CN114218979A - Method for constructing weighted joint lifting envelope spectrum based on local features of spectral coherence - Google Patents

Method for constructing weighted joint lifting envelope spectrum based on local features of spectral coherence Download PDF

Info

Publication number
CN114218979A
CN114218979A CN202111363661.7A CN202111363661A CN114218979A CN 114218979 A CN114218979 A CN 114218979A CN 202111363661 A CN202111363661 A CN 202111363661A CN 114218979 A CN114218979 A CN 114218979A
Authority
CN
China
Prior art keywords
frequency
spectrum
spectral
alpha
fault
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
Application number
CN202111363661.7A
Other languages
Chinese (zh)
Other versions
CN114218979B (en
Inventor
程尧
张凡
陈丙炎
张卫华
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.)
Southwest Jiaotong University
Original Assignee
Southwest Jiaotong 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 Southwest Jiaotong University filed Critical Southwest Jiaotong University
Priority to CN202111363661.7A priority Critical patent/CN114218979B/en
Publication of CN114218979A publication Critical patent/CN114218979A/en
Application granted granted Critical
Publication of CN114218979B publication Critical patent/CN114218979B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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
    • G06F2218/02—Preprocessing
    • G06F2218/04—Denoising
    • 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
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Marketing (AREA)
  • Strategic Management (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Acoustics & Sound (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Signal Processing (AREA)
  • Artificial Intelligence (AREA)
  • Manufacturing & Machinery (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Abstract

The invention discloses a method for constructing a weighted joint lifting envelope spectrum based on local features of spectral coherence, which specifically comprises the following steps: calculating the spectral coherence of the actually measured signal; identifying a bearing candidate fault frequency based on the spectral coherent local features; dividing spectrum coherent spectrum frequency bands by using 1/3-binary tree, and quantizing fault information of each spectrum frequency narrow band by using the identified candidate fault frequency; and constructing a weighted combined lifting envelope spectrum WCIES, selecting a narrow-band lifting envelope spectrum IES with diagnosis information in each decomposition layer, constructing a combined lifting envelope spectrum CIES, and then carrying out weighted average on the CIES to obtain the WCIES. The method has the advantages that the bearing fault information distributed in different narrow bands can be fully integrated, and the method does not depend on the nominal fault period information; the method can effectively extract the local defect fault characteristic information of the rotary machine, and can be used for early fault diagnosis of the rotary machine.

Description

Method for constructing weighted joint lifting envelope spectrum based on local features of spectral coherence
Technical Field
The invention belongs to the field of health monitoring of rotating machinery structures, and particularly relates to a method for building weighted combined lifting envelope spectrum based on local features of spectrum coherence.
Background
Rolling bearings are one of the key precision mechanical parts of rotary machines, which are susceptible to axial loads, radial loads, impact loads and various external excitations under severe working conditions, with the risk of inducing structural fatigue damage internally. Failure to implement an appropriate maintenance strategy in a timely manner can easily cause failure of the mechanical system, potentially resulting in significant economic loss. Therefore, early failure detection of rolling bearings is essential to ensure safety and reliability of the railway vehicle.
The local damage region periodically passes through the bearing carrying area to excite a series of transient impacts, which are expressed as second-order cyclostationary characteristics in the vibration signal. The spectrum coherent energy can map the vibration signal to a two-dimensional frequency-frequency domain consisting of a frequency spectrum frequency and a cycle frequency, and is a typical method for revealing the second-order cyclostationary feature of the vibration signal of the rotating machine. The method for identifying the bearing fault is an effective method for identifying the bearing fault based on the full-spectrum frequency band information, but is easily polluted by broadband noise, in this case, the narrow-band frequency spectrum narrow band containing fault information is identified, and then the method for constructing the spectrum analysis tool is the key for diagnosing the weak fault of the bearing. Wang et al screens a spectrum band with a fixed bandwidth based on a maximized L2/L1 norm, generates a lifting envelope spectrum for normal coherent mode integration in a narrow-band spectrum band, and realizes fault identification of a rolling bearing (WANG Dong, ZHAO Xuejun, KOU Lin-Lin, et al. evaluation and fast identification for generating enhanced/square enhanced spectrum from spectral coherence for detecting fault diagnosis [ J ]. Mechanical Systems and Signal Processing,2019,122: 754-. Mauricio et al propose IESFOgram, divide the frequency Spectrum band into different narrow bands by using 1/3-binary tree filter, select information narrow band based on Fault Characteristic Frequency (FCF) to generate IES, and realize fault identification of rolling bearing under different conditions (Mauricio A, Smith W A, Randall R B, et al.
The methods provide ideas for identifying the narrow frequency band of the information spectrum of the SCoh, but when the fault information is distributed in a plurality of narrow bands, the fault information is easy to miss. Therefore, Mauricio et al constructs a weight function about spectrum frequency based on FCF on The basis of IESFOgram, and performs weighted integration on SCoh modulus along spectrum frequency axis to obtain Combined IES (Mauricio A, Griylias K. cyclic-based Multi band Envelope spectrum Extraction for bearing diagnostics: The Combined Improved Envelope Spectra, mechanical Systems and Signal Processing,2021,149: 107150.). However, small fluctuations in shaft speed near the nominal speed may adversely affect these FCF-based methods, even resulting in poor fault detection capabilities. Therefore, when the rotating speed information is unknown or inaccurate, how to utilize the characteristics of SCoh, and how to construct a spectrum analysis tool independent of sparse indexes and FCF to realize the weak fault detection of the bearing is very critical.
Disclosure of Invention
The invention provides a method for building weighted combined lifting envelope spectrum based on local features of spectrum coherence, aiming at solving the problem that weak fault features of a train axle box bearing are difficult to extract in a wide frequency band and based on the second-order cyclostationarity of fault signals.
The invention discloses a method for constructing a weighted joint lifting envelope spectrum based on local characteristics of spectral coherence, which comprises the following steps:
step 1: the spectral coherence of the measured signal is calculated.
Actually measured rotary mechanical vibration signal x (t)n),tn＝n/FsN-0, 1, …, the spectral correlation SC of N-1 being defined as:
Figure BDA0003359790320000021
in the formula: k ═ 2N +1) FsN is the signal length, FsIs the sampling frequency of the signal; alpha is the cycle frequency; f is frequencyA spectral frequency; r (t)n,τm) Is x (t)n) Instantaneous autocorrelation function of, tn＝n/FsFor the sampling instant, τmIs a delay factor. Spectral coherence is a normalized version of spectral correlation defined as follows:
Figure BDA0003359790320000022
estimating the spectral coherence of the signal by selecting a proper numerical calculation method to obtain gamma (alpha)n,fm)，αnIs a discrete cycle frequency; f. ofmAre discrete spectral frequencies.
Step 2: candidate failure frequencies are identified based on the spectral coherent local features.
Firstly, noise reduction processing is carried out on spectral coherence based on median filtering:
Figure BDA0003359790320000023
in the formula: mean () stands for median filtering; operator [ ·]+Setting all numbers less than zero to zero; delta (n) ═ alphan－kΔα,αn+kΔα]Is at alphanThe central neighborhood is delta alpha, the resolution of the cycle frequency is delta alpha, and the value of the parameter k is 5-50.
Then, for an arbitrary fixed spectral frequency fmDefining:
Figure BDA0003359790320000024
in the formula: the parameter L determines the cyclic frequency slice
Figure BDA0003359790320000025
Sparsity of local maxima of the modes of (a); for any cycle frequency of alphanDefining a spectral frequency slice gamma (alpha)nThe number of local maxima on η (n) then:
Figure BDA0003359790320000026
in the formula: m is the number of discrete spectral frequencies; g is the number of discrete cycle frequencies; alpha is alphanThe value of η (n) tends to be larger for frequencies associated with bearing failure; for this purpose, η (n) is sorted by size to obtain
Figure BDA0003359790320000031
If it is not
Figure BDA0003359790320000032
Then there is kiAnd ki+1So that omega (k)i)>Ω(ki+1) Is formed in which
Figure BDA0003359790320000033
And
Figure BDA0003359790320000034
the function Ω (-) is defined as follows:
Figure BDA0003359790320000035
before selection, of maximum D
Figure BDA0003359790320000036
Cycle frequency used
Figure BDA0003359790320000037
D is a candidate failure frequency, and there are:
Figure BDA0003359790320000038
and reasonably setting the parameter D to ensure that the candidate fault frequency mainly contains frequency components related to the bearing fault.
And step 3: and (4) based on 1/3-spectrum coherent spectrum band division of a binary tree, and quantizing the fault information of each spectrum frequency narrow band by using the identified candidate fault frequency.
Dividing the frequency spectrum by using an 1/3-binary tree filter bank to obtain frequency spectrum frequency narrow bands with different center frequencies and bandwidths; the kth, k of the l, l-0, 1,1.6,2,2.6,3, … layer is 1, …,2lA narrow band Bl,k＝[Fs·(k-1)/2l +1,Fs·k/2l+1]Center frequency fc ═ Fs·(2k-1)/2l+1Bandwidth Bw ═ Fs/2l+1(ii) a Based on narrow band Bl,kCalculating narrow-band lifting envelope spectrum IESl,k(α)：
Figure BDA0003359790320000039
Next, define IESl,k(α) energy at CFFs vs. entire IESl,kEnergy ratio ER of (. alpha.)l,kAs diagnostic indicators:
Figure BDA00033597903200000310
ERl,kquantizes the frequency spectrum narrow band Bl,kIncluded second order cyclostationary feature information related to the fault; ERl,kLarger, indicating a narrow band Bl,kThe more fault information that is contained.
And 4, step 4: constructing a weighted combined lifting envelope spectrum WCIES, firstly selecting a narrow-band lifting envelope spectrum IES with diagnosis information in each decomposition layer to construct a combined lifting envelope spectrum CIES, and then carrying out weighted average on the CIES to obtain the WCIES, wherein the WCIES is specifically as follows:
(1) computing all IES's on layer ll,k(α),k＝1,…,2lMaximum value ER of the diagnostic index of (1)l,max：
ERl,max＝max{ERl,k,k＝1,...,2l} (10)
Let SKl＝Rs·ERl,max(0<Rs<1) Set as a threshold value as identifying value in layer lThe basis of IES.
(2) The identified valuable IES are averaged over each number of decomposition layers to yield a CIES:
Figure BDA00033597903200000311
in the formula: h (l) is the number of IES selected at layer l; exemplary function I { ERl,k≥SKlValue 0 or 1 only, and only in ERl,k≥SKlAnd when true, takes a value of 1.
(3) Finally, the CIES of each decomposition layerl(α) performing a weighted summation to obtain WCIES:
Figure BDA0003359790320000041
in the formula: nlevel is the maximum number of decomposition layers; w (l) is a weight function of the l-th layer, from CIESl(alpha) a diagnostic index ERlDetermining:
Figure BDA0003359790320000042
preferably, the algorithm for estimating the spectral coherence of the signal in step 1 is the Fast SC algorithm.
Preferably, the value of the parameter k in the step 2 is not more than N/10.
Preferably, the value of the parameter L in the step 2 is 3-10.
Preferably, when the parameter D in step 3 is too small, a sufficient frequency related to the fault cannot be identified; when D is too large, too many failure-independent frequencies are introduced in the CFFs; therefore, by setting the parameter D reasonably to ensure that the CFFs mainly contain frequency components related to bearing faults, considering that the fault signature frequency and the number of harmonics thereof are roughly proportional to the maximum cycle frequency, the parameter D is set as follows:
D＝p·αmax (14)
in the formula:αmaxthe maximum cycle frequency; p is a ratio parameter and is in the range of [0.01,0.1 ]]An internal value.
Preferably, the value of the parameter Rs in the step 4 is 0.5.
The beneficial technical effects of the invention are as follows:
the method has the advantages that the bearing fault information distributed in different narrow bands can be fully integrated, and the method does not depend on the nominal fault period information. The method can effectively extract the local defect fault characteristic information of the rotary machine, and can be used for early fault diagnosis of the rotary machine.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a spectral coherence diagram of the measured signal in the present invention.
Fig. 3 is a schematic diagram of candidate fault signature frequencies in the present invention.
Fig. 4 shows the joint lifting envelope spectrum CIES of the various decomposition layers in the present invention.
Fig. 5 is a weighted joint lifting envelope spectrum WCIES in the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and the detailed description.
The method for building weighted combined lifting envelope spectrum based on local features of spectrum coherence is shown in fig. 1, and based on spectrum coherence/spectrum coherence of an actually measured signal, discrete spectrum frequency containing fault feature information is screened by using local peak value distribution information of a cyclic frequency spectrum slice, and then an enhanced envelope spectrum is built through an integral operator to identify rotary machine faults. The method specifically comprises the following steps:
step 1: and collecting vibration acceleration signals of the rotating machine by using sensing equipment, and calculating the spectrum coherence of the actually measured signals.
Actually measured rotary mechanical vibration signal x (t)n),tn＝n/FsN-0, 1, …, the spectral correlation SC of N-1 being defined as:
Figure BDA0003359790320000051
in the formula: k ═ 2N +1) FsN is the signal length, FsIs the sampling frequency of the signal; alpha is the cycle frequency; f is the frequency spectrum; r (t)n,τm) Is x (t)n) Instantaneous autocorrelation function of, tn＝n/FsFor the sampling instant, τmIs a delay factor. Spectral coherence is a normalized version of spectral correlation defined as follows:
Figure BDA0003359790320000052
estimating the spectral coherence of the signal by selecting a suitable numerical calculation method (such as a fast spectral correlation algorithm, but not limited to the fast spectral correlation algorithm) to obtain gamma (alpha)n,fm)，αnIs a discrete cycle frequency; f. ofmAre discrete spectral frequencies. The spectral coherence map of the measured signal is shown in figure 2.
Step 2: candidate failure frequencies are identified based on the spectral coherent local features.
Firstly, noise reduction processing is carried out on spectral coherence based on median filtering:
Figure BDA0003359790320000053
in the formula: mean () stands for median filtering; operator [ ·]+Setting all numbers less than zero to zero; delta (n) ═ alphan－kΔα,αn+kΔα]Is at alphanThe central neighborhood is delta alpha, the resolution of the cycle frequency is delta alpha, and the value of the parameter k is 5-50.
Then, for an arbitrary fixed spectral frequency fmDefining:
Figure BDA0003359790320000054
in the formula: the parameter L determines the cyclic frequency slice
Figure BDA0003359790320000055
Sparsity of local maxima of the modes of (a); for any cycle frequency of alphanDefining a spectral frequency slice gamma (alpha)nThe number of local maxima on η (n) then:
Figure BDA0003359790320000056
in the formula: m is the number of discrete spectral frequencies; g is the number of discrete cycle frequencies; alpha is alphanThe value of η (n) tends to be larger for frequencies associated with bearing failure; for this purpose, η (n) is sorted by size to obtain
Figure BDA0003359790320000057
If it is not
Figure BDA0003359790320000058
Then there is kiAnd ki+1So that omega (k)i)>Ω(ki+1) Is formed in which
Figure BDA0003359790320000059
And
Figure BDA00033597903200000510
the function Ω (-) is defined as follows:
Figure BDA0003359790320000061
before selection, of maximum D
Figure BDA0003359790320000062
Cycle frequency used
Figure BDA0003359790320000063
D is a candidate failure frequency, and there are:
Figure BDA0003359790320000064
and reasonably setting the parameter D to ensure that the candidate fault frequency mainly contains frequency components related to the bearing fault.
The identified candidate fault signature frequencies are shown in fig. 3.
And step 3: and (4) based on 1/3-spectrum coherent spectrum band division of a binary tree, and quantizing the fault information of each spectrum frequency narrow band by using the identified candidate fault frequency.
Dividing the frequency spectrum by using an 1/3-binary tree filter bank to obtain frequency spectrum frequency narrow bands with different center frequencies and bandwidths; the kth, k of the l, l-0, 1,1.6,2,2.6,3, … layer is 1, …,2lA narrow band Bl,k＝[Fs·(k-1)/2l +1,Fs·k/2l+1]Center frequency fc ═ Fs·(2k-1)/2l+1Bandwidth Bw ═ Fs/2l+1(ii) a Based on narrow band Bl,kCalculating narrow-band lifting envelope spectrum IESl,k(α)：
Figure BDA0003359790320000065
Next, define IESl,k(α) energy at CFFs vs. entire IESl,kEnergy ratio ER of (. alpha.)l,kAs diagnostic indicators:
Figure BDA0003359790320000066
ERl,kquantizes the frequency spectrum narrow band Bl,kIncluded second order cyclostationary feature information related to the fault; ERl,kLarger, indicating a narrow band Bl,kThe more fault information that is contained.
And 4, step 4: constructing a weighted combined lifting envelope spectrum WCIES, firstly selecting a narrow-band lifting envelope spectrum IES with diagnosis information in each decomposition layer to construct a combined lifting envelope spectrum CIES, and then carrying out weighted average on the CIES to obtain the WCIES, wherein the WCIES is specifically as follows:
(1) Computing all IES's on layer ll,k(α),k＝1,…,2lMaximum value ER of the diagnostic index of (1)l,max：
ERl,max＝max{ERl,k,k＝1,...,2l} (10)
Let SKl＝Rs·ERl,max(0<Rs<1) Set as a threshold as a basis for identifying valuable IES in level i.
(2) The identified valuable IES are averaged over each number of decomposition layers to yield CIES (as shown in fig. 4):
Figure BDA0003359790320000067
in the formula: h (l) is the number of IES selected at layer l; exemplary function I { ERl,k≥SKlValue 0 or 1 only, and only in ERl,k≥SKlAnd when true, takes a value of 1.
(3) Finally, the CIES of each decomposition layerl(α) weighted sum to get WCIES (as shown in fig. 5):
Figure BDA0003359790320000068
in the formula: nlevel is the maximum number of decomposition layers; w (l) is a weight function of the l-th layer, from CIESl(alpha) a diagnostic index ERlDetermining:
Figure BDA0003359790320000071
finally, a rotating machine fault is identified.

Claims (6)

1. A method for constructing a weighted joint lifting envelope spectrum based on local features of spectral coherence is characterized by comprising the following steps:
step 1: calculating the spectral coherence of the actually measured signal;
actually measured rotating machinery vibration signal x (tn),tn＝n/FsN-0, 1, …, the spectral correlation SC of N-1 being defined as:
Figure FDA0003359790310000011
in the formula: k ═ 2N +1) FsN is the signal length, FsIs the sampling frequency of the signal; alpha is the cycle frequency; f is the frequency spectrum; r (t)n,τm) Is x (t)n) Instantaneous autocorrelation function of, tn＝n/FsFor the sampling instant, τmIs a delay factor; spectral coherence is a normalized version of spectral correlation defined as follows:
Figure FDA0003359790310000012
estimating the spectral coherence of the signal by selecting a proper numerical calculation method to obtain gamma (alpha)n,fm)，αnIs a discrete cycle frequency; f. ofmIs a discrete spectral frequency;
step 2: identifying candidate fault frequencies based on the spectral coherent local features;
firstly, noise reduction processing is carried out on spectral coherence based on median filtering:
Figure FDA0003359790310000013
in the formula: mean () stands for median filtering; operator [ ·]+Setting all numbers less than zero to zero; delta (n) ═ alphan－kΔα,αn+kΔα]Is at alphanThe neighborhood is the center, delta alpha is the resolution of the cycle frequency, and the value of the parameter k is 5-50;
then, for an arbitrary fixed spectral frequency fmDefining:
Figure FDA0003359790310000014
in the formula: the parameter L determines the cyclic frequency slice
Figure FDA0003359790310000015
Sparsity of local maxima of the modes of (a); for any cycle frequency of alphanDefining a spectral frequency slice gamma (alpha)nThe number of local maxima on η (n) then:
Figure FDA0003359790310000016
in the formula: m is the number of discrete spectral frequencies; g is the number of discrete cycle frequencies; alpha is alphanThe value of η (n) tends to be larger for frequencies associated with bearing failure; for this purpose, η (n) is sorted by size to obtain
Figure FDA0003359790310000017
If it is not
Figure FDA0003359790310000018
Then there is kiAnd ki+1So that omega (k)i)>Ω(ki+1) Is formed in which
Figure FDA0003359790310000019
And
Figure FDA00033597903100000110
the function Ω (-) is defined as follows:
Figure FDA0003359790310000021
before selection, of maximum D
Figure FDA0003359790310000022
Cycle frequency used
Figure FDA0003359790310000023
D is a candidate failure frequency, and there are:
Figure FDA0003359790310000024
by reasonably setting the parameter D, the candidate fault frequency is ensured to mainly contain frequency components related to bearing faults;
and step 3: based on 1/3-spectrum coherent spectrum band division of the binary tree, and quantizing fault information of each spectrum frequency narrow band by using the identified candidate fault frequency;
dividing the frequency spectrum by using an 1/3-binary tree filter bank to obtain frequency spectrum frequency narrow bands with different center frequencies and bandwidths; the kth, k of the l, l-0, 1,1.6,2,2.6,3, … layer is 1, …,2lA narrow band Bl,k＝[Fs·(k-1)/2l+1,Fs·k/2l+1]Center frequency fc ═ Fs·(2k-1)/2l+1Bandwidth Bw ═ Fs/2l+1(ii) a Based on narrow band Bl,kCalculating narrow-band lifting envelope spectrum IESl,k(α)：
Figure FDA0003359790310000025
Next, define IESl,k(α) energy at CFFs vs. entire IESl,kEnergy ratio ER of (. alpha.)l,kAs diagnostic indicators:
Figure FDA0003359790310000026
ERl,kquantizes the frequency spectrum narrow band Bl,kIncluded second order cyclostationary feature information related to the fault; ERl,kLarger, indicating a narrow band Bl,kThe more fault information that is contained;
and 4, step 4: constructing a weighted combined lifting envelope spectrum WCIES, firstly selecting a narrow-band lifting envelope spectrum IES with diagnosis information in each decomposition layer to construct a combined lifting envelope spectrum CIES, and then carrying out weighted average on the CIES to obtain the WCIES, wherein the WCIES is specifically as follows:
(1) computing all IES's on layer ll,k(α),k＝1,…,2lMaximum value ER of the diagnostic index of (1)l,max：
ERl,max＝max{ERl,k,k＝1,...,2l} (10)
Let SKl＝Rs·ERl,max(0<Rs<1) Set as a threshold as a basis for identifying valuable IES in level i;
(2) the identified valuable IES are averaged over each number of decomposition layers to yield a CIES:
Figure FDA0003359790310000027
in the formula: h (l) is the number of IES selected at layer l; exemplary function I { ERl,k≥SKlValue 0 or 1 only, and only in ERl,k≥SKlIf the result is true, the value is 1;
(3) finally, the CIES of each decomposition layerl(α) performing a weighted summation to obtain WCIES:
Figure FDA0003359790310000028
in the formula: nlevel is the maximum number of decomposition layers; w (l) is a weight function of the l-th layer, from CIESl(alpha) a diagnostic index ERlDetermining:
Figure FDA0003359790310000031
2. the method for constructing a weighted joint lifting envelope spectrum based on the local features of spectral coherence according to claim 1, wherein the algorithm for estimating the spectral coherence of the signal in step 1 is Fast SC algorithm.
3. The method for constructing the weighted joint lifting envelope spectrum based on the local features of the spectral coherence according to claim 1, wherein the value of the parameter k in the step 2 is not more than N/10.
4. The method for constructing the weighted joint lifting envelope spectrum based on the local features of the spectral coherence according to claim 1, wherein a value of a parameter L in the step 2 is 3-10.
5. The method for constructing a weighted joint lifting envelope spectrum based on the local features of spectral coherence as claimed in claim 1, wherein if the parameter D in step 3 is too small, it will result in that a sufficient number of frequencies related to faults cannot be identified; when D is too large, too many failure-independent frequencies are introduced in the CFFs; therefore, by setting the parameter D reasonably to ensure that the CFFs mainly contain frequency components related to bearing faults, considering that the fault signature frequency and the number of harmonics thereof are roughly proportional to the maximum cycle frequency, the parameter D is set as follows:
D＝p·αmax (14)
in the formula: alpha is alphamaxThe maximum cycle frequency; p is a ratio parameter and is in the range of [0.01,0.1 ]]An internal value.
6. The method for constructing the weighted joint lifting envelope spectrum based on the local features of the spectral coherence according to claim 1, wherein a value of the parameter Rs in the step 4 is 0.5.
CN202111363661.7A 2021-11-17 2021-11-17 Method for constructing weighted joint lifting envelope spectrum based on local features of spectral coherence Active CN114218979B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111363661.7A CN114218979B (en) 2021-11-17 2021-11-17 Method for constructing weighted joint lifting envelope spectrum based on local features of spectral coherence

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111363661.7A CN114218979B (en) 2021-11-17 2021-11-17 Method for constructing weighted joint lifting envelope spectrum based on local features of spectral coherence

Publications (2)

Publication Number Publication Date
CN114218979A true CN114218979A (en) 2022-03-22
CN114218979B CN114218979B (en) 2023-04-07

Family

ID=80697428

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111363661.7A Active CN114218979B (en) 2021-11-17 2021-11-17 Method for constructing weighted joint lifting envelope spectrum based on local features of spectral coherence

Country Status (1)

Country Link
CN (1) CN114218979B (en)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102982517A (en) * 2012-11-02 2013-03-20 武汉大学 Remote-sensing image fusion method based on local correlation of light spectrum and space
CN104236908A (en) * 2014-09-23 2014-12-24 石家庄铁道大学 Combined slicing bearing fault diagnosis method on basis of MID (modulation intensity distribution) algorithm
CN107063428A (en) * 2017-04-19 2017-08-18 广东电网有限责任公司电力科学研究院 The fault characteristic frequency display methods and device of a kind of on-line vibration monitoring system
CA2991725A1 (en) * 2018-01-10 2019-07-10 Psi Technologies Inc. Method and apparatus to predict failure and control vibrations in a subsurface artificial lift system
CN111797567A (en) * 2020-06-09 2020-10-20 合肥工业大学 Deep learning network-based bearing fault classification method and system
CN112113767A (en) * 2020-09-29 2020-12-22 昆明理工大学 Bearing fault diagnosis method based on proportional band selection criterion
CN112487882A (en) * 2020-11-13 2021-03-12 西南交通大学 Method for generating non-sparse index-guided enhanced envelope spectrum based on spectrum coherence

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102982517A (en) * 2012-11-02 2013-03-20 武汉大学 Remote-sensing image fusion method based on local correlation of light spectrum and space
CN104236908A (en) * 2014-09-23 2014-12-24 石家庄铁道大学 Combined slicing bearing fault diagnosis method on basis of MID (modulation intensity distribution) algorithm
CN107063428A (en) * 2017-04-19 2017-08-18 广东电网有限责任公司电力科学研究院 The fault characteristic frequency display methods and device of a kind of on-line vibration monitoring system
CA2991725A1 (en) * 2018-01-10 2019-07-10 Psi Technologies Inc. Method and apparatus to predict failure and control vibrations in a subsurface artificial lift system
CN111797567A (en) * 2020-06-09 2020-10-20 合肥工业大学 Deep learning network-based bearing fault classification method and system
CN112113767A (en) * 2020-09-29 2020-12-22 昆明理工大学 Bearing fault diagnosis method based on proportional band selection criterion
CN112487882A (en) * 2020-11-13 2021-03-12 西南交通大学 Method for generating non-sparse index-guided enhanced envelope spectrum based on spectrum coherence

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
JEUNG HOON LEE: "a weighting function for improvement of spectral coherence based envelope spectrum" *
程尧: "基于加权联合提升包络谱的轴箱轴承故障诊断" *

Also Published As

Publication number Publication date
CN114218979B (en) 2023-04-07

Similar Documents

Publication Publication Date Title
Sharma et al. A review of gear fault diagnosis using various condition indicators
Altmann et al. Multiple band-pass autoregressive demodulation for rolling-element bearing fault diagnosis
Bajric et al. Feature extraction using discrete wavelet transform for gear fault diagnosis of wind turbine gearbox
Zhang et al. Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram
CN109000921B (en) Method for diagnosing main shaft fault of wind turbine generator
CN103091096A (en) Extraction method for early failure sensitive characteristics based on ensemble empirical mode decomposition (EEMD) and wavelet packet transform
RU2484442C1 (en) Vibration diagnostics and forecasting method of sudden failure of engine, and carrier
CN108195584B (en) A kind of Fault Diagnosis of Roller Bearings based on accuracy spectrogram
CN114323642A (en) Wind turbine generator vibration data processing system and data dilution method
Elasha et al. A novel condition indicator for bearing fault detection within helicopter transmission
CN113834658A (en) Early fault identification method for rolling bearing based on WPD-KVI-Hilbert
KR20170086308A (en) Apparatus and method for monitoring machine state
CN115371993A (en) Rolling bearing fault diagnosis method with stable angle time circulation without tachometer
CN114218979B (en) Method for constructing weighted joint lifting envelope spectrum based on local features of spectral coherence
Qian et al. A new health indicator for rolling bearings based on impulsiveness and periodicity of signals
CN113865871A (en) Rolling bearing fault diagnosis method based on fault characteristic frequency multiplication energy judgment
CN107702919A (en) Sliding bearing lubricating state monitoring method based on sound emission
CN116644280A (en) High-frequency vibration diagnosis method for failure of main bearing of engine
CN112132069A (en) Rolling bearing weak fault intelligent diagnosis method based on deep learning
CN112380782A (en) Rotating equipment fault prediction method based on mixed indexes and neural network
CN116150585A (en) Rotary machine fault diagnosis method based on product envelope spectrum
CN112345247A (en) Fault diagnosis method and device for rolling bearing
CN110108489B (en) Method for acquiring performance degradation trend of rolling bearing
CN113049251A (en) Bearing fault diagnosis method based on noise
CN113686577A (en) Bearing fault diagnosis method based on rapid nonlinear sparse spectrum

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