CN107271184B - A kind of the kernel regression decomposition method and system of rolling bearing fault diagnosis - Google Patents
A kind of the kernel regression decomposition method and system of rolling bearing fault diagnosis Download PDFInfo
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- CN107271184B CN107271184B CN201710367859.XA CN201710367859A CN107271184B CN 107271184 B CN107271184 B CN 107271184B CN 201710367859 A CN201710367859 A CN 201710367859A CN 107271184 B CN107271184 B CN 107271184B
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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- G01M13/045—Acoustic or vibration analysis
Abstract
The invention discloses the kernel regression decomposition methods and system of a kind of rolling bearing fault diagnosis.The present invention includes motor, V belt translation axis, shaft coupling, test bearing, acceleration transducer, multi-channel data acquisition analyzer, computer；The faulty bearings are connected by motor, V belt translation axis and shaft coupling, acceleration transducer is fixed on test bearing bearing block, acceleration transducer output end is connect with multi-channel data acquisition analyzer, data after analyzer extraction are by being sent to computer after saving, and signal data is analyzed and processed on computers in conjunction with kernel regression decomposition method, it identifies bearing combined failure, realizes the accurate detection to rolling bearing operating status.The present invention can effectively acquire the fault message of rolling bearing, have transmission performance good, and accuracy rate is done, and speed is fast, simple operation and other advantages, have engineering application value.
Description
Technical field
The present invention relates to mechanical fault diagnosis field, in particular to a kind of kernel regressions of rolling bearing fault diagnosis point
Solve method and system.
Background technique
Rolling bearing is the element for being easiest to damage in all kinds of machines in widely applied important mechanical part and machine
One of.Rotating machinery is the health status of rolling body bearing of placing one's entire reliance upon, and almost accounts for the equipment fault of 40- 50%.Axis
The failure held may be and its serious that may cause the shut-down of the entire production line, even result in the injures and deaths of personnel.At this stage
Artificial experience is mainly passed through to the fault diagnosis of bearing or instrument judges the operating status of bearing, but is actually obtaining vibration signal
In often due to working condition is complicated, ambient noise is big, equipment is in the reasons such as working condition for a long time, be sometimes difficult to obtain therefore
Hinder the obvious signal of aspect ratio.Therefore, signal denoising has become the committed step of bearing fault signal processing.Current event
Hinder diagnostic system there are stability limitations such as poor, cumbersome, Shang Buneng is widely used popularization.In order to solve to roll
The technical problems such as bearing failure diagnosis difficulty, False Rate height, the research of the fault diagnosis system of bearing also become more and more intentionally
Justice.
In existing all kinds of bearing failure diagnosis technologies, the analysis of vibration signal is still a kind of main method.Base
It is indicated in the signal frequency domain of Fourier transformation, discloses the inner link between the function of time and frequency spectrum function, put down in traditional
When having played extremely important effect in steady signal analysis and processing, but having extracted signal spectrum with the method for Fourier transformation, need
Whole time-domain informations of signal are utilized, this is a kind of integral transformation, lacks time domain positioning function.Wavelet transformation is in recent years
A kind of new transform analysis method, it inherits and has developed the thought of short time discrete Fourier transform localization, while overcoming window again
The disadvantages of mouthful size does not change with frequency, wavelet transformation can better observation signal local characteristics, letter can be observed simultaneously
Number time and frequency information, this is that Fourier transformation is not achieved.But noise is inhibited often to handle using wavelet transformation
Lead to oscillation effect when Low SNR signal, while needing to manually select suitable small echo when carrying out wavelet transformation, it is right in this way
The required time is considerably increased in the processing of Practical Project problem.EMD (the experience that American Engineering academician doctor Huang E proposes
Mode decomposition) a kind of adaptive data processing and method for digging are had proven to, it is very suitable to non-linear and non-stationary
The processing of time series, and any basic function need not be preset, it is substantially also the tranquilization to data sequence or signal
Processing.The purpose of EMD algorithm is the intrinsic mode functions by the bad signal decomposition of performance for one group of better performances, is decomposited
The each component come contains the local feature signal of the different time scales of original signal.The suitable component of selection, then carries out
Hilbert transform obtains time-frequency spectrum, obtains the frequency of physical significance.In the work environment, but how correctly to select to add
Noise amplitude still needs to further study, modal overlap frequently occur be also EMD one of major defect, modal overlap is signal
Caused by interruption, interruption is a kind of disturbing signal of indefinite form, the case where being frequently encountered in actual treatment.Interruption can be led
Time-frequency distributions are obscured in cause, and then destroy the physical significance of IMF.
Summary of the invention
The technical problem to be solved by the embodiment of the invention is that providing a kind of kernel regression point of rolling bearing fault diagnosis
Method and system are solved, and processing analysis is carried out to vibration signal using kernel regression decomposition method, bearing correlated characteristic is extracted and goes forward side by side
Row identification.
To achieve the above object, the technical scheme is that the following steps are included:
(1) data acquire, and the vibration signal data of rolling bearing to be measured is acquired using acceleration transducer, which passes
Sensor is mounted on the bearing block of bearing to be measured, and acceleration transducer output end is connect with multi-channel data acquisition analyzer, more
Channel data acquisition and analysis instrument is by the data after extraction by being transmitted to computer after saving；
(2) signal processing is carried out to real time data measured by acceleration transducer using kernel regression decomposition method, core returns
Return decomposition method:
(2.1) data for first obtaining multi-channel data acquisition analyzer are carried out as original signal with gaussian kernel function
First time kernel regression handles to obtain a remaining variable, the formula that kernel regression used decomposes are as follows:
In formula: f∑,1It (t) is original signal, f∑,1(ti) it is kernel function center, λ1For bandwidth parameter, f∑,2(t) it is
The new signal obtained after kernel regression transformation, K1For gaussian kernel function；
(2.2) then resulting remaining variable and original signal are subtracted each other to obtain a new representation in components to be first remnants
Component, then to f∑,2(t) carry out kernel regression formula manipulation again, and so on be continuously available new residual components, but all points
Solution is not endless decomposition, stops decomposing when meeting the standard deviation standard that we give, formula are as follows:
In formula: T is the length of data, Cj(t) component to be obtained with kernel regression；When meeting the threshold value set in above formula,
Kernel regression processing stops carrying out next step analysis；
Then, some extra information have been handled included in each complete caused new residual components for no
Soft-threshold processing is carried out, resulting component is further subjected to denoising, threshold formula are as follows:
θj=MAD (Cj(t))/0.6745 (3)
In formula: MAD is mean absolute deviation；
(2.3) final step of kernel regression is exactly that all obtained residual components are reconstructed, by will be all residual
Remaining component carries out obtaining new signal after adding up to carry out data analysis and fine fault diagnosis；
(3) finally all components are superimposed to obtain new signal progress Hilbert envelope spectrum analysis later, in time-frequency
Rolling bearing fault information is obtained in conversion.
It is 0.2 that further setting, which is the threshold value that the processing of kernel regression set by formula (2) stops in the step (2.2),.
The present invention also provides a kind of Diagnosing System for Detecting of Antifriction Bearings based on kernel regression decomposition method, include for pacifying
Transmission shaft, the bearing for driving the power unit for being located at transmission shaft rotation, being set to bearing to be measured for filling rolling bearing to be measured
On seat by acquire roller bearing acceleration information to be measured acceleration transducer and multi-channel data acquisition analyzer and based on
Calculation machine, acceleration transducer output end are connect with multi-channel data acquisition analyzer, and multi-channel data acquisition analyzer will extract
Data afterwards are by being transmitted to computer after saving；
Signal is carried out to real time data measured by acceleration transducer using kernel regression decomposition method in the computer
Processing, kernel regression decomposition method:
The data that multi-channel data acquisition analyzer is obtained first carry out first as original signal with gaussian kernel function
Secondary kernel regression handles to obtain a remaining variable, the formula that kernel regression used decomposes are as follows:
In formula: f∑,1It (t) is original signal, f∑,1(ti) it is kernel function center, λ1For bandwidth parameter, f∑,2It (t) is kernel regression
The new signal obtained after transformation, K1For gaussian kernel function；
Then resulting remaining variable and original signal are subtracted each other to obtain a new representation in components to be first residual components,
Then to f∑,2(t) carry out kernel regression formula manipulation again, and so on be continuously available new residual components, but all decomposition are simultaneously
It is not endless decomposition, stops decomposing when meeting the standard deviation standard that we give, formula are as follows:
In formula: T is the length of data, Cj(t) component to be obtained with kernel regression；When meeting the threshold value set in above formula,
Kernel regression processing stops carrying out next step analysis；
Then, some extra information have been handled included in each complete caused new residual components for no
Soft-threshold processing is carried out, resulting component is further subjected to denoising, threshold formula are as follows:
θj=MAD (Cj(t))/0.6745 (3)
In formula: MAD is mean absolute deviation；
The final step of kernel regression is exactly that all obtained residual components are reconstructed, by by all residual components
It carries out obtaining new signal after adding up to carry out data analysis and fine fault diagnosis；
Finally all components are superimposed to obtain new signal progress Hilbert envelope spectrum analysis later, in time-frequency convert
In obtain rolling bearing fault information.
It includes motor that further setting, which is power unit, and the output shaft of motor is passed by belt gear unit and transmission shaft
Dynamic connection, the transmission shaft install rolling bearing to be measured by shaft coupling.
Kernel regression method of the present invention is a kind of new digital signal processing method, will first based on EMD principle
It includes certain characteristic information that signal is resolved into several scales while each scale with kernel regression technology again, but each scale is again
Contain some useless information.Therefore, optimize this algorithm in conjunction with soft threshold method and standard deviation standard, reached with this
Signal is finally carried out time-frequency convert using Hilbert envelope spectral method, by new signal processing by the effect of denoising
Result is obtained later and judges fault message.
By actual application, the present invention has good effect in processing rolling bearing fault signal, and this method is more
Suitable for denoising and fault detection in mechanical system.
The beneficial effects of the present invention are: the present invention has transmission performance good, speed is fast, easy to operate, finds machine in time
The failure of rolling bearing has good engineer application effect.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention, for those of ordinary skill in the art, without any creative labor, according to
These attached drawings obtain other attached drawings and still fall within scope of the invention.
Fig. 1 is kernel regression decomposition method trouble-shooting chart of the invention；
Fig. 2 is kernel regression method route figure of the invention；
Fig. 3 is the processing result of bearing outer ring fault-signal of the present invention；
Fig. 4 is bearing combined failure signal processing results of the present invention.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, the present invention is made into one below in conjunction with attached drawing
Step ground detailed description.
The direction and position term that the present invention is previously mentioned, for example, "upper", "lower", "front", "rear", "left", "right", "inner", "
Outside ", " top ", " bottom ", " side " etc. are only direction or position with reference to attached drawing.Therefore, the direction used and position term
It is rather than the limiting the scope of the invention to illustrate and understand the present invention.
As shown in Figures 1 to 4, in the embodiment of the present invention, including motor, V belt translation axis, shaft coupling, faulty bearings plus
Velocity sensor, multi-channel data acquisition analyzer, PC machine, faulty bearings described in Fig. 1 pass through motor, V belt translation axis and shaft coupling
It is connected with transmission shaft, acceleration transducer is fixed on test bearing bearing block, acceleration transducer output end and multichannel number
It is connected according to acquisition and analysis instrument, analyzer is by the data after extraction by being transmitted to computer, the present embodiment computer after saving
Using the PC machine of traditional X86 or X64, PC machine combination kernel regression decomposition technique is analyzed and processed signal data, realization pair
The accurate detection of rolling bearing operating status.Operating procedure is as follows:
(1) data collector is selected first, and the present invention acquires data, acceleration transducer peace using acceleration transducer
On the bearing block of bearing to be measured, sample frequency is generally 25600Hz, according to the specific parameter setting of sensor, in Fig. 2 reality
It tests on platform and is passed in PC machine in real time after data measured result.
(2) secondly the present invention will carry out letter to real time data measured by acceleration transducer using kernel regression decomposition method
Number processing.Kernel regression decomposition method:
The data that multi-channel data acquisition analyzer is obtained first carry out first as original signal with gaussian kernel function
Secondary kernel regression handles to obtain a remaining variable, the formula that kernel regression used decomposes are as follows:
In formula: f∑,1It (t) is original signal, f∑,1(ti) it is kernel function center, λ1For bandwidth parameter, f∑,2It (t) is kernel regression
The new signal obtained after transformation, K1For gaussian kernel function.
Then resulting remaining variable and original signal are subtracted each other to obtain a new representation in components to be first residual components,
Then to f∑,2(t) carry out kernel regression formula manipulation again, and so on be continuously available new residual components.But all decomposition are simultaneously
It is not endless decomposition, stops decomposing when meeting the standard deviation standard that we give.Its formula are as follows:
In formula: T is the length of data, Cj(t) component to be obtained with kernel regression.When meeting the threshold value set in above formula,
When generally we are set as 0.2, kernel regression processing stops carrying out next step analysis.
In addition to this, it is possible to not handle in above-mentioned treatment process completely, be wrapped again in each new residual components
Contained some extra information, therefore the method that the present invention uses for reference EMD carries out soft-threshold processing, further by resulting component into
Row denoising.Its threshold formula are as follows:
θj=MAD (Cj(t))/0.6745 (3)
In formula: MAD is mean absolute deviation.
The final step of kernel regression is exactly that all obtained residual components are reconstructed, by by all residual components
It carries out obtaining new signal after adding up to carry out data analysis and fine fault diagnosis.
(3) finally all components are superimposed to obtain new signal progress Hilbert envelope spectrum analysis later, in time-frequency
Bearing fault information is obtained in conversion.Concrete operations process is with reference to as shown in Figure 2.
As shown in figure 1, the kernel regression decomposition technique bearing failure diagnosis system, fault message are adopted by acceleration transducer
Collection.
Acceleration transducer is mounted on faulty bearings as shown in figure 1, is placed on the bearing block of test bearing, is carried out radial
The vibration signal in direction samples.
Multi-channel data acquisition analyzer model AVANT-MI-7016 as shown in figure 1 has 16 channels, each channel tool
Have signal acquisition, extraction, filtering, signal source output function, so that this system is become relatively reliable on hardware and simplify perhaps
The intermediate steps of multi signal processing, it is practical and convenient.
Referring to Fig. 1, Fig. 1 is a kind of knot of specific embodiment of Diagnosing System for Detecting of Antifriction Bearings provided by the present invention
Structure block diagram.
Diagnosing System for Detecting of Antifriction Bearings is used to detect the phenomenon of the failure of rolling bearing, and fault vibration signal is uploaded to
PC machine.In specific scheme, motor drives V belt translation, and shaft coupling and transmission shaft provide revolving speed in the testing stand of simulation, and
By acceleration transducer acquisition parameter signal on faulty bearings, these parameter signals acquired are carried out by kernel regression method
Processing, to determine the situation of equipment, the description to this status of equipment is phenomenon of the failure, as the inner ring of bearing, outer ring,
The phenomena of the failure such as rolling element.Specific calculation method can be calculated with following equation:
Outer ring failure formula:
Inner ring failure formula:
Rolling element failure formula:
In formula: frFor speed, n is bearing roller number, and φ is radial direction contact angle, and d is that rolling element is average straight
Diameter, D are the average diameter of bearing.
Example case 1:
It is the result figure by being obtained handled by the Diagnosing System for Detecting of Antifriction Bearings as shown in Figure 3, according to existing axis
Holding information and can calculating the failure-frequency of this experiment axis bearing outer-ring is 91.15Hz, and failure can be rapidly distinguished from figure
Frequency be 87.5Hz it is close with theoretical value, wherein 29.8Hz for the motor speed, it is possible to determine that this rolling bearing
Fault type is bearing outer ring failure.
Case study on implementation 2:
On the basis of example 1, the implementation case 2 will be further processed more complicated bearing fault, by calculating this
It includes bearing inner race, outer ring and rolling element failure that faulty bearings, which are combined failure, in experiment.Pass through the data meter to test bearing
The failure-frequency for calculating available inner ring is 197.05Hz, and outer ring failure-frequency is 121.51Hz, and rolling element failure is
79.25Hz.Frequency is 120Hz and outer ring Trouble Match, the two frequencys multiplication symbol of 160Hz and rolling element failure as can be drawn from Figure 4
It closes, 202.5Hz is substantially conformed to inner ring failure.To further illustrate that the present invention has preferable treatment effect, it is worthy to be popularized
Using.
Those of ordinary skill in the art will appreciate that implement the method for the above embodiments be can be with
Relevant hardware is instructed to complete by program, the program can be stored in a computer readable storage medium,
The storage medium, such as ROM/RAM, disk, CD.
The above disclosure is only the preferred embodiments of the present invention, cannot limit the right model of the present invention with this certainly
It encloses, therefore equivalent changes made in accordance with the claims of the present invention, is still within the scope of the present invention.
Claims (3)
1. a kind of kernel regression decomposition method of rolling bearing fault diagnosis, it is characterised in that the following steps are included:
(1) data acquire, and the vibration signal data of rolling bearing to be measured are acquired using acceleration transducer, the acceleration transducer
It is mounted on the bearing block of bearing to be measured, acceleration transducer output end is connect with multi-channel data acquisition analyzer, multichannel
Data collection and analysis instrument is by the data after extraction by being transmitted to computer after saving；
(2) signal processing, kernel regression point are carried out to real time data measured by acceleration transducer using kernel regression decomposition method
Solution method:
(2.1) data for first obtaining multi-channel data acquisition analyzer carry out first as original signal with gaussian kernel function
Secondary kernel regression handles to obtain a remaining variable, the formula that kernel regression used decomposes are as follows:
In formula: f∑,1It (t) is original signal, f∑,1(ti) it is kernel function center, λ1For bandwidth parameter, f∑,2(t) it is converted for kernel regression
The new signal obtained afterwards, K1For gaussian kernel function；
(2.2) then resulting remaining variable and original signal are subtracted each other to obtain a new representation in components to be first remnants points
Amount, then to f∑,2(t) carry out kernel regression formula manipulation again, and so on be continuously available new residual components, but all decomposition
It is not endless decomposition, stops decomposing when meeting the standard deviation standard that we give, formula are as follows:
In formula: T is the length of data, Cj(t) component to be obtained with kernel regression；When meeting the threshold value set in above formula, kernel regression
Processing stops carrying out next step analysis；
Then, some extra information progress have been handled included in each complete caused new residual components for no
Soft-threshold processing, further carries out denoising, threshold formula for resulting component are as follows:
θj=MAD (Cj(t))/0.6745 (3)
In formula: MAD is mean absolute deviation；
(2.3) final step of kernel regression is exactly that all obtained residual components are reconstructed, by by all remnants points
Amount carries out obtaining new signal after adding up to carry out data analysis and fine fault diagnosis；
(3) finally all components are superimposed to obtain new signal progress Hilbert envelope spectrum analysis later, in time-frequency convert
In obtain rolling bearing fault information.
2. a kind of Diagnosing System for Detecting of Antifriction Bearings based on kernel regression decomposition method, it is characterised in that: include for installing
The transmission shaft of rolling bearing to be measured, the bearing block for driving the power unit for being located at transmission shaft rotation, being set to bearing to be measured
On for acquiring acceleration transducer and multi-channel data acquisition analyzer and the calculating of roller bearing acceleration information to be measured
Machine, acceleration transducer output end are connect with multi-channel data acquisition analyzer, after multi-channel data acquisition analyzer will extract
Data by save after be transmitted to computer；
Signal processing is carried out to real time data measured by acceleration transducer using kernel regression decomposition method in the computer,
Kernel regression decomposition method:
The data that multi-channel data acquisition analyzer is obtained first carry out first time core as original signal with gaussian kernel function
Recurrence handles to obtain a remaining variable, the formula that kernel regression used decomposes are as follows:
In formula: f∑,1It (t) is original signal, f∑,1(ti) it is kernel function center, λ1For bandwidth parameter, f∑,2(t) it is converted for kernel regression
The new signal obtained afterwards, K1For gaussian kernel function；
Then resulting remaining variable and original signal are subtracted each other to obtain a new representation in components to be first residual components, then
To f∑,2(t) carry out kernel regression formula manipulation again, and so on be continuously available new residual components, but all decomposition are not
Endless decomposition stops decomposing when meeting the standard deviation standard that we give, formula are as follows:
In formula: T is the length of data, Cj(t) component to be obtained with kernel regression；When meeting the threshold value set in above formula, kernel regression
Processing stops carrying out next step analysis；
Then, some extra information progress have been handled included in each complete caused new residual components for no
Soft-threshold processing, further carries out denoising, threshold formula for resulting component are as follows:
θj=MAD (Cj(t))/0.6745 (3)
In formula: MAD is mean absolute deviation；
The final step of kernel regression is exactly that all obtained residual components are reconstructed, by carrying out all residual components
New signal is obtained after cumulative to carry out data analysis and fine fault diagnosis；
Finally all components are superimposed to obtain new signal progress Hilbert envelope spectrum analysis later, in time-frequency convert
Rolling bearing fault information out.
3. a kind of Diagnosing System for Detecting of Antifriction Bearings based on kernel regression decomposition method according to claim 2, feature
Be: power unit includes motor, and the output shaft of motor is connect by belt gear unit with transmission shaft driven.
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Application publication date: 20171020 Assignee: Guowang Technology (Zhejiang) Co.,Ltd. Assignor: Wenzhou University Contract record no.: X2023330000103 Denomination of invention: A Kernel Regression Decomposition Method and System for Fault Diagnosis of Rolling Bearings Granted publication date: 20190611 License type: Common License Record date: 20230311 |
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