CN110163190B - Rolling bearing fault diagnosis method and device - Google Patents

Rolling bearing fault diagnosis method and device Download PDF

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Publication number
CN110163190B
CN110163190B CN201910522045.8A CN201910522045A CN110163190B CN 110163190 B CN110163190 B CN 110163190B CN 201910522045 A CN201910522045 A CN 201910522045A CN 110163190 B CN110163190 B CN 110163190B
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fault
frequency
rolling bearing
value
time domain
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CN110163190A (en
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雷文平
李长伟
韩捷
王宏超
王凯
陈磊
王丽雅
李凌均
陈宏�
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Zhengzhou Enpu Technology Co ltd
Zhengzhou University
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Zhengzhou Enpu Technology Co ltd
Zhengzhou University
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    • 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
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Abstract

The invention provides a fault diagnosis method and device for a rolling bearing, and belongs to the technical field of fault diagnosis. The method comprises the following steps: acquiring a time domain data sequence of a vibration acceleration signal of a rolling bearing to be detected by using an acceleration sensor; calculating a peak index, a kurtosis index and a waveform index by using the time domain data sequence; if the value of one index is larger than the normal value of the index, performing high-pass filtering processing and segmentation processing on the time domain data sequence to obtain a new time domain data sequence; resampling the new time domain data sequence, and calculating by using the resampled time domain data sequence to obtain a frequency spectrum; and if the maximum amplitude value in the frequency spectrum is greater than the fault alarm value, judging that the rolling bearing to be tested has a fault. According to the invention, the amplitude value at the fault characteristic frequency of the frequency spectrum of the rolling bearing to be detected is necessarily contained in the first amplitude maximum values of the frequency spectrum when a fault occurs, so that the fault diagnosis of the rolling bearing to be detected is realized, and the diagnosis result can objectively reflect the real fault condition of the rolling bearing to be detected.

Description

Rolling bearing fault diagnosis method and device
Technical Field
The invention relates to a fault diagnosis method and device for a rolling bearing, and belongs to the technical field of fault diagnosis.
Background
In the fault diagnosis of the rolling bearing, an early fault signal of the rolling bearing is often submerged in background noise and is difficult to find and extract, the early fault signal of the rolling bearing appears in a high frequency band, in addition, a fault vibration signal of the rolling bearing generally comprises a periodic pulse impact signal and generates a modulation phenomenon of the vibration signal, and the modulation phenomenon shows that modulation side bands with uniform intervals appear on two sides of a characteristic frequency on a frequency spectrum.
At present, the fault diagnosis of the rolling bearing is generally realized by adopting an envelope demodulation analysis method, the method extracts modulation information from a fault vibration signal of the rolling bearing, and the degree and the position of the damage of the rolling bearing can be judged by analyzing the intensity, the harmonic and the side frequency band of the modulation information. However, the fault diagnosis of the rolling bearing by using the method still has the following defects: firstly, human diagnosticians are required to manually select frequency in the diagnosis process, and the diagnosis results obtained by selecting different analysis frequency bands are greatly different; secondly, a human diagnostician is required to perform fault diagnosis according to the spectrum characteristics and the existing knowledge, so that the diagnosis result contains large artificial uncertain factors. In summary, the fault diagnosis result of the rolling bearing obtained by the envelope demodulation and analysis method is greatly influenced by subjective factors, so that the fault diagnosis result of the rolling bearing is difficult to objectively reflect the real fault condition of the rolling bearing, and the method is not suitable for intelligent fault diagnosis of the rolling bearing.
Disclosure of Invention
The invention aims to provide a rolling bearing fault diagnosis method, which is used for solving the problem that the real fault condition of a rolling bearing is difficult to objectively reflect by using a rolling bearing fault diagnosis result obtained by an envelope demodulation analysis method; the invention also provides a rolling bearing fault diagnosis device, which is used for solving the problem that the rolling bearing fault diagnosis result obtained by using the envelope demodulation analysis method is difficult to objectively reflect the real fault condition of the rolling bearing.
In order to achieve the above object, the present invention provides a rolling bearing fault diagnosis method, including the steps of:
acquiring a time domain data sequence of a vibration acceleration signal of a rolling bearing to be detected by using an acceleration sensor;
calculating a peak index, a kurtosis index and a waveform index by using the time domain data sequence;
if the value of one index is larger than the normal value of the index, performing high-pass filtering processing and segmentation processing on the time domain data sequence to obtain a new time domain data sequence; wherein the segmentation process comprises: taking an absolute value of the time domain data sequence after the high-pass filtering processing, dividing the time domain data sequence after the absolute value is taken into a plurality of data segments, and replacing all data values in each data segment by a maximum value in the data segment;
resampling the new time domain data sequence, and calculating by using the resampled time domain data sequence to obtain a frequency spectrum;
and if the maximum value of the amplitude in the frequency spectrum is greater than the fault alarm value, judging that the rolling bearing to be detected has a fault.
The invention also provides a rolling bearing fault diagnosis device which comprises a processor and a memory, wherein the processor is used for operating the program instructions stored in the memory so as to realize the rolling bearing fault diagnosis method.
The invention has the beneficial effects that: the method comprises the steps of calculating a peak index, a kurtosis index and a waveform index of a time domain data sequence of a vibration acceleration signal of the rolling bearing to be detected, when the value of one index is larger than a normal value of the index, carrying out high-pass filtering processing, segmentation processing and resampling on the time domain data sequence to obtain a frequency spectrum of the rolling bearing to be detected, and judging the fault of the rolling bearing to be detected when the maximum value of an amplitude value in the frequency spectrum is larger than a fault alarm value, wherein the step of judging the fault of the rolling bearing to be detected means that the rolling bearing to be detected is judged to have the possibility of the fault preliminarily. It can be seen that in the fault diagnosis process of the rolling bearing, the invention sets two judgment conditions: 1) the value of one index of the peak index, the kurtosis index and the waveform index is larger than the normal value of the index; 2) the maximum value of the amplitude in the frequency spectrum is greater than the fault alarm value; the rolling bearing to be detected is judged to have a fault only when the two judgment conditions are met simultaneously, so that the fault diagnosis process of the rolling bearing to be detected is more rigorous, the obtained fault diagnosis result is more reliable, and the fault diagnosis of the rolling bearing to be detected is realized according to the fact that the amplitude of the fault characteristic frequency of the frequency spectrum of the rolling bearing to be detected is contained in the first amplitude maximum values of the frequency spectrum when the fault occurs.
In order to determine whether the rolling bearing to be detected is in an outer ring fault, further, in the rolling bearing fault diagnosis method and device, frequency values corresponding to the maximum values of the first plurality of amplitude values in the frequency spectrum are extracted, and if a certain frequency value exists to enable an outer ring fault formula to be established, the rolling bearing to be detected is determined to be in an outer ring fault; the outer ring fault formula is as follows:
a×fBPFO-δ≤fm≤a×fBPFO+δ
wherein a is more than or equal to 1 and less than or equal to 4, a is an integer, fBPFOFor the outer ring fault characteristic frequency, δ is the correction error of the calculated frequency, fmIs the frequency value corresponding to the extracted mth maximum value of the amplitude.
In order to determine whether the rolling bearing to be detected is an inner ring fault, further, in the rolling bearing fault diagnosis method and device, frequency values corresponding to the maximum values of the first plurality of amplitude values in the frequency spectrum are extracted, and if a certain frequency value exists to enable an inner ring fault formula to be established, the rolling bearing to be detected is determined to be an inner ring fault; the inner ring fault formula consists of the following 3 formulas:
b×fr-δ≤fm≤b×fr+δ
fBPFI-(b-1)×fr-δ≤fm≤fBPFI-(b-1)×fr+δ
fBPFI+(b-1)×fr-δ≤fm≤fBPFI+(b-1)×fr+δ
wherein b is more than or equal to 1 and less than or equal to 3, b is an integer, frIs the reference axis frequency, and δ is the correction of the calculated frequencyPositive error, fmIs the frequency value corresponding to the extracted mth maximum value of the amplitude value, fBPFIIs the inner ring fault characteristic frequency.
In order to determine whether the rolling bearing to be detected is a rolling element fault, further, in the rolling bearing fault diagnosis method and device, frequency values corresponding to the maximum values of the first plurality of amplitude values in the frequency spectrum are extracted, and if a certain frequency value exists to enable a rolling element fault formula to be established, the rolling bearing to be detected is determined to be a rolling element fault; the rolling element fault formula consists of the following 5 formulas:
c×fFTF-δ≤fm≤c×fFTF+δ
fBSF-(d-1)×fFTF-δ≤fm≤fBSF-(d-1)×fFTF+δ
fBSF+(d-1)×fFTF-δ≤fm≤fBSF+(d-1)×fFTF+δ
2×fBSF-(d-1)×fFTF-δ≤fm≤2×fBSF-(d-1)×fFTF+δ
2×fBSF+(d-1)×fFTF-δ≤fm≤2×fBSF+(d-1)×fFTF+δ
wherein c is more than or equal to 1 and less than or equal to 3, d is more than or equal to 1 and less than or equal to 2, c and d are integers, delta is the correction error of the calculation frequency, fmIs the frequency value corresponding to the extracted mth maximum value of the amplitude value, fFTFIs the cage rotation frequency, fBSFIs the characteristic frequency of the rolling element fault.
In order to enable the collected vibration acceleration signal of the rolling bearing to be tested to contain the early fault characteristic signal of the rolling bearing to be tested so as to realize the diagnosis of the early fault of the rolling bearing to be tested, further, in the rolling bearing fault diagnosis method and device, the resonance frequency of the acceleration sensor is greater than 20 kHz.
In order to simplify the segmentation process and improve the fault diagnosis efficiency, further, in the rolling bearing fault diagnosis method and device, the data segments are divided into a plurality of data segments with equal length.
In order to enable the set fault alarm value to accord with the real condition of the rolling bearing and improve the accuracy of the fault diagnosis result of the rolling bearing, further, in the fault diagnosis method and the fault diagnosis device of the rolling bearing, the fault alarm value is 1.5-2 times of the maximum value of the amplitude value in the frequency spectrum of the rolling bearing to be detected in the normal state.
Drawings
FIG. 1 is a schematic view of the installation of a vibration acceleration signal collecting facility of a rolling bearing in an embodiment of the method of the present invention;
FIG. 2 is a flow chart of a rolling bearing fault diagnosis method of an embodiment of the method of the present invention;
FIG. 3 is a time domain waveform of a bearing under test in an embodiment of the method of the present invention;
FIG. 4 is a frequency spectrum diagram of a bearing under test corresponding to FIG. 3;
FIG. 5-a is a graph of envelope demodulation spectrum corresponding to the 0-240Hz band of FIG. 4;
FIG. 5-b is a graph of envelope demodulation spectrum corresponding to the 200Hz-440Hz band of FIG. 4;
FIG. 5-c is a graph of envelope demodulation spectrum for the 600Hz-900Hz band of FIG. 4;
FIG. 5-d is a graph of the envelope demodulation spectrum for the 1150Hz-1400Hz band of FIG. 4;
FIG. 6 is a frequency spectrum diagram of a bearing to be tested obtained by the rolling bearing fault diagnosis method in the embodiment of the method of the invention;
in the figure, 1 is an eddy current sensor, 2 is a key phase block, 3 is an acceleration sensor, and 4 is a motor.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
The method comprises the following steps:
the embodiment provides a rolling bearing fault diagnosis method (hereinafter referred to as fault diagnosis method) based on high-frequency pulse detection, the method not only can realize intelligent diagnosis of rolling bearing faults, but also can further confirm detailed fault types, manual intervention is not needed in the whole diagnosis process, and the obtained fault diagnosis result can objectively reflect the real fault condition of the rolling bearing.
Before the fault diagnosis is performed on the rolling bearing by using the fault diagnosis method of the embodiment, the following preparation work needs to be performed:
(1) vibration acceleration signal acquisition facility for installing rolling bearing
As shown in fig. 1, the rolling bearing vibration acceleration signal acquisition facility includes: an eddy current sensor 1, a key phase block 2 and an acceleration sensor 3. The eddy current sensor 1 is matched with the key phase block 2 for use and is used for measuring a reference value rotating speed; the acceleration sensor 3 is used for acquiring a vibration acceleration signal of the rolling bearing. In practical application, the motor 1 is connected with a reference rotor, the reference rotor is connected with a rolling bearing, the key phase block 2 is installed on the reference rotor, the eddy current sensor 1 is installed on the key phase block 2, and the acceleration sensor 3 is installed on the rolling bearing. As other embodiments, other types of rotation speed sensors may be used to measure the reference value rotation speed.
In this embodiment, according to the characteristic that the early failure symptom of the rolling bearing occurs in a high frequency band, the resonant frequency of the selected acceleration sensor is greater than 20kHz, so that the acquired vibration acceleration signal of the rolling bearing includes the early failure characteristic signal of the rolling bearing, and the early failure of the rolling bearing is diagnosed.
(2) Setting sampling parameters of acceleration sensor
In this embodiment, the sampling frequency and the number of sampling points (i.e., sampling parameters) of the acceleration sensor are set according to the following principle in combination with the reference shaft rotation speed/reference shaft rotation frequency:
f for setting sampling frequencysIs shown, and fsGenerally satisfies the formula fs≥1024×fr，frIs reference axis frequency conversion, and the number N of sampling points is more than or equal to 1024 Xf under the condition that the resolution is higher than 1Hz (the value is less than 1Hz)rAnd N is an integer power of 2.
The typical configuration of the sampling parameters is specifically shown in table 1:
TABLE 1 typical configuration table of acquisition parameters
Reference shaft speed (r/min)/reference shaft frequency fr(Hz) Sampling frequency fs(Hz) Number of sampling points N
600/10 3840 16384
600/10 5120 16384
600/10 10240 16384
1200/20 15360 16384
1200/20 20480 16384
1800/30 30720 32768
2400/40 40960 65536
3600/60 61440 65536
4800/80 92160 131072
5400/90 102400 131072
In this embodiment, a rolling bearing fault test is performed according to a rotary mechanical vibration test platform of a certain model, and frequency multiples of fault characteristic frequencies are calculated, where the inner ring is 7.1538, the outer ring is 4.8462, the rolling body is 2.5038, and the cage is 0.4038. The sampling parameters in table 1 are typically configured: the rotating speed of the reference shaft is 600r/min, and the sampling frequency fs3840Hz, and the number of sampling points N is 16384. Due to reference axis rotation frequency fr10Hz, the characteristic frequency f of the inner ring fault at this timeBPFI71.54Hz, outer ring fault characteristic frequency fBPFO48.46Hz characteristic frequency f of rolling element faultBSF25.04Hz, cage rotation frequency fFTF＝4.04Hz。
Wherein the reference axis is rotated by frCan be changed according to the table 1 when the reference axis is rotated by frCharacteristic frequency f of inner ring fault when changedBPFIOuter ring fault characteristic frequency fBPFORolling element failure characteristic frequency fBSFAnd cage rotation frequency fFTFChanges are made accordingly.
After the preparation work is done, the fault diagnosis method of the embodiment can be used to perform fault diagnosis on the rolling bearing to be tested (hereinafter referred to as the bearing to be tested), and the specific process is shown in fig. 2.
(1) Sampling data triggering mode takes a triggering reference rotor as a triggering reference, and sampling is carried outSampling in a synchronous whole period, and acquiring a time domain data sequence X of a vibration acceleration signal of the bearing to be detected0(N) (hereinafter referred to as sequence X)0(N))。
(2) Using sequence X0(N) calculating a peak index kCFKurtosis index kKFThe waveform index ksF。
Peak index kCFThe calculation formula of (2) is as follows:
kCF＝xp/xrms
kurtosis index kKFThe calculation formula of (2) is as follows:
Figure BDA0002097025150000071
waveform index ksFThe calculation formula of (2) is as follows:
Figure BDA0002097025150000072
wherein x ispIs | X0The maximum value of (N) | is,
Figure BDA0002097025150000073
xifor the acceleration value corresponding to the ith sampling point, the unit is m/s2，xjFor the acceleration value corresponding to the jth sampling point, in m/s2，
Figure BDA0002097025150000074
(3) Calculating the peak index k of the bearing under normal conditions according to the historical data of the bearing to be measuredCF0Kurtosis index kKF0The waveform index kSF0。
(4) Judgment of kCF＞kCF0Or kKF＞kKF0Or kSF＞kSF0And (5) if the judgment result is positive, executing the step (5).
(5) To pairSequence X0And (N) performing high-pass filtering processing and segmentation processing.
Firstly, a sequence X is obtained through high-pass filtering processing1(N), removing low-frequency interference parts, and generally selecting the cut-off frequency of a high-pass filter to be 1 kHz; then, for sequence X1(N) taking the absolute value to obtain the sequence X2(N), i.e. X2(N)＝|X1(N), and taking N as 8 as data segment length to divide sequence X into2(N) dividing the data into a plurality of data segments, selecting a maximum value in each data segment, assigning the maximum value to all data points in the data segment, and processing all the data segments in the same way to obtain a sequence X3(N) is provided. In this embodiment, the sequence X is divided into data segments with n-8 as the data segment length2(N) performing equal-length division, wherein as another embodiment, the length of the data segment set when the data segment is divided into equal-length segments can be adjusted according to actual needs; in addition, equal length division may not be performed.
(6) For sequence X3(N) resampling at a sampling frequency of
Figure BDA0002097025150000081
The number of sampling points is
Figure BDA0002097025150000082
Performing fast Fourier transform on the resampled time domain data sequence to obtain a frequency spectrum Y3(M). As other embodiments, other transformation methods in the prior art may also be selected to obtain the frequency spectrum.
(7) Setting an alarm value alarm (i.e. a failure alarm value) if the frequency spectrum Y3And (5) judging the fault of the bearing to be detected if the maximum amplitude value in the (M) is greater than the alarm value alarm. That is, two judgment conditions are simultaneously satisfied: 1) peak index kCFKurtosis index kKFSum waveform index ksFThe value of one index is larger than the normal value of the index; 2) frequency spectrum Y3And (M) judging the fault of the rolling bearing to be detected when the maximum amplitude value in the (M) is greater than the fault alarm value, wherein the judgment of the fault of the rolling bearing to be detected refers to the preliminary judgment of the possible fault of the rolling bearing to be detected.
The alarm value alarm is set according to experience and is generally set to be 1.5-2 times of the maximum value of the amplitude value in the frequency spectrum of the bearing to be measured in the normal state.
(8) And (5) after judging that the bearing to be tested has a fault, confirming whether the bearing to be tested has the fault by utilizing the steps 1) to 6).
1) Extracting the frequency spectrum Y3The first 20 amplitude maxima in (M) and the 20 frequency values corresponding to the first 20 amplitude maxima are calculated. Wherein the extracted mth maximum amplitude value is represented by Y3(Km) Is represented by m is an integer of 1 to 20, KmFor the extracted mth maximum of the amplitude in the frequency spectrum Y3(M) number, frequency value f corresponding to the mth maximum value of amplitudem＝(Km-1)×fsand/N. In this embodiment, the first 20 maximum amplitude values are extracted, and as another embodiment, the number of the extracted maximum amplitude values may be adjusted according to actual needs.
2) Obtaining the fault characteristic frequency of the bearing to be measured through table lookup or calculation, in the embodiment, the fault characteristic frequency f of the inner ringBPFI71.54Hz, outer ring fault characteristic frequency fBPFO48.46Hz characteristic frequency f of rolling element faultBSF25.04Hz, cage rotation frequency fFTF4.04Hz, setting the frequency selection bandwidth Delta 4Hz, and calculating the frequency correction error Delta 2 Hz.
3) And if one of the 20 frequency values enables an outer ring fault formula to be established, determining that the bearing to be tested is the outer ring fault. Wherein, the outer lane trouble formula is:
a×fBPFO-δ≤fm≤a×fBPFO+δ
in the formula, a is more than or equal to 1 and less than or equal to 4, and a is an integer.
4) And if one of the 20 frequency values enables an inner ring fault formula to be established, determining that the bearing to be tested is an inner ring fault. Wherein, the inner circle fault formula comprises the following 3 formulas:
b×fr-δ≤fm≤b×fr+δ
fBPFI-(b-1)×fr-δ≤fm≤fBPFI-(b-1)×fr+δ
fBPFI+(b-1)×fr-δ≤fm≤fBPFI+(b-1)×fr+δ
in the formula, b is more than or equal to 1 and less than or equal to 3, and b is an integer.
5) And if one of the 20 frequency values enables the rolling element fault formula to be established, determining that the bearing to be tested is the rolling element fault. Wherein, the rolling element fault formula consists of the following 5 formulas:
c×fFTF-δ≤fm≤c×fFTF+δ
fBSF-(d-1)×fFTF-δ≤fm≤fBSF-(d-1)×fFTF+δ
fBSF+(d-1)×fFTF-δ≤fm≤fBSF+(d-1)×fFTF+δ
2×fBSF-(d-1)×fFTF-δ≤fm≤2×fBSF-(d-1)×fFTF+δ
2×fBSF+(d-1)×fFTF-δ≤fm≤2×fBSF+(d-1)×fFTF+δ
in the formula, c is more than or equal to 1 and less than or equal to 3, d is more than or equal to 1 and less than or equal to 2, and c and d are integers.
6) And if the judgment results of the steps 3) to 5) are negative, judging whether other parts in the system are in fault.
The validity of the failure diagnosis method of the present embodiment is verified below.
Fig. 3 is a time domain waveform diagram of the bearing to be tested, and it is obvious from the diagram that there is periodic vibration impact, and it can be determined that the bearing to be tested has a fault, but detailed fault information is not easily seen on the time domain diagram.
Fig. 4 is a spectrogram of the bearing to be tested corresponding to fig. 3, where a plurality of peak positions are selected in the spectrogram to be used as an envelope demodulation spectrum comparison graph, the specific frequency bands are displayed by rectangular windows in the graph, the four frequency bands are 0-240Hz, 200Hz-440Hz, 600Hz-900Hz and 1150Hz-1400Hz, respectively, and the envelope demodulation spectrograms corresponding to the respective frequency bands are shown in fig. 5-a, fig. 5-b, fig. 5-c and fig. 5-d, respectively.
Fig. 5-a is an envelope demodulation frequency spectrum corresponding to the frequency band of 0-240Hz in fig. 4, in which only the fault characteristic frequency and harmonic of the retainer and the rolling element can be seen, and the amplitude is irregular and scattered, while the modulation information is almost no and the fault characteristic information of the rolling element is unclear.
Fig. 5-b is an envelope demodulation frequency spectrum corresponding to the frequency band of 200Hz to 440Hz in fig. 4, in which only the fault characteristic frequency and harmonic of the retainer can be seen, the amplitude is irregular and scattered, the modulation information is almost not available, and the fault characteristic information of the rolling element is unclear.
Fig. 5-c are envelope demodulation frequency spectrums corresponding to the frequency bands of 600Hz to 900Hz in fig. 4, in which only fault characteristic frequencies and harmonics of the retainer and the rolling elements can be seen, and the amplitudes are irregular and are relatively disordered, while modulation information is almost not available, and the fault characteristic information of the rolling elements is unclear.
Fig. 5-d is an envelope demodulation frequency spectrum corresponding to the frequency band of 1150Hz to 1400Hz in fig. 4, in which fault characteristic frequencies and harmonics of the retainer and the rolling element can be seen, the 1-time frequency and modulation information characteristics of the fault characteristic frequency of the rolling element are not obvious, and the 2-time frequency and modulation information are relatively clear.
FIG. 6 is a frequency spectrum diagram of a bearing under test obtained by the fault diagnosis method of the present embodiment, in which the characteristic frequency and harmonic f of the fault of the cage are shownFTF、2×fFTF、3×fFTFCharacteristic frequency f of rolling element failureBSFAnd a side band fFTF+fBSF、-fFTF+fBSFFrequency 2 multiplied by 2 xf of rolling element fault characteristic frequencyBSFAnd a side band fFTF+2×fBSF、-fFTF+2×fBSFAnd the side frequency band is amplitude modulation information of the characteristic frequency of the fault of the retainer, and the bearing fault is obviously found as a rolling body fault by contrasting a spectrogram.
In summary, according to the fault diagnosis method of the embodiment, the fault diagnosis of the bearing to be detected is realized according to the fact that the amplitude at the fault characteristic frequency of the frequency spectrum of the bearing to be detected is necessarily included in the first maximum amplitude values of the frequency spectrum when a fault occurs, the whole diagnosis process does not need manual intervention, not only is the intelligent fault diagnosis of the rolling bearing realized, but also the diagnosis result can objectively reflect the real fault condition of the bearing to be detected.
The embodiment of the device is as follows:
the rolling bearing fault diagnosis device of the embodiment comprises a processor and a memory, wherein the processor is used for operating program instructions stored in the memory to realize a rolling bearing fault diagnosis method, and the method is the same as the rolling bearing fault diagnosis method in the method embodiment and is not described again here.

Claims (5)

1. A method for diagnosing a failure of a rolling bearing, characterized by comprising the steps of:
acquiring a time domain data sequence of a vibration acceleration signal of a rolling bearing to be detected by using an acceleration sensor;
calculating a peak index, a kurtosis index and a waveform index by using the time domain data sequence;
if the value of one index is larger than the normal value of the index, performing high-pass filtering processing and segmentation processing on the time domain data sequence to obtain a new time domain data sequence; wherein the segmentation process comprises: taking an absolute value of the time domain data sequence after the high-pass filtering processing, dividing the time domain data sequence after the absolute value is taken into a plurality of data segments, and replacing all data values in each data segment by a maximum value in the data segment;
resampling the new time domain data sequence, and calculating by using the resampled time domain data sequence to obtain a frequency spectrum;
if the maximum value of the amplitude in the frequency spectrum is larger than the fault alarm value, judging that the rolling bearing to be tested has a fault;
extracting frequency values corresponding to the maximum values of the first plurality of amplitude values in the frequency spectrum, and if a certain frequency value exists to enable an outer ring fault formula to be established, determining that the rolling bearing to be tested is in an outer ring fault; the outer ring fault formula is as follows:
a×fBPFO-δ≤fm≤a×fBPFO+δ
wherein a is more than or equal to 1 and less than or equal to 4, a is an integer, fBPFOFor the outer ring fault characteristic frequency, δ is the correction error of the calculated frequency, fmIs the m-th maximum of the amplitude extractedA corresponding frequency value;
extracting frequency values corresponding to the maximum values of the first plurality of amplitude values in the frequency spectrum, and if a certain frequency value exists to enable an inner ring fault formula to be established, determining that the rolling bearing to be tested is an inner ring fault; the inner ring fault formula consists of the following 3 formulas:
b×fr-δ≤fm≤b×fr+δ
fBPFI-(b-1)×fr-δ≤fm≤fBPFI-(b-1)×fr+δ
fBPFI+(b-1)×fr-δ≤fm≤fBPFI+(b-1)×fr+δ
wherein b is more than or equal to 1 and less than or equal to 3, b is an integer, frIs the reference axis frequency, delta is the correction error of the calculated frequency, fmIs the frequency value corresponding to the extracted mth maximum value of the amplitude value, fBPFIIs the inner ring fault characteristic frequency;
extracting frequency values corresponding to the maximum values of the first plurality of amplitude values in the frequency spectrum, and if a certain frequency value exists to enable a rolling element fault formula to be established, determining that the rolling bearing to be tested is a rolling element fault; the rolling element fault formula consists of the following 5 formulas:
c×fFTF-δ≤fm≤c×fFTF+δ
fBSF-(d-1)×fFTF-δ≤fm≤fBSF-(d-1)×fFTF+δ
fBSF+(d-1)×fFTF-δ≤fm≤fBSF+(d-1)×fFTF+δ
2×fBSF-(d-1)×fFTF-δ≤fm≤2×fBSF-(d-1)×fFTF+δ
2×fBSF+(d-1)×fFTF-δ≤fm≤2×fBSF+(d-1)×fFTF+δ
wherein c is more than or equal to 1 and less than or equal to 3, d is more than or equal to 1 and less than or equal to 2, c and d are integers, delta is the correction error of the calculation frequency, fmIs the frequency value corresponding to the extracted mth maximum value of the amplitude value, fFTFIs the cage rotation frequency, fBSFIs a rolling bodyThe characteristic frequency of the fault.
2. The rolling bearing fault diagnosis method according to claim 1, characterized in that the resonance frequency of the acceleration sensor is greater than 20 kHz.
3. The rolling bearing fault diagnosis method according to claim 1, wherein the division into the plurality of data segments is an equal-length division into a plurality of data segments.
4. The rolling bearing fault diagnosis method according to claim 1, wherein the fault alarm value is 1.5 to 2 times the maximum value of the amplitude in the frequency spectrum of the rolling bearing under test in the normal state.
5. A rolling bearing failure diagnosis apparatus comprising a processor and a memory, the processor being configured to execute program instructions stored in the memory to implement the rolling bearing failure diagnosis method according to any one of claims 1 to 4.
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