CN115824647A - Bearing fault diagnosis method and diagnosis equipment based on mean square error time domain down-sampling - Google Patents

Bearing fault diagnosis method and diagnosis equipment based on mean square error time domain down-sampling Download PDF

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CN115824647A
CN115824647A CN202310120379.9A CN202310120379A CN115824647A CN 115824647 A CN115824647 A CN 115824647A CN 202310120379 A CN202310120379 A CN 202310120379A CN 115824647 A CN115824647 A CN 115824647A
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bearing
sampling
signal
mean square
square error
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CN115824647B (en
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徐徐
钱进
杨世飞
孙磊
邹小勇
刘宗斌
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Nanjing Chaos Data Technology Co ltd
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Abstract

The invention discloses a bearing fault diagnosis method and diagnosis equipment based on mean square error time domain down-sampling, wherein the method comprises the following steps: acquiring an original acceleration signal of a bearing; setting a down-sampling offset value and a down-sampling coefficient; performing mean square error time domain down-sampling on the original signal to obtain a down-sampled signal; performing FFT on the down-sampled signal; calculating the characteristic frequency of the bearing; and checking the amplitude of the characteristic frequency of the bearing in the frequency spectrum of the down-sampled signal, and if the amplitude of a certain characteristic frequency exceeds a threshold value, the bearing component corresponding to the characteristic frequency has a fault. The mean square error time domain down-sampling method provided by the invention directly performs down-sampling processing on the signal, eliminates interference, obtains effective components in the signal, and can simply, clearly and effectively identify the fault frequency of the bearing; down-sampling the processed signal avoids the complexity of signal processing, achieves the desired result in a simple way, and is easy to implement in practical applications.

Description

Bearing fault diagnosis method and diagnosis equipment based on mean square error time domain down-sampling
Technical Field
The invention belongs to the field of bearing fault diagnosis, and particularly relates to a bearing fault diagnosis method and diagnosis equipment based on mean square error time domain down-sampling.
Background
When a bearing breaks down, effective components in signals of the bearing are often submerged by interference noise, a large number of researchers who remove the interference components in the signals make a large amount of research to obtain a certain degree of effect, but the signal analysis methods are too strong in theory and difficult to apply in actual engineering. For example, the commonly used time-frequency domain analysis methods include wavelet analysis, EMD decomposition, EEMD decomposition, etc., the wavelet analysis is usually performed on stationary signals and loses effect on non-stationary signals, and the EMD decomposition and EEMD decomposition can be used for processing non-stationary signals, but modal aliasing occurs.
Disclosure of Invention
The invention aims to provide a bearing fault diagnosis method and diagnosis equipment based on mean square error time domain down-sampling, which are used for removing interference in a bearing fault signal to obtain effective components in the signal and are easy to realize in practical application.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a bearing fault diagnosis method based on mean square error time domain down-sampling comprises the following steps:
s1, acquiring an original acceleration signal of a bearing
Figure SMS_1
(ii) a Wherein the content of the first and second substances,ncounting the collection points of the original signals;
s2, setting a down-sampling offset valuebAnd down-sampling coefficientd；
S3, signal length after down samplinglIs composed ofn/d；
S4, presume the original signaliThe amplitude of the point is
Figure SMS_2
After down-sampling the signalkThe amplitude of the point is
Figure SMS_3
；
S5, mean square error time domain down-sampling is as follows:
Figure SMS_4
in the formula (I), the compound is shown in the specification,
Figure SMS_5
is the original signal of the signal to be transmitted,
Figure SMS_6
is a down-sampled signal, calculatesk-1)*d+1+bTok*d+bIn the range of
Figure SMS_7
The mean square error value is
Figure SMS_8
；k=1，2，3…l；
S6, down-sampling signal
Figure SMS_9
Performing FFT conversion;
s7, calculating the characteristic frequency of the bearing;
s8, checking the amplitude of the characteristic frequency of the bearing in the frequency spectrum of the down-sampled signal, and if the amplitude of a certain characteristic frequency exceeds a threshold value, enabling the bearing component corresponding to the characteristic frequency to be in fault.
Further, an acceleration sensor is used for acquiring a raw acceleration signal of the bearing.
Furthermore, after the original acceleration signal of the bearing is obtained, noise reduction pretreatment is carried out on the original acceleration signal.
Further, the noise reduction preprocessing specifically comprises: and denoising the original signal by adopting an EEMD method, and performing signal reconstruction by taking the components of the first five orders, the first six orders or the first seven orders.
Further, down-sampling the coefficientdThe range of (2) to (8).
Further, the bearing characteristic frequency comprises the characteristic frequencies of four structural components of an inner ring, an outer ring, a retainer and a rolling body of the bearing.
Further, the characteristic frequency of the inner ring
Figure SMS_10
Comprises the following steps:
Figure SMS_11
characteristic frequency of outer ring
Figure SMS_12
Comprises the following steps:
Figure SMS_13
characteristic frequency of cage
Figure SMS_14
Comprises the following steps:
Figure SMS_15
characteristic frequency of rolling body
Figure SMS_16
Comprises the following steps:
Figure SMS_17
wherein Z is the number of the rolling elements,
Figure SMS_18
the frequency of the bearing is converted for the bearing,
Figure SMS_19
the diameter of the rolling element is taken as the diameter,
Figure SMS_20
the diameter of the bearing is the middle diameter of the bearing,
Figure SMS_21
is the contact angle.
A diagnosis device adopts the bearing fault diagnosis method based on mean square error time domain down-sampling.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the mean square error time domain down-sampling method provided by the invention directly performs down-sampling processing on the signal, eliminates interference, obtains effective components in the signal, and can simply, clearly and effectively identify the fault frequency of the bearing; down-sampling the processed signal avoids the complexity of signal processing, achieves the desired result in a simple way, and is easy to implement in practical applications.
Furthermore, before the mean square error time domain down-sampling is performed on the acceleration signal of the bearing, noise reduction pretreatment such as EEMD can be performed on the acceleration signal, so that the accuracy of bearing fault diagnosis can be further improved.
Drawings
FIG. 1 is a flow chart of a diagnostic method of the present invention;
FIG. 2 is a graph of the original signal spectrum of the present invention;
FIG. 3 is a frequency spectrum diagram of a down-sampled signal according to the present invention;
fig. 4 is a schematic view of the bearing structure of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the respective embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The mean square error time domain down-sampling method provided by the invention directly performs down-sampling processing on the signal, eliminates interference and obtains effective components in the signal.
The bearing fault diagnosis method based on mean square error time domain down-sampling disclosed by the invention specifically comprises the following steps as shown in figure 1:
s1, acquiring an original acceleration signal of a bearing
Figure SMS_22
；nCollecting points for the original signal; an acceleration sensor can be used for collecting an original acceleration signal of the rolling bearing;
after the original acceleration signal of the bearing is acquired, it may be subjected to noise reduction preprocessing, for example, noise reduction is performed on the original signal by the EEMD method, and signal reconstruction is performed by using the components of the first several orders, which may be the components of the first five orders or the first six orders, or the components of the first seven orders. EEMD (ensemble empirical Mode Decomposition) is a noise reduction method, and white noise and iteration times are determined as key requirements; in the acceleration signal of the bearing, white noise can be 1dB, the iteration times can be 100, the first seven-order component signals after decomposition are added to form a new signal, namely a reconstructed signal, and then the reconstructed signal is subjected to mean square error time domain down-sampling.
S2, setting a down-sampling offset valuebSetting an offset valuebIs to change the bias of the original signal alongbFluctuating up and down; setting down-sampling coefficientsdDown sampling coefficientdThe range of (A) is 2 to 8;
s3, signal length after down samplinglIs composed ofn/d；
S4, presume the original signaliThe amplitude of the point is
Figure SMS_23
After down-sampling the signalkThe amplitude of the point is
Figure SMS_24
；
S5, the mean square error time domain down-sampling method comprises the following steps:
Figure SMS_25
in the formula (I), the compound is shown in the specification,
Figure SMS_26
is the original signal of the signal to be transmitted,
Figure SMS_27
is a down-sampled signal, calculatesk-1)*d+1+bTok*d+bWithin the range of
Figure SMS_28
The mean square error value is
Figure SMS_29
；k=1，2，3…l；
S6, performing FFT (fast Fourier transform) on the down-sampled signal to remove a high-frequency interference signal;
s7, calculating the characteristic frequency of the bearing;
s8, checking the amplitude of the characteristic frequency of the bearing in the frequency spectrum, and if the amplitude of a certain characteristic frequency exceeds a threshold value, enabling the bearing component corresponding to the characteristic frequency to have a fault.
The bearing characteristic frequency comprises the characteristic frequencies of four structural components of an inner ring, an outer ring, a retainer and a rolling body of the bearing. Fig. 4 is a schematic view of a typical structure of a rolling bearing. The bearing diameter corresponding to the rolling element center is called the bearing pitch diameter. According to the geometrical relationship shown in FIG. 4, the bearing pitch diameter can be known
Figure SMS_30
Equal to:
Figure SMS_31
wherein the content of the first and second substances,
Figure SMS_32
is the diameter of the raceway of the inner ring of the bearing,
Figure SMS_33
the diameter of the raceway of the bearing outer ring. The relationship of these two diameters to the bearing pitch diameter can be written as:
Figure SMS_34
Figure SMS_35
wherein the content of the first and second substances,
Figure SMS_36
in order to be the diameter of the rolling elements of the bearing,
Figure SMS_37
is the contact angle. The rotational linear velocity of the inner and outer race raceways can be written as:
Figure SMS_38
Figure SMS_39
in the above formula, the first and second carbon atoms are,
Figure SMS_40
is the angular velocity of rotation of the inner race,
Figure SMS_41
is the angular velocity of rotation of the outer ring raceway.
If the rolling elements ideally roll on the raceways, the cage rotational speed is the average of the inner and outer ring rotational speeds:
Figure SMS_42
expressed in frequency, this is:
Figure SMS_43
in the formula (I), the compound is shown in the specification,
Figure SMS_44
is the characteristic frequency of the cage or cages,
Figure SMS_45
the frequency of the bearing is converted for the bearing,
Figure SMS_46
the frequency conversion of the outer ring raceway;
typically, the outer race of the bearing is stationary when the bearing is installed in the apparatus. Then the above equation can be simplified to:
Figure SMS_47
the rolling frequency of the cage relative to the inner ring can be written as:
Figure SMS_48
if the bearing has Z rolling bodies, the rolling bodies pass through the fixed point of the inner ring for Z times when the retainer rolls for one circle relative to the inner ring. The frequency of passage of the rolling elements through the inner ring fastening point is then:
Figure SMS_49
when the outer ring is kept fixed, the simplification is as follows:
Figure SMS_50
the rotational frequency of the cage relative to the outer race can be calculated similarly:
Figure SMS_51
similarly, the passing frequency of the rolling elements through the outer ring fixing point is:
Figure SMS_52
the spin frequency of the rolling elements can be written as:
Figure SMS_53
in this embodiment, a down-sampling offset value b =0 and a down-sampling coefficient d =4 are set.
As shown in fig. 2, the vibration energy is concentrated in the high frequency signal in the spectrogram of the original signal, and the fault signal of the bearing is submerged; as shown in fig. 3, the high-frequency interference signal is removed from the spectrogram obtained by FFT of the down-sampled signal, and the signal is much cleaner.
The bearing in this embodiment is a 6314 deep groove ball bearing. The bearing parameters are as follows: number of rolling elements
Figure SMS_54
Diameter of rolling element
Figure SMS_55
Bearing pitch diameter
Figure SMS_56
Contact angle
Figure SMS_57
. The bearing failure frequency at 1800r/min is shown in Table 1.
TABLE 1 bearing characteristic frequency (Hz)
Structure of the product Inner ring Outer ring Holding rack Rolling body
Characteristic frequency 147.7 92.3 12.0 123.0
As shown in fig. 3, according to step S8, the characteristic frequency of the bearing is substituted into the envelope spectrum to find the amplitude of the corresponding frequency, and the amplitude of 146.9Hz is found to be relatively prominent, so that the failure of the inner ring of the bearing can be preliminarily determined.
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 invention also provides a diagnosis device, which adopts the bearing fault diagnosis method based on the mean square error time domain down-sampling to diagnose the fault of the bearing.
In summary, the mean square error time domain down-sampling method can simply and clearly identify the fault frequency of the bearing, and the down-sampling process signal avoids the complexity of signal processing, obtains the desired result by a simple method, and is easy to be realized in practical application.
It should be noted that, according to the implementation requirement, each step/component described in the present application can be divided into more steps/components, and two or more steps/components or partial operations of the steps/components can be combined into new steps/components to achieve the purpose of the present invention.
It will be understood by those skilled in the art that the foregoing is merely a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included within the scope of the present invention.

Claims (8)

1. A bearing fault diagnosis method based on mean square error time domain down-sampling is characterized by comprising the following steps:
s1, acquiring an original acceleration signal of a bearing
Figure QLYQS_1
(ii) a Wherein the content of the first and second substances,ncounting the collection points of the original signals;
s2, setting a down-sampling offset valuebAnd down-sampling coefficientd；
S3, signal length after down samplinglIs composed ofn/d；
S4, presume the original signaliThe amplitude of the point is
Figure QLYQS_2
After down-sampling the signalkThe amplitude of the point is
Figure QLYQS_3
；
S5, mean square error time domain down-sampling is as follows:
Figure QLYQS_4
in the formula (I), the compound is shown in the specification,
Figure QLYQS_5
is the original signal of the signal to be transmitted,
Figure QLYQS_6
is a down-sampled signal, calculatesk-1)*d+1+bTok*d+bWithin the range of
Figure QLYQS_7
The mean square error value is
Figure QLYQS_8
；k=1，2，3…l；
S6, down-sampling signal
Figure QLYQS_9
Performing FFT conversion;
s7, calculating the characteristic frequency of the bearing;
s8, checking the amplitude of the characteristic frequency of the bearing in the frequency spectrum of the down-sampled signal, and if the amplitude of a certain characteristic frequency exceeds a threshold value, enabling the bearing component corresponding to the characteristic frequency to be in fault.
2. The mean square error time domain downsampling-based bearing fault diagnosis method according to claim 1, characterized in that an acceleration sensor is used for acquiring a raw acceleration signal of a bearing.
3. The method for diagnosing the fault of the bearing based on the mean square error time domain down sampling as claimed in claim 1, wherein after the original acceleration signal of the bearing is obtained, the noise reduction pretreatment is carried out on the original acceleration signal.
4. The mean square error time domain down-sampling based bearing fault diagnosis method according to claim 3, wherein the noise reduction preprocessing specifically comprises: and denoising the original signal by adopting an EEMD method, and performing signal reconstruction by taking the components of the first five orders, the first six orders or the first seven orders.
5. The mean square error time domain down-sampling based bearing fault diagnosis method of claim 1, wherein a down-sampling coefficientdThe range of (B) is 2 to 8.
6. The method for diagnosing the bearing fault based on the time-domain down-sampling of the mean square error according to claim 1, wherein the characteristic frequencies of the bearing comprise the characteristic frequencies of four structural components of an inner ring, an outer ring, a cage and a rolling body of the bearing.
7. The mean square error time domain down-sampling based bearing fault diagnosis method of claim 6, wherein a characteristic frequency of an inner ring
Figure QLYQS_10
Comprises the following steps:
Figure QLYQS_11
characteristic frequency of outer ring
Figure QLYQS_12
Comprises the following steps:
Figure QLYQS_13
characteristic frequency of cage
Figure QLYQS_14
Comprises the following steps:
Figure QLYQS_15
characteristic frequency of rolling body
Figure QLYQS_16
Comprises the following steps:
Figure QLYQS_17
wherein Z is the number of rolling elements,
Figure QLYQS_18
the frequency of the bearing is converted for the bearing,
Figure QLYQS_19
the diameter of the rolling element is taken as the diameter,
Figure QLYQS_20
the diameter of the bearing is the middle diameter of the bearing,
Figure QLYQS_21
is the contact angle.
8. A diagnostic device characterized in that it employs the mean square error time domain down-sampling based bearing fault diagnosis method of any one of claims 1 to 7.
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