CN117150350A - Bearing fault diagnosis method and system based on self-adaptive ICEEMDAN noise reduction - Google Patents
Bearing fault diagnosis method and system based on self-adaptive ICEEMDAN noise reduction Download PDFInfo
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Abstract
The application discloses a bearing fault diagnosis method and system based on self-adaptive ICEEMDAN noise reduction, which are characterized in that an original acceleration signal of a bearing is obtained, and ICEEMDAN decomposition is carried out on the original acceleration signal; the kurtosis of each IMF component is calculated, and the signal-to-noise ratio SNR is calculated; if the signal-to-noise ratio SNR is greater than the set signal-to-noise ratio threshold, calculating the correlation coefficient of each IMF component and the original signal; adding the components IMF with the correlation coefficient larger than the set value of the correlation coefficient to obtain a reconstruction signal; carrying out envelope demodulation on the reconstructed signal to calculate the characteristic frequency of the bearing; and checking the amplitude value of the characteristic frequency of the bearing in the frequency spectrum to determine a fault point. The application can obtain the best decomposition effect and improve the shortcoming of modal aliasing. The method is simple and easy to operate, has low requirements on operators on the engineering site, and can be used for operators in the system after solidification.
Description
Technical Field
The application relates to the field of bearing fault diagnosis, aims to remove interference in bearing fault signals, and provides a bearing fault diagnosis method based on self-adaptive ICEEMDAN noise reduction.
Background
Bearings are widely used in industry. For the continuous process industry, the downtime or production outage caused by the failure of the bearings can result in significant economic losses. Therefore, the running state of the bearing is mastered in real time, and the bearing fault is predicted in advance, so that the method has great significance for the safe and efficient production of enterprises.
Vibration monitoring has been widely accepted as an effective tool for rotary machine condition detection. When the bearing breaks down, the bearing vibration signal can change obviously, and the characteristic frequency of the bearing can increase obviously. Therefore, the identification of the characteristic frequency of the bearing is particularly important when diagnosing the bearing faults.
However, in the actual process, when the bearing breaks down, the effective components in the signal of the bearing are often submerged by interference noise, so that in order to remove the interference components in the signal, a great number of researchers have made a great deal of research to obtain a certain effect, but the signal analysis methods are quite strong in theory, particularly in the interference removal process, the decomposition effect is poor, the mode is aliased, and the effective components are difficult to use in the actual engineering, so that the fault part of the bearing cannot be accurately identified.
Disclosure of Invention
The application aims to provide a bearing fault diagnosis method and system based on self-adaptive ICEEMDAN noise reduction, so as to obtain an optimal decomposition effect and improve the defect of modal aliasing.
Embodiments of the present application are implemented as follows:
a bearing fault diagnosis method based on self-adaptive ICEEMDAN noise reduction is characterized by comprising the following steps:
s1, acquiring an original acceleration signal of a bearing;
s2, setting a value range and a step length of white noise Nstd;
s3, setting a value range and a step length of iteration times NR;
s4, ICEEMDAN decomposition is carried out on the original acceleration signal, and a plurality of IMF components are obtained;
s5, calculating the kurtosis of each IMF component, adding IMF components with the kurtosis larger than a kurtosis threshold value to serve as signals, and adding IMF components with the kurtosis smaller than the kurtosis threshold value to serve as noise;
s6, calculating a signal-to-noise ratio (SNR);
s7, if the signal-to-noise ratio SNR is greater than the set signal-to-noise ratio threshold, executing a step S8; if the SNR is smaller than the set signal-to-noise threshold, jumping back to the step S2;
s8, calculating a correlation coefficient between each IMF component and the original signal;
s9, adding the components IMF with the phase relation number larger than the set value of the correlation coefficient to obtain a reconstruction signal;
s10, carrying out envelope demodulation on the reconstructed signal;
s11, calculating bearing characteristic frequency;
s12, checking the amplitude value of the characteristic frequency of the bearing in the frequency spectrum, and if the amplitude value of a certain characteristic frequency exceeds a threshold value, the bearing component corresponding to the characteristic frequency is failed.
In the above technical solution, the value range of the white noise Nstd in step S2 is 0.01-0.4.
In the above technical scheme, the range of the iteration number NR of the step S3 is 10-50.
In the technical scheme, the kurtosis threshold value is 3.
In the above technical solution, the set value of the correlation coefficient is 0.3.
In the above technical solution, the signal-to-noise ratio threshold is set to 40.
In the above technical solution, the range of the correlation coefficient between each IMF component and the original signal is: -0.0001-0.7209.
In the above technical solution, step S4 performs icemdan decomposition to obtain each IMF component through the input parameters Nstd, NR, maxIter and SNRFlag.
In the above technical solution, step S4 performs the icemdan decomposition on the original acceleration signal as follows:
(5) Constructing N signals containing controllable noise:
（1）
wherein:is->Constructing signals; />Is the noise standard deviation of the signal at the first decomposition; />Is->Zero mean unit variance white noise is added; />Is the first IMF operator to calculate the signal;
(6) Calculate eachLocal mean, obtaining a first residual component:
（2）
wherein:representing a local mean in the signal;
(7) The first mode when k=1 is found, using the original signalSubtracting the residual error generated at the first calculation +.>：
（3）
(8) Find the firstModality(s)>I.e. using the last calculated residual +.>Subtracting the residual error of the calculation ∈>：
（4）
（5）
(5) ObtainingAnd (3) a mode, returning to the public (4), and stopping iteration when the residual component meets the termination condition or the mode component is smaller than the first third-order local extremum.
The application also provides a bearing fault diagnosis system based on self-adaptive ICEEMDAN noise reduction, which is characterized by being used for executing the steps.
The beneficial effects of the application are as follows: according to the bearing fault diagnosis method and system for self-adaptive ICEEMDAN noise reduction, as the original acceleration signals are collected to carry out self-adaptive ICEEMDAN decomposition, residual components are set to meet the termination condition or modal components are smaller than the former third-order local extremum and serve as iteration stop conditions, the correlation degree of each IMF component and the original signals is taken as a denoising factor instead of single signal-to-noise ratio processing, so that the data are more in real conditions, the most suitable NStd and NR can be obtained, the optimal decomposition effect is obtained, and the defect of modal aliasing is overcome.
Meanwhile, a technician determines the initial range of the white noise NStd and the iteration number NR through the bearing fault frequency analysis through creative experiments, so that the data authenticity can be ensured, and the data processing efficiency can be improved.
Carrying out envelope demodulation on the reconstructed signal to calculate the characteristic frequency of the bearing; the fault point can be determined by checking the amplitude of the characteristic frequency of the bearing in the frequency spectrum, the operation is simple and easy, the requirement on operators on the engineering site is low, and the curing can be used for operators in the system.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a bearing fault diagnosis method based on adaptive ICEEMDAN noise reduction.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the present application, it should be noted that, directions or positional relationships indicated by terms such as "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., are directions or positional relationships based on those shown in the drawings, or those that are conventionally put in use of the product of the application, are merely for convenience of describing the present application and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific direction, be configured and operated in a specific direction, and thus should not be construed as limiting the present application. Furthermore, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Furthermore, the terms "horizontal," "vertical," "overhang," and the like do not denote a requirement that the component be absolutely horizontal or overhang, but rather may be slightly inclined. As "horizontal" merely means that its direction is more horizontal than "vertical", and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the present application, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
In the present application, unless expressly stated or limited otherwise, a first feature "above" or "below" a second feature may include both the first and second features being in direct contact, as well as the first and second features not being in direct contact but being in contact with each other through additional features therebetween. Moreover, a first feature being "above," "over" and "on" a second feature includes the first feature being directly above and obliquely above the second feature, or simply indicating that the first feature is higher in level than the second feature. The first feature being "under", "below" and "beneath" the second feature includes the first feature being directly under and obliquely below the second feature, or simply means that the first feature is less level than the second feature.
The features and capabilities of the present application are described in further detail below in connection with the examples.
Example 1
FIG. 1 is a flow chart of an analysis method of the application, which is a bearing fault diagnosis method based on self-adaptive ICEEMDAN noise reduction, specifically comprising the following steps:
s1, acquiring an original acceleration signal of a bearing;
s2, setting the value range of the white noise NStd to be 0.01-0.4, and setting the step length to be 0.01;
s3, setting the value range of the iteration number NR to be 10-50, and setting the step length to be 1;
s4, ICEEMDAN decomposition is carried out on the original signal, namely each component IMF is obtained by ICEEMDAN decomposition through input parameters Nstd, NR, maxIter and SNRFlag, wherein MaxIter is set to 5000, and SNRFlag is set to 1;
s5, calculating the kurtosis of each IMF component. Adding IMF components with kurtosis larger than 3 to be used as signals, and adding IMF components with kurtosis smaller than 3 to be used as noise;
s6, calculating a signal-to-noise ratio (SNR);
s7, if the SNR is greater than 40, executing a step S8; if the SNR is less than 40, jumping back to the S2 step;
s8, calculating a correlation coefficient between each IMF component and the original signal;
s9, adding the component IMFs with the phase relation number larger than 0.3 to reconstruct;
s10, carrying out envelope demodulation on the reconstructed signal;
s11, calculating bearing characteristic frequency;
s12, checking the amplitude value of the characteristic frequency of the bearing in the frequency spectrum, and if the amplitude value of a certain characteristic frequency exceeds a threshold value, the bearing component corresponding to the characteristic frequency is failed.
The ICEEMDAN decomposition step:
(9) Constructing N signals containing controllable noise:
（1）
wherein:is->Constructing signals; />Is the noise standard deviation of the signal at the first decomposition; />Is->Zero mean unit variance white noise is added; />Is the first IMF operator to calculate the signal.
(10) Calculate eachCalculating local mean values and averaging simultaneously to obtain a first residual component:
（2）
wherein:representing the local mean in the signal.
(11) The first mode (k=1) is determined by the method of the original signalSubtracting the residual error generated at the first calculation +.>：
（3）
(12) Find the firstThe modality, i.e. using the last calculated residual +.>Subtracting the residual error of the calculation ∈>：
（4）
（5）
(5) ObtainingA modality. Returning to (4), when the residual component meets the termination condition or the modal component is less than the previous third-order local extremum, the iteration is stopped.
According to S2-S7, the optimal value of Nstd is 0.05, and the optimal value of NR is 15;
according to S8, the icemdan decomposition is performed on the original acceleration signal, and the correlation coefficient between each IMF component and the original signal is calculated, as shown in table 1.
TABLE 1 correlation coefficient
According to S9, components with a phase relation greater than 0.3 are added, i.e. IMF1, IMF2 and IMF3 are added to obtain a reconstructed signal.
According to S10, carrying out envelope demodulation on the reconstructed signal;
the bearing model is 6314 deep groove ball bearing. Bearing parameters: number of rolling elementsDiameter of rolling elementBearing pitch diameter->Contact angle->. The bearing failure frequency at 1800r/min is shown in Table 2.
Table 2 bearing characteristic frequency (Hz)
As shown in fig. 1, according to step S8, the characteristic frequency of the bearing is brought into the envelope spectrum to find the amplitude of the corresponding frequency, and the amplitude of 146Hz is found to be more prominent, so that the bearing inner ring can be primarily judged to be faulty.
The verification result shows that the method can effectively identify the fault part of the bearing, so that the bearing fault can be effectively diagnosed.
The embodiments described above are some, but not all embodiments of the application. The detailed description of the embodiments of the application is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Claims (10)
1. A bearing fault diagnosis method based on self-adaptive ICEEMDAN noise reduction is characterized by comprising the following steps:
s1, acquiring an original acceleration signal of a bearing;
s2, setting a value range and a step length of white noise Nstd;
s3, setting a value range and a step length of iteration times NR;
s4, ICEEMDAN decomposition is carried out on the original acceleration signal, and a plurality of IMF components are obtained;
s5, calculating the kurtosis of each IMF component, adding IMF components with the kurtosis larger than a kurtosis threshold value to serve as signals, and adding IMF components with the kurtosis smaller than the kurtosis threshold value to serve as noise;
s6, calculating a signal-to-noise ratio (SNR);
s7, if the signal-to-noise ratio SNR is greater than the set signal-to-noise ratio threshold, executing a step S8; if the SNR is smaller than the set signal-to-noise threshold, jumping back to the step S2;
s8, calculating a correlation coefficient between each IMF component and the original signal;
s9, adding the components IMF with the phase relation number larger than the set value of the correlation coefficient to obtain a reconstruction signal;
s10, carrying out envelope demodulation on the reconstructed signal;
s11, calculating bearing characteristic frequency;
s12, checking the amplitude value of the characteristic frequency of the bearing in the frequency spectrum, and if the amplitude value of a certain characteristic frequency exceeds a threshold value, the bearing component corresponding to the characteristic frequency is failed.
2. The method for diagnosing bearing faults based on adaptive ICEEMDAN noise reduction according to claim 1, wherein the value range of white noise NStd in step S2 is 0.01-0.4.
3. The bearing fault diagnosis method based on the adaptive ICEEMDAN noise reduction according to claim 1, wherein the iteration number NR in the step S3 is 10-50.
4. The method for diagnosing bearing faults based on adaptive ICEEMDAN noise reduction as claimed in claim 1, wherein the kurtosis threshold is 3.
5. The method for diagnosing bearing faults based on adaptive ICEEMDAN noise reduction as claimed in claim 1, wherein the set value of the correlation coefficient is 0.3.
6. The method for diagnosing bearing faults based on adaptive ICEEMDAN noise reduction as claimed in claim 1, wherein the signal to noise ratio threshold is set to be 40.
7. The bearing fault diagnosis method based on adaptive ICEEMDAN noise reduction according to claim 1, wherein the range of the correlation coefficient between each IMF component and the original signal is as follows: -0.0001-0.7209.
8. The method for diagnosing bearing faults based on adaptive ICEEMDAN noise reduction according to claim 1, wherein in step S4, ICEEMDAN decomposition is carried out through input parameters Nstd, NR, maxIter and SNRFlag to obtain each IMF component.
9. The bearing fault diagnosis method based on adaptive ICEEMDAN noise reduction according to claim 1, wherein the step S4 of carrying out ICEEMDAN decomposition on the original acceleration signal is as follows:
(1) Constructing N signals containing controllable noise:
（1）
wherein:is->Constructing signals; />Is the firstNoise standard deviation of the signal during primary decomposition; />Is->Zero mean unit variance white noise is added; />Is the first IMF operator to calculate the signal;
(2) Calculate eachLocal mean, obtaining a first residual component:
（2）
wherein:representing a local mean in the signal;
(3) The first mode when k=1 is found, using the original signalSubtracting the residual error generated at the first calculation +.>：
（3）
(4) Find the firstModality(s)>I.e. using the last calculated residual +.>Subtracting the residual error of the calculation ∈>：
（4）
（5）
(5) ObtainingAnd (3) a mode, returning to the public (4), and stopping iteration when the residual component meets the termination condition or the mode component is smaller than the first third-order local extremum.
10. A bearing fault diagnosis system based on adaptive icemdan noise reduction, characterized by the steps for performing the bearing fault diagnosis method based on adaptive icemdan noise reduction according to any of the preceding claims 1-9.
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Application publication date: 20231201 |