational modal decomposition according to the preferred embodiment of the present invention;
fig. 12 is a flow chart of the present invention.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic views illustrating only the basic structure of the present invention in a schematic manner, and thus show only the constitution related to the present invention.
As shown in fig. 12, the method for diagnosing the early failure of the rolling bearing based on the bandwidth fourier decomposition of the present invention includes the following steps:
the first step is as follows: adaptively decomposing the original vibration signal into a bandwidth modal function through bandwidth Fourier decomposition;
the objective of the bandwidth fourier decomposition is to transform the time domain signal F (t) into the frequency domain signal F (ω) by fourier transform, and then adaptively decompose the signal F (t) into K sparse signals in the frequency domain, such as { ω }kThe narrow band sub-signal centered { U }k(ω) }, then inverse Fourier transform will { U }k(ω) } into a bandwidth mode function uk(t) }. Wherein, the sparsity of the narrow-band sub-signal is realized by optimizing the total bandwidth thereof, and the constraint optimization problem L of the structure thereof0({Uk(ω)},{ωk}) is:
Figure BDA0002546195080000041
Figure BDA0002546195080000042
constrained optimization problem L by Lagrange multiplier method0({Uk(ω)},{ωk}) to unconstrained solutionProblem of extreme value L1({Uk(ω)},{ωk})：
Figure BDA0002546195080000043
By making a pair L1({Uk(ω)},{ωkAnd) solving a saddle point to obtain an optimized decomposition result of the frequency domain signal F (omega). Wherein, UkThe explicit solution of (ω) is:
Figure BDA0002546195080000044
narrow band signal center frequency omegakThe implicit solution of (c) is:
the bandwidth Fourier decomposition is carried out by carrying out the Fourier decomposition on the center frequency [ omega ] of the narrowband sub-signalkIterative optimization of time domain signal f (t) is decomposed into K bandwidth mode functions { u }k(t), the specific calculation flow is as follows:
(1) converting the time domain signal F (t) into a frequency domain signal F (ω) by fourier transformation;
(2) narrow band sub-signal U in Nyquist frequency rangek(ω) } center frequency ωkInitializing a circuit in a power spectrum | F (ω) < >2At the maximum of;
(3) according to
Figure BDA0002546195080000052
Calculating a narrowband sub-signal { Uk(ω) } analytic solution;
(4) according to
Figure BDA0002546195080000053
Iteratively updating tape signals { U }k(ω) } center frequency ωk}；
(5) Repeating the step (3) and the step (4) until the center frequency { omega }kConverge to a stable value
(6) Passing roundInverse Fourier transform frequency domain signal { U }k(ω) } into a time-domain signal { u }k(t)}。
The number K of the center frequencies is determined according to the condition whether the narrowband signal is satisfied after two adjacent bandwidth mode functions are superposed into one component, and the specific method comprises the following steps:
(1) let K be 2;
(2) performing a bandwidth Fourier decomposition on the original signal f (t);
(3) judgment of
Figure BDA0002546195080000054
If not, executing the step (4); if yes, indicating that excessive decomposition occurs, and enabling K to be K-1 to finish circulation;
(4) making K equal to K +1, performing step (2);
as a further preferred embodiment of the present invention, in the second step, the calculation formula of the fault characteristic frequency ratio FCFR of the bandwidth modal function envelope spectrum is:
Figure BDA0002546195080000055
wherein: f. ofcDetermining the fault characteristic frequency by the structural parameters and the operating rotating speed of the bearing; t (omega) is the envelope spectrum of each bandwidth mode function. The FCFR represents the proportion of fault features, and in order to reduce the influence of noise, the FCFR in a frequency range which is 3 times of the fault features in an envelope spectrum is only calculated.
The second step is that: solving the envelope spectrum of each bandwidth mode function through Hilbert transformation, and calculating fault characteristic frequency ratios corresponding to different types of faults;
the third step: selecting a bandwidth modal function with the maximum fault characteristic frequency ratio as an effective component corresponding to each fault type, and realizing effective diagnosis of the rolling bearing fault through envelope analysis of each effective component;
the selected effective components are the effective components corresponding to the fault types respectively, and the unknown fault of the bearing is determined through envelope analysis of the effective components.
Finally, simulation signal and experimental signal analysis are carried out
Simulated signal analysis
The simulation signal of the local fault of the rolling bearing is as follows:
x(t)＝x1(t)+x2(t)+n(t)
x1(t)＝Alcos(2πf0t)
Figure BDA0002546195080000061
S(t)＝e-tsin(2πfREt)
wherein x is1(t) represents power frequency vibration caused by bearing imbalance, Al0.1 is the amplitude of the power frequency vibration, f0The frequency is converted when the frequency is 10 Hz; x is the number of2(t) represents an impact due to a local failure, Ci0.3 is the amplitude of the impact, Tf0.025s is the period of fault impact, the attenuation coefficient of the impact signal is 200, fRE2400Hz is the natural vibration frequency of the system; n (t) is Gaussian white noise, and the signal-to-noise ratio of the simulation signal is-10 dB. Fig. 1 and 2 are a time domain graph and a fourier spectrum, respectively, of a simulated signal, wherein the sampling frequency of the simulated signal is 9600Hz and the simulation time is 1 s. As can be seen from fig. 1, the impact caused by the local fault is already submerged in a strong noise environment, but the natural frequency of the system is still clearly observed in the fourier spectrum of fig. 2.
Fig. 3(a) and 3(b) are respectively a bandwidth mode function time domain diagram obtained by the bandwidth fourier decomposition of the simulation signal and a fourier spectrum corresponding to the bandwidth mode function time domain diagram. Table 1 shows the fault characteristic frequency ratio calculated after the envelope spectrum is calculated by hilbert transform for each bandwidth mode function. As can be seen from the calculation results in Table 1, BMF5The envelope spectrum of (a) has a relatively large proportion of fault characteristic frequencies, and fig. 4 is the envelope spectrum thereof. As can be seen from FIG. 4, BMF5The obvious fault characteristic frequency (40Hz) and frequency doubling components thereof appear in the envelope spectrum, namely the fault characteristic frequency of the simulation signal can be effectively extracted.
TABLE 1
Figure BDA0002546195080000062
Analysis of Experimental signals
Experimental analysis data was provided by the institute of design science and basic components of the university of sienna traffic, with a test stand layout as shown in fig. 5. The bearing test bed comprises an alternating current induction motor, a motor speed controller, a supporting shaft, two supporting bearings (heavy roller bearings), a hydraulic loading system and the like. The test bed is designed for carrying out accelerated degradation tests on the rolling bearing under different working conditions. The radial force is generated by a hydraulic loading system and is applied to a shell of the tested bearing, and the rotating speed is controlled by a speed controller of an alternating current induction motor. The test bearing is an LDK UER204 type rolling bearing, and the structural parameters of the test bearing are shown in Table 2. And the 2 nd group test duration under the 1 st working condition is 161min, and the abrasion of the outer ring of the rolling bearing is found after the test is finished. And table 3 shows the fault characteristic frequencies corresponding to different fault types of the rolling bearing when the frequency conversion is 35 Hz.
TABLE 2
Figure BDA0002546195080000071
TABLE 3
Figure BDA0002546195080000072
As shown in fig. 6, when the test was performed to the 32 th min, the root mean square of the vertical direction vibration signal started to rise slowly, i.e., the bearing started to enter the early failure stage. Fig. 7a) and 7b) are time domain waveform diagrams of the vibration of the bearing in the vertical direction when the test time is 1min and the test time is 32min respectively. As can be seen from fig. 7, when the bearing just enters the early failure stage, the vibration amplitude is increased to a certain extent compared with that in the absence of the failure, and because the failure characteristic signal is weak, no obvious impact can be observed in the time domain diagram, that is, it is difficult to directly identify the early failure from the time domain diagram.
Fig. 8 shows a time domain diagram of a bandwidth mode function obtained by bandwidth fourier decomposition in two states of no fault and early fault. Table 4 shows the absence of faults and the early stageAnd comparing the fault characteristic frequency of the envelope spectrum of each bandwidth mode function in two states of the fault. As can be seen from the calculation results in Table 4, BMF was found in the no-fault state1The proportion of fault characteristic frequency in the envelope spectrum is relatively large; in early failure state, BMF7Containing considerably more fault information. FIG. 9 shows BMF under no fault conditions1And BMF in early failure state7The envelope spectrum of (a). As can be seen from FIG. 9, the effective component BMF in the no-fault state1Has no obvious prominent component in envelope spectrum, and has effective component BMF in early failure state7The envelope spectrum of (1) shows a clear fault characteristic frequency (107Hz) and its frequency multiplication components. The invention can effectively extract the early fault characteristics and realize the effective diagnosis of the early fault of the rolling bearing.
TABLE 4
Figure BDA0002546195080000081
In contrast, fig. 10 and 11 show the envelope spectra obtained by empirical mode decomposition and variation mode decomposition, respectively. As can be seen from fig. 10, although the fault characteristic frequency can be found in the effective component envelope spectrum of the EMD decomposition result, there are many interference spectral lines around the effective component envelope spectrum, so that the interference spectral lines are not prominent in the envelope spectrum, which affects the effective diagnosis of the fault to some extent, i.e. the effect of identifying the early fault type by the EMD method is not ideal. BIMF in FIG. 116The frequency multiplication of 2 and the frequency multiplication of 3 of the fault characteristic frequency in the envelope spectrum are more prominent, but the fault characteristic frequency with the maximum fault diagnosis significance is not prominent, namely the early fault characteristic frequency cannot be effectively extracted through the VMD.
It should be noted that although the type of fault and the number of faults may be unknown during actual operation, the characteristic frequencies of different faults can be calculated according to the structure and the operation parameters. And respectively calculating the FCFRs of the different types of faults to select corresponding effective components, and realizing effective diagnosis of the different types of faults through envelope analysis. In the simulation signal analysis and test analysis part, in order to facilitate the description of the effectiveness and superiority of the fault feature extraction of the invention, only the FCFR corresponding to the known fault and the envelope spectrum of the effective component of the FCFR are provided, and the effective diagnosis of the unknown fault can be realized according to the invention.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The meaning of "and/or" as used herein is intended to include both the individual components or both.
The term "connected" as used herein may mean either a direct connection between components or an indirect connection between components via other components.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.

Claims (6)

1. A rolling bearing early fault diagnosis method based on bandwidth Fourier decomposition is characterized in that: the method comprises the following steps:
the first step is as follows: adaptively decomposing the original vibration signal into a bandwidth modal function through bandwidth Fourier decomposition;
the second step is that: solving the envelope spectrum of each bandwidth mode function through Hilbert transformation, and calculating Fault Characteristic Frequency Ratios (FCFR) corresponding to different fault types;
the third step: and selecting a bandwidth modal function with the maximum fault characteristic frequency ratio as effective components corresponding to different fault types, and realizing effective diagnosis of the early weak fault of the rolling bearing by envelope analysis of each effective component.
2. The method for diagnosing the early failure of the rolling bearing based on the bandwidth Fourier decomposition as claimed in claim 1, wherein the method comprises the following steps: in the first step, the objective of the bandwidth fourier decomposition is to transform the time domain signal F (t) into the frequency domain signal F (ω) by fourier transform, and then adaptively decompose the frequency domain signal F (t) into K sparse signals in the frequency domain, such as { ω }kThe narrow band sub-signal centered { U }k(ω) }, then inverse Fourier transform will { U }k(ω) } into a bandwidth mode function uk(t) }, in which sparsity of the narrowband sub-signal is achieved by optimizing its total bandwidth, a constrained optimization problem L of its construction0({Uk(ω)},{ωk}) is:
Figure FDA0002546195070000011
s.t.
Figure FDA0002546195070000012
constrained optimization problem L by Lagrange multiplier method0({Uk(ω)},{ωk}) into an unconstrained extremum solving problem L1({Uk(ω)},{ωk})：
Figure FDA0002546195070000013
By making a pair L1({Uk(ω)},{ωkAnd) solving a saddle point to obtain an optimized decomposition result of the frequency domain signal F (omega). Wherein, UkThe explicit solution of (ω) is:
Figure FDA0002546195070000014
k＝1,2,...K
narrow band signal center frequency omegakThe implicit solution of (c) is:
Figure FDA0002546195070000021
3. the bandwidth fourier decomposition-based early fault diagnosis method for a rolling bearing according to claim 2, wherein: in the first step, the bandwidth Fourier decomposition is performed by applying a Fourier transform to the center frequency { omega } of the narrowband signalkIterative optimization of time domain signal f (t) is decomposed into K bandwidth mode functions { u }k(t), the specific calculation flow is as follows:
(1) converting the time domain signal F (t) into a frequency domain signal F (ω) by fourier transformation;
(2) narrow band sub-signal U in Nyquist frequency rangek(ω) } center frequency ωkInitializing a circuit in a power spectrum | F (ω) < >2At the maximum of;
(3) according to
Figure FDA0002546195070000022
Calculating a narrowband sub-signal { Uk(ω) } analytic solution;
(4) according to
Figure FDA0002546195070000023
Iteratively updating tape signals { U }k(ω) } center frequency ωk}；
(5) Repeating the step (3) and the step (4) until the center frequency { omega }kConverge to a stable value;
(6) converting the frequency domain signal { U ] through inverse Fourier transformk(ω) } into a time-domain signal { u }k(t)}。
4. The bandwidth fourier decomposition-based early fault diagnosis method for a rolling bearing according to claim 2, wherein: in the first step, the number K of center frequencies is determined according to the condition whether a component is superimposed as a component by two adjacent bandwidth mode functions and satisfies a narrowband signal, and the specific method is as follows:
(1) let K be 2;
(2) performing a bandwidth Fourier decomposition on the original signal f (t);
(3) judgment of
Figure FDA0002546195070000024
If not, executing the step (4); if yes, indicating that excessive decomposition occurs, and enabling K to be K-1 to finish circulation;
(4) and (3) making K equal to K +1, and executing the step (2).
5. The method for diagnosing the early failure of the rolling bearing based on the bandwidth Fourier decomposition as claimed in claim 1, wherein the method comprises the following steps: in the second step, the calculation formula of the fault characteristic frequency ratio FCFR of the bandwidth modal function envelope spectrum is as follows:
Figure FDA0002546195070000031
wherein: f. ofcDetermining the fault characteristic frequency by the structural parameters and the operating rotating speed of the bearing; y (omega) is the envelope spectrum of each bandwidth mode function.
6. The method for diagnosing the early failure of the rolling bearing based on the bandwidth Fourier decomposition as claimed in claim 1, wherein the method comprises the following steps: in the third step: the selected effective components are the effective components corresponding to the fault types respectively, and the unknown fault of the bearing is determined through envelope analysis of the effective components.
CN202010560719.6A 2020-06-18 2020-06-18 Rolling bearing early fault diagnosis method based on bandwidth Fourier decomposition Pending CN111881736A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010560719.6A CN111881736A (en) 2020-06-18 2020-06-18 Rolling bearing early fault diagnosis method based on bandwidth Fourier decomposition

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010560719.6A CN111881736A (en) 2020-06-18 2020-06-18 Rolling bearing early fault diagnosis method based on bandwidth Fourier decomposition

Publications (1)

Publication Number Publication Date
CN111881736A true CN111881736A (en) 2020-11-03

Family

ID=73157618

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010560719.6A Pending CN111881736A (en) 2020-06-18 2020-06-18 Rolling bearing early fault diagnosis method based on bandwidth Fourier decomposition

Country Status (1)

Country Link
CN (1) CN111881736A (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112539933A (en) * 2020-12-16 2021-03-23 东南大学 Gear box fault diagnosis method based on resonance baseband bandwidth Fourier decomposition
CN112734001A (en) * 2020-12-09 2021-04-30 东南大学 Wind power transmission chain intelligent fault diagnosis method based on order spectrum migration
CN113758708A (en) * 2021-09-14 2021-12-07 北京化工大学 Frequency domain fault diagnosis method of rolling bearing signal based on L1 norm and group norm constraint
CN114018571A (en) * 2021-10-28 2022-02-08 国能太仓发电有限公司 Gear box fault diagnosis method based on frequency-induced variation modal decomposition
CN114441172A (en) * 2021-12-28 2022-05-06 东南大学 Rolling bearing fault vibration signal analysis method
CN117970105A (en) * 2024-03-28 2024-05-03 浙江大学 Early fault diagnosis method and system for motor bearing based on signal fusion

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102840907A (en) * 2012-09-18 2012-12-26 河南省电力公司电力科学研究院 Rolling bearing vibration signal characteristic extracting and analyzing method under early fault state
CN103335841A (en) * 2013-06-04 2013-10-02 辽宁大学 An antifriction bearing fault diagnosis method employing a pulse wavelet energy spectrum analysis
CN108444709A (en) * 2018-04-18 2018-08-24 北京信息科技大学 The Fault Diagnosis of Roller Bearings that VMD is combined with FastICA

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102840907A (en) * 2012-09-18 2012-12-26 河南省电力公司电力科学研究院 Rolling bearing vibration signal characteristic extracting and analyzing method under early fault state
CN103335841A (en) * 2013-06-04 2013-10-02 辽宁大学 An antifriction bearing fault diagnosis method employing a pulse wavelet energy spectrum analysis
CN108444709A (en) * 2018-04-18 2018-08-24 北京信息科技大学 The Fault Diagnosis of Roller Bearings that VMD is combined with FastICA

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
MINQIANG DENG等: "Bandwidth Fourier decomposition and its application in incipient fault identification of rolling bearings", 《MEASUREMENT SCIENCE AND TECHNOLOGY》 *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112734001A (en) * 2020-12-09 2021-04-30 东南大学 Wind power transmission chain intelligent fault diagnosis method based on order spectrum migration
CN112539933A (en) * 2020-12-16 2021-03-23 东南大学 Gear box fault diagnosis method based on resonance baseband bandwidth Fourier decomposition
CN113758708A (en) * 2021-09-14 2021-12-07 北京化工大学 Frequency domain fault diagnosis method of rolling bearing signal based on L1 norm and group norm constraint
CN113758708B (en) * 2021-09-14 2024-03-26 北京化工大学 Rolling bearing signal frequency domain fault diagnosis method based on L1 norm and group norm constraint
CN114018571A (en) * 2021-10-28 2022-02-08 国能太仓发电有限公司 Gear box fault diagnosis method based on frequency-induced variation modal decomposition
CN114441172A (en) * 2021-12-28 2022-05-06 东南大学 Rolling bearing fault vibration signal analysis method
CN114441172B (en) * 2021-12-28 2023-12-15 东南大学 Rolling bearing fault vibration signal analysis method
CN117970105A (en) * 2024-03-28 2024-05-03 浙江大学 Early fault diagnosis method and system for motor bearing based on signal fusion

Similar Documents

Publication Publication Date Title
CN111881736A (en) Rolling bearing early fault diagnosis method based on bandwidth Fourier decomposition
Bin et al. Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network
Chen et al. Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals
Cui et al. Double-dictionary matching pursuit for fault extent evaluation of rolling bearing based on the Lempel-Ziv complexity
Wang et al. Dual-enhanced sparse decomposition for wind turbine gearbox fault diagnosis
WO2020078132A1 (en) Motor bearing failure diagnosis device
He et al. Multifractal entropy based adaptive multiwavelet construction and its application for mechanical compound-fault diagnosis
Wang et al. Rolling element bearing fault diagnosis via fault characteristic order (FCO) analysis
Hong et al. A novel vibration-based fault diagnostic algorithm for gearboxes under speed fluctuations without rotational speed measurement
CN109655266B (en) Wind turbine generator bearing fault diagnosis method based on AVMD and spectrum correlation analysis
CN109883706A (en) A kind of rolling bearing local damage Weak fault feature extracting method
Chen et al. Integrated early fault diagnosis method based on direct fast iterative filtering decomposition and effective weighted sparseness kurtosis to rolling bearings
Li et al. Multipoint optimal minimum entropy deconvolution adjusted for automatic fault diagnosis of hoist bearing
He et al. A data-driven group-sparse feature extraction method for fault detection of wind turbine transmission system
CN113375939A (en) Mechanical part fault diagnosis method based on SVD and VMD
Huang et al. An improved empirical wavelet transform method for rolling bearing fault diagnosis
CN115096581B (en) Complex transmission device fault diagnosis tracing method and system based on time-frequency domain characteristics
Mauricio et al. Vibration-based condition monitoring of helicopter gearboxes based on cyclostationary analysis
Berntsen et al. Enhanced demodulation band selection based on Operational Modal Analysis (OMA) for bearing diagnostics
Zhao et al. Research on constructing a degradation index and predicting the remaining useful life for rolling element bearings of complex equipment
CN111307426A (en) Rotating machinery fault feature extraction method based on FrFT-EWT principle
Wei et al. Fault diagnosis of bearings in multiple working conditions based on adaptive time-varying parameters short-time Fourier synchronous squeeze transform
Chen et al. A time-varying instantaneous frequency fault features extraction method of rolling bearing under variable speed
CN107490477B (en) The Fault Diagnosis of Gear Case method compared based on frequency spectrum kernel density function correlation
CN111220388B (en) Rolling bearing fault diagnosis method based on time domain analysis

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
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20201103