CN113702041B - Bearing fault diagnosis method - Google Patents
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- CN113702041B CN113702041B CN202111096662.XA CN202111096662A CN113702041B CN 113702041 B CN113702041 B CN 113702041B CN 202111096662 A CN202111096662 A CN 202111096662A CN 113702041 B CN113702041 B CN 113702041B
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- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000003745 diagnosis Methods 0.000 title claims abstract description 17
- 230000002159 abnormal effect Effects 0.000 claims abstract description 41
- 238000001514 detection method Methods 0.000 claims abstract description 20
- 238000012360 testing method Methods 0.000 claims abstract description 18
- 238000001228 spectrum Methods 0.000 claims abstract description 15
- 238000009434 installation Methods 0.000 claims abstract description 7
- 238000013499 data model Methods 0.000 claims description 9
- 238000006243 chemical reaction Methods 0.000 abstract description 4
- 230000001133 acceleration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005336 cracking Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 238000004901 spalling Methods 0.000 description 1
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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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
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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
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- General Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
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Abstract
The invention discloses a bearing fault diagnosis method, which relates to the field of bearing fault diagnosis, and comprises the following steps: step one: appearance detection: detecting an inner ring and an outer ring of the bearing; step two: and (3) working test: the bearing is subjected to installation test, noise, temperature and vibration frequency generated by the bearing are recorded in the test process, and the process is kept for 10min; step three: and (3) frequency treatment: and (3) frequency treatment: carrying out Fourier change on the vibration signals acquired by the acquisition device to generate a Fourier spectrum, converting the spectrum into polar coordinates according to the Fourier spectrum in the conversion process, and displaying a chart of vibration frequency and time through data characteristic analysis in the polar coordinates; whether abnormal data need to be removed or not is judged through the change relation between the temperature and the noise, so that the accuracy of a produced bearing frequency model can be ensured, and the accuracy of bearing detection is further improved.
Description
Technical Field
The invention relates to the technical field of bearing fault diagnosis, in particular to a bearing fault diagnosis method.
Background
The bearing is an important part in mechanical equipment, and has the main functions of supporting a mechanical rotating body, reducing the friction coefficient in the motion process and ensuring the rotation precision;
During use, the bearing may fail, thereby generating a lot of noise and vibration, and further reducing the service life of the bearing itself, and the bearing typically fails in the form of metal spalling on the raceway surface, plastic deformation of the bearing, bearing race cracking, cage chipping, and severe wear of the race raceway.
Through retrieval, the invention patent with the Chinese patent number of CN207652325U provides a fault diagnosis method for a rolling bearing, which comprises a controller which is pre-recorded with normal working parameter values of the bearing, a temperature sensor, a rotating speed sensor and an acceleration sensor which collect the working parameters of the bearing in real time, and a quasi-fault identifier is recorded for the parameters which are larger than the preset parameter values by comparing the collected parameter values with the preset parameter values. And judging whether the bearing fails or not by comparing the primary quasi-fault identification at the beginning of the time length t with the quasi-fault identification at the end of the time length t. The invention has simple principle, effectively avoids judging the normal fluctuation of the single parameter in the working process as working fault, and has strong practicability;
However, the above method only directly compares the vibration frequency of the bearing and other parameters of the bearing with the highest threshold, but because the vibration frequency has noise generated by the driving device, and the vibration frequency has a part of abnormal data, the compared result deviates, and the detection result is wrong.
Disclosure of Invention
The invention aims to solve the defects that in the prior art, noise generated by a driving device is generated in the vibration frequency, and part of abnormal data is generated in the vibration frequency, so that the result is easy to be wrong.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
A bearing fault diagnosis method comprising the steps of:
step one: appearance detection: detecting an inner ring and an outer ring of the bearing;
Step two: and (3) working test: the bearing is subjected to installation test, noise, temperature and vibration frequency generated by the bearing are recorded in the test process, and the process is kept for 10min;
Step three: and (3) frequency treatment: carrying out Fourier change on the vibration signals acquired by the acquisition device to generate a Fourier spectrum, converting nodes in the Fourier spectrum into polar coordinates, and analyzing data characteristics in the polar coordinates to generate a vibration frequency-time chart;
Step four: and (3) data processing: removing abnormal data in the vibration frequency-time chart according to a graph drawn by temperature and noise to correct the vibration frequency-time chart data, and generating a frequency model according to the vibration frequency-time chart from which the abnormal data are removed;
Step five: and (3) model comparison: and comparing the corrected frequency model with the existing problem model in the database to determine the failure cause of the bearing.
The technical scheme further comprises the following steps:
the noise generated during the operation of the driving device is collected in the process of detecting the bearing noise, and then the noise generated during the operation of the driving device is removed on the basis of the bearing noise, so that the real noise generated during the operation of the bearing can be obtained.
The vibration of the bearing is collected by a collector, and then the collected signal is amplified by an amplifier.
In data processing, abnormal data in the vibration frequency is collected, then bearing noise and temperature changes in the front 0.5s and the rear 0.5s of the abnormal vibration frequency are compared, and if the temperature and the noise do not change greatly, the data are removed.
If the abnormal vibration frequency is changed greatly in any one of the noise and the temperature of the bearing in the front 0.5s and the rear 0.5s, the abnormal vibration frequency is judged to be abnormal if the noise exceeds 80 decibels, the abnormal vibration frequency is judged to be abnormal if the temperature exceeds 80 ℃, and the data are required to be stored.
The model is used for modeling the vibration frequency data after abnormal data are removed, and then the vibration frequency data of 0-1min, 1-2min, 2-5min and 5-10min are respectively modeled.
And respectively comparing the vibration frequency data models of 0-1min, 1-2min, 2-5min and 5-10min with the problem model, and simultaneously comparing the temperature and the noise to realize the diversity detection of bearing damage.
Compared with the prior art, the invention has the beneficial effects that:
1. in the invention, in the process of processing abnormal data in data processing, whether the abnormal data need to be removed is judged by comparing the time period of the abnormal data with the noise and the temperature change amplitude, so that the accuracy of a produced bearing frequency model can be ensured, and the accuracy of bearing detection is further improved.
2. According to the invention, the accuracy of diagnosis can be increased by comparing the whole vibration frequency model with the vibration frequency model with multiple time periods in the model comparison, and the temperature and the noise are compared, so that the diversity detection of bearing damage is realized, and the whole diagnosis of the cause of bearing faults is ensured.
Detailed Description
The technical scheme of the invention is further described below with reference to specific embodiments.
A bearing fault diagnosis method comprising the steps of:
step one: appearance detection: detecting an inner ring and an outer ring of the bearing;
Step two: and (3) working test: the method comprises the steps of performing an installation test on a bearing, recording noise, temperature and vibration frequency generated by the bearing in the test process, keeping the process for 10min, collecting the noise generated by the driving device during the operation of the bearing noise in the detection process, and removing the noise generated by the driving device during the operation of the driving device on the basis of the bearing noise to obtain real noise generated by the bearing during the operation;
Step three: and (3) frequency treatment: carrying out Fourier change on the vibration signals acquired by the acquisition device to generate a Fourier spectrum, converting nodes in the Fourier spectrum into polar coordinates, and analyzing data characteristics in the polar coordinates to generate a vibration frequency-time chart;
Step four: and (3) data processing: removing abnormal data in the vibration frequency-time chart according to a graph drawn by temperature and noise to correct the vibration frequency-time chart data, and generating a frequency model according to the vibration frequency-time chart from which the abnormal data are removed;
Collecting abnormal data in the vibration frequency, comparing bearing noise and temperature change in the front 0.5s and the rear 0.5s of the vibration frequency, wherein the noise is judged to be abnormal when the noise exceeds 80 db, the temperature is judged to be abnormal when the temperature exceeds 80 ℃, and eliminating the data if the temperature and the noise are not changed greatly;
If the vibration frequency is changed greatly in any one of the bearing noise and the temperature in the front 0.5s and the rear 0.5s, the data are required to be stored;
Step five: and (3) model comparison: comparing the corrected frequency model with the existing problem model in the database, determining the fault cause of the bearing, respectively modeling vibration frequency data of 0-1min, 1-2min, 2-5min and 5-10min, comparing the established vibration frequency data model with the known problem model in the database, determining the problem of the bearing, respectively comparing the vibration frequency data models of 0-1min, 1-2min, 2-5min and 5-10min with the problem model, and simultaneously comparing the temperature and the noise to realize diversity detection of bearing damage.
Example 1
The first step: firstly detecting an inner ring and an outer ring of a bearing, then performing installation test on the bearing, detecting vibration frequency signals of the bearing through a collector in the process of testing, detecting the working temperature of the bearing through a temperature sensor, detecting the working noise of the bearing through a noise sensor, installing the noise sensor at a driving position, removing the working noise of a driving device on the basis of the bearing noise, and obtaining real noise when the bearing works, wherein the whole detection process is maintained at 10min;
And a second step of: carrying out Fourier change on the vibration signals acquired by the acquisition device to generate a Fourier spectrum, converting nodes in the Fourier spectrum into polar coordinates through conversion, and analyzing data characteristics in the polar coordinates to generate a vibration frequency-time chart;
And a third step of: collecting abnormal data in the vibration frequency (wherein the noise is abnormal when the noise exceeds 80 dB and the temperature is abnormal when the noise exceeds 80 ℃, then comparing bearing noise and temperature changes in the front 0.5s and the rear 0.5s of the vibration frequency, and eliminating the data if the temperature and the noise do not change greatly;
If the variation in either the bearing noise or the temperature is large in the first 0.5s and the second 0.5s of the vibration frequency, the data needs to be stored.
Fourth step: according to the graph drawn by temperature and noise, eliminating abnormal data in a vibration frequency-time graph, correcting the vibration frequency-time graph data, generating a frequency model according to the vibration frequency-time graph eliminating the abnormal data, respectively modeling the vibration frequency data of 0-1min, 1-2min, 2-5min and 5-10min, comparing the established vibration frequency data model with known problem models in a database, determining the problem of the bearing, comparing the vibration frequency data models of 0-1min, 1-2min, 2-5min and 5-10min with the problem models, simultaneously comparing the temperature and the noise, realizing the diversity detection of bearing damage, and ensuring the complete diagnosis of the bearing failure cause.
Example two
The first step: firstly detecting an inner ring and an outer ring of a bearing, then performing installation test on the bearing, detecting vibration frequency signals of the bearing through a collector in the process of testing, detecting the working temperature of the bearing through a temperature sensor, detecting the working noise of the bearing through a noise sensor, installing the noise sensor at a driving position, removing the working noise of a driving device on the basis of the bearing noise, and obtaining real noise when the bearing works, wherein the whole detection process is maintained at 10min;
And a second step of: carrying out Fourier change on the vibration signals acquired by the acquisition device to generate a Fourier spectrum, converting nodes in the Fourier spectrum into polar coordinates through conversion, and analyzing data characteristics in the polar coordinates to generate a vibration frequency-time chart;
And a third step of: and (3) integrally modeling the vibration frequency data, then respectively modeling the vibration frequency data of 0-1min, 1-2min, 2-5min and 5-10min, comparing the established vibration frequency data model with a known problem model in a database to determine the problem of the bearing, comparing the vibration frequency data models of 0-1min, 1-2min, 2-5min and 5-10min with the problem model, and simultaneously comparing the temperature and the noise, thereby realizing the diversity detection of the damage to the bearing and ensuring the complete diagnosis of the failure cause of the bearing.
Example III
The first step: firstly detecting an inner ring and an outer ring of a bearing, then performing installation test on the bearing, detecting vibration frequency signals of the bearing through a collector in the process of testing, detecting the working temperature of the bearing through a temperature sensor, detecting the working noise of the bearing through a noise sensor, installing the noise sensor at a driving position, removing the working noise of a driving device on the basis of the bearing noise, and obtaining real noise when the bearing works, wherein the whole detection process is maintained at 10min;
And a second step of: carrying out Fourier change on the vibration signals acquired by the acquisition device to generate a Fourier spectrum, converting nodes in the Fourier spectrum into polar coordinates through conversion, and analyzing data characteristics in the polar coordinates to generate a vibration frequency-time chart;
And a third step of: collecting abnormal data in the vibration frequency, comparing bearing noise and temperature changes in the front 0.5s and the rear 0.5s of the vibration frequency, and eliminating the data if the temperature and the noise have no large changes (wherein the noise is judged to be abnormal when the noise exceeds 80 db, and the temperature is judged to be abnormal when the noise exceeds 80 ℃;
If the variation in either the bearing noise or the temperature is large in the first 0.5s and the second 0.5s of the vibration frequency, the data needs to be stored.
Fourth step: and eliminating abnormal data in the vibration frequency-time chart according to a graph drawn by the temperature and the noise to correct the vibration frequency-time chart data, generating a frequency model according to the vibration frequency-time chart with the abnormal data eliminated, comparing the established vibration frequency data model with a known problem model in a database, and determining the problem of the bearing.
In summary, in the third embodiment, the diagnosis result obtained in the first embodiment is basically consistent with the damage condition of the bearing, while some of the detection results obtained in the second embodiment are that the bearing is not problematic, and the detection result obtained in the third embodiment is that the bearing is lack of problems originally existing in the bearing, so the method implemented in the first embodiment is most effective and accurate.
The present invention is not limited to the above-mentioned embodiments, and any person skilled in the art, based on the technical solution of the present invention and the inventive concept thereof, can be replaced or changed within the scope of the present invention.
Claims (4)
1. A bearing failure diagnosis method, characterized by comprising the steps of:
step one: appearance detection: detecting an inner ring and an outer ring of the bearing;
Step two: and (3) working test: the bearing is subjected to installation test, noise, temperature and vibration frequency generated by the bearing are recorded in the test process, and the process is kept for 10min;
Step three: and (3) frequency treatment: carrying out Fourier change on the vibration signals acquired by the acquisition device to generate a Fourier spectrum, converting nodes in the Fourier spectrum into polar coordinates, and analyzing data characteristics in the polar coordinates to generate a vibration frequency-time chart;
Step four: and (3) data processing: removing abnormal data in the vibration frequency-time chart according to a graph drawn by temperature and noise to correct the vibration frequency-time chart data, and generating a frequency model according to the vibration frequency-time chart of which the abnormal data is removed;
Step five: and (3) model comparison: comparing the frequency model with the existing problem model in the database to determine the failure cause of the bearing; the method comprises the following steps: modeling is carried out by eliminating vibration frequency data after abnormal data, and then vibration frequency data of 0-1min, 1-2min, 2-5min and 5-10min are respectively modeled; and respectively comparing the vibration frequency data models of 0-1min, 1-2min, 2-5min and 5-10min with the problem model, and simultaneously comparing the temperature and the noise to realize the diversity detection of bearing damage.
2. The bearing fault diagnosis method according to claim 1, wherein noise generated when the driving device is operated is collected during detection of bearing noise, and then noise generated when the driving device is operated is removed on the basis of the bearing noise, so that real noise generated when the bearing is operated is obtained.
3. A bearing failure diagnosis method according to claim 1, wherein vibration of the bearing is collected by a collector, and then the collected signal is amplified by an amplifier.
4. The method for diagnosing bearing faults as claimed in claim 1, wherein abnormal data in the vibration frequency is collected in the data processing, and then bearing noise and temperature changes in the front 0.5s and the rear 0.5s of the abnormal vibration frequency are compared, and if the temperature and the noise have no large changes, the data are removed;
If the abnormal vibration frequency is changed greatly in any one of the noise and the temperature of the bearing in the front 0.5s and the rear 0.5s, the abnormal vibration frequency is judged to be abnormal if the noise exceeds 80 decibels, the abnormal vibration frequency is judged to be abnormal if the temperature exceeds 80 ℃, and the data are required to be stored.
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| CN101430240A (en) * | 2008-11-28 | 2009-05-13 | 华北电力大学 | On-line real-time diagnosis method for parallel misalignment fault of coupling |
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| CN109708891A (en) * | 2019-01-30 | 2019-05-03 | 华南理工大学 | A kind of flexibility elliptic bearing raceway method for diagnosing faults |
| CN110455396A (en) * | 2018-05-07 | 2019-11-15 | 光子瑞利科技（北京）有限公司 | Mechanical breakdown method for detecting vibration and system based on Fibre Optical Sensor |
| CN111060317A (en) * | 2020-01-03 | 2020-04-24 | 上海电器科学研究所（集团）有限公司 | Method for judging fault signal of rolling bearing of mining fan motor |
| CN113049251A (en) * | 2021-03-16 | 2021-06-29 | 哈工大机器人（合肥）国际创新研究院 | Bearing fault diagnosis method based on noise |
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- 2021-09-18 CN CN202111096662.XA patent/CN113702041B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101430240A (en) * | 2008-11-28 | 2009-05-13 | 华北电力大学 | On-line real-time diagnosis method for parallel misalignment fault of coupling |
| CN107167318A (en) * | 2017-06-19 | 2017-09-15 | 北京时代龙城科技有限责任公司 | A kind of quick failure diagnostic apparatus of bearing intelligent and diagnostic method |
| CN110455396A (en) * | 2018-05-07 | 2019-11-15 | 光子瑞利科技（北京）有限公司 | Mechanical breakdown method for detecting vibration and system based on Fibre Optical Sensor |
| CN109708891A (en) * | 2019-01-30 | 2019-05-03 | 华南理工大学 | A kind of flexibility elliptic bearing raceway method for diagnosing faults |
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