CN115356109B - Rolling bearing fault identification method and system - Google Patents
Rolling bearing fault identification method and system Download PDFInfo
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- 238000005096 rolling process Methods 0.000 title claims abstract description 74
- 238000000034 method Methods 0.000 title claims abstract description 26
- 230000002159 abnormal effect Effects 0.000 claims abstract description 24
- 230000000737 periodic effect Effects 0.000 claims description 8
- 238000001514 detection method Methods 0.000 claims description 5
- 238000013075 data extraction Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 238000013473 artificial intelligence Methods 0.000 abstract description 2
- 230000001133 acceleration Effects 0.000 description 4
- 230000000739 chaotic effect Effects 0.000 description 3
- 230000005856 abnormality Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 238000005299 abrasion Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
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- 238000010586 diagram Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000004519 manufacturing process 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
- G01M13/045—Acoustic or vibration analysis
Abstract
The invention discloses a method and a system for identifying faults of a rolling bearing, which relate to the field of artificial intelligence, and the method comprises the following steps: acquiring all vibration signals of the rolling bearing to be detected from the beginning of running to the current period; obtaining the information entropy fluctuation range of the vibration signals by utilizing the variance and the local information entropy corresponding to each vibration signal; acquiring a vibration signal corresponding to a local information entropy in an information entropy fluctuation range of the vibration signal and marking the vibration signal as an initial target vibration signal; obtaining a plurality of target vibration signals belonging to the same periodicity by utilizing the time difference between any two initial target vibration signals; performing curve fitting on local information entropy of the target vibration signals with the same periodicity to obtain a fitted curve; the slope of each point on each fitting curve is clustered to obtain multiple types of slopes, and whether the current rolling bearing is abnormal or not is determined by utilizing the difference value between the average values of the slopes of each type.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a rolling bearing fault identification method and system.
Background
The rolling bearing is one of common parts in mechanical equipment, is used for reducing the loss caused by friction between shafts in the operation process, can play a good supporting role in normal operation of parts between the shafts, and can lead to the reduction of the operation precision of the shafts and the part damage caused by the stress concentration of part when the bearing in the mechanical equipment is damaged, so that the whole equipment cannot be used, the production efficiency is influenced, and even safety accidents can be possibly caused.
At present, most of collected vibration signals of the rolling bearing are utilized to carry out fault identification on the rolling bearing, the vibration signals of the rolling bearing in the current period and the vibration signals of the rolling bearing in the initial normal period are subjected to similarity measurement, and a threshold value is set according to a similarity measurement result to judge whether the rolling bearing is abnormal or not.
Disclosure of Invention
The invention provides a method and a system for identifying faults of a rolling bearing, which are used for automatically analyzing vibration signals of the rolling bearing to obtain the fault identification result of the current rolling bearing.
The invention relates to a rolling bearing fault identification method, which adopts the following technical scheme:
acquiring all vibration signals of the rolling bearing to be detected from the beginning of running to the current period;
selecting a signal segment with set time length by taking each vibration signal as a center as a target signal segment of the vibration signal;
obtaining local information entropy of each vibration signal by using signal values of all vibration signals in a target signal section of the vibration signal;
acquiring variances of signal values of all vibration signals in a target signal section of each vibration signal, and obtaining an information entropy fluctuation range of the vibration signal by utilizing the variances and local information entropy corresponding to each vibration signal;
acquiring a vibration signal corresponding to a local information entropy in an information entropy fluctuation range of the vibration signal and marking the vibration signal as an initial target vibration signal;
dividing the initial target vibration signal into a plurality of target vibration signals with different periodicity by using the time difference between any two initial target vibration signals;
performing curve fitting on local information entropy of each target vibration signal belonging to the same periodicity to obtain a fitted curve;
and clustering the slope of each point on each fitting curve to obtain multiple types of slopes, and determining whether the current rolling bearing is abnormal or not by utilizing the difference value between the average values of the slopes of each type.
Further, the length of the target signal segment is greater than the rolling bearing rotation period.
Further, the step of obtaining the information entropy fluctuation range of the vibration signal by using the variance and the local information entropy corresponding to each vibration signal includes:
respectively acquiring the mean value of the variances of all vibration signals and the mean value of the local information entropy as a target variance and a target local information entropy;
subtracting the target variance from the target local information entropy to obtain the minimum value of the information entropy fluctuation range of the vibration signal;
utilizing the target local information entropy plus the target variance as the maximum value of the information entropy fluctuation range of the vibration signal;
and taking a section formed by the maximum value and the minimum value of the information entropy fluctuation range as the information entropy fluctuation range of the vibration signal.
Further, the step of dividing the initial target vibration signal into a plurality of different periodic target vibration signals using a time difference between any two of the initial target vibration signals includes:
acquiring the time difference between any two initial target vibration signals;
the vibration signals with equal time differences and rotation periods and time intervals of the rotation periods are divided into target vibration signals of the same period.
Further, the slope of each point on the fitted curve is classified by a k-means algorithm to obtain two kinds of slopes.
Further, the step of determining whether the current rolling bearing has an abnormality using the difference between the slope means of each type includes:
acquiring the average value of all slopes in each type of slope, and acquiring the difference value between the average values of the two types of slopes;
when the difference value is larger than a preset abnormal threshold value, the rolling bearing is considered to have faults;
and when the difference value is not larger than the preset abnormal threshold value, the rolling bearing is considered to have no fault.
A rolling bearing failure recognition system, comprising:
and a data acquisition module: the method comprises the steps of acquiring all vibration signals of a rolling bearing to be detected from the beginning of running to the current period, and taking each vibration signal as a center to select a signal section with set time length as a target signal section of the vibration signal;
and a data processing module: the method comprises the steps of obtaining local information entropy of each vibration signal by using signal values of all vibration signals in a target signal section of the vibration signal; acquiring variances of signal values of all vibration signals in a target signal section of each vibration signal, and obtaining an information entropy fluctuation range of the vibration signal by utilizing the variances and local information entropy corresponding to each vibration signal;
and a data extraction module: the vibration signal corresponding to the local information entropy in the information entropy fluctuation range of the vibration signal is recorded as an initial target vibration signal; dividing the initial target vibration signal into a plurality of target vibration signals with different periodicity by using the time difference between any two initial target vibration signals;
and a detection and identification module: the method comprises the steps of performing curve fitting on local information entropy of each target vibration signal belonging to the same periodicity to obtain a fitted curve; and clustering the slope of each point on each fitting curve to obtain multiple types of slopes, and determining whether the current rolling bearing is abnormal or not by utilizing the difference value between the average values of the slopes of each type.
The beneficial effects of the invention are as follows: according to the rolling bearing fault identification method and system, the information entropy fluctuation range is determined through the variance and the local information entropy, the vibration signals in the information entropy fluctuation range in all vibration signals are extracted by utilizing the information entropy fluctuation range, the local information entropy is expanded into the information entropy fluctuation range by utilizing the variance, and the information entropy is used as the fault tolerance range of data acquisition errors, so that normal points can be reserved as far as possible when noise points are removed; screening an initial target vibration signal by utilizing an information entropy fluctuation range to obtain a vibration signal from which part of noise points are removed; the periodic vibration signals are screened out through the rotation period, so that the vibration signals without periodic regular noise can be filtered out, and the follow-up fault identification result is more accurate; after the interference of noise points is removed, curve fitting is carried out on the local information entropy of all vibration signals, and whether the rolling bearing in the current period is abnormal or not is judged by utilizing the slope, so that the automatic analysis of the fault identification of the rolling bearing is realized.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other 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 the general steps of an embodiment of a method for identifying a rolling bearing failure of the present invention;
fig. 2 is a schematic structural diagram of a rolling bearing failure recognition system according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
An embodiment of a rolling bearing failure recognition method of the present invention, as shown in fig. 1, includes:
s1, acquiring all vibration signals of a rolling bearing to be detected from the beginning to the current period; and selecting a signal segment with a set time length by taking each vibration signal as a center as a target signal segment of the vibration signal.
Specifically, a miniature acceleration sensor is arranged on a mechanical part of the rolling bearing for collecting vibration signals of the rolling bearing. All vibration signals from the beginning of the running of the rolling bearing to the current moment are acquired by a micro acceleration sensor.
In the scheme, the length of the selected time window is 0.5ms, an implementer can adjust according to specific implementation scenes, and the time and the rotating speed required by one circle of rotation of the rolling bearing can be utilized to obtain the rotation period of the rolling bearing.
All vibration signals in the time window of each vibration signal are collectively referred to as the target signal segment for that vibration signal.
S2, obtaining local information entropy of each vibration signal by using signal values of all vibration signals in a target signal segment of the vibration signal; and acquiring variances of signal values of all vibration signals in the target signal section of each vibration signal, and obtaining an information entropy fluctuation range of the vibration signal by utilizing the variances and the local information entropy corresponding to each vibration signal.
If the historical vibration data of the rolling bearing are abnormal, vibration signal data can be relatively chaotic, even if the rolling bearing is used normally, vibration can be generated, noise can be generated during signal acquisition, so that fault detection of the rolling bearing is affected, the abnormal degree of the vibration signal can be represented by calculating the local information entropy of the vibration signal, and the less chaotic the target signal section is, namely, the smaller the entropy value is when the signal value of the vibration signal is changed uniformly; the more chaotic the vibration signal in the target signal section, namely, the more irregular the change of the signal value, the larger the entropy value, the more likely the phenomenon caused by the fault of the rolling bearing, and the signal value refers to the signal data of the vibration signal acquired by the micro acceleration sensor, namely, the acceleration signal value of the vibration signal.
Specifically, the local information entropy of each vibration signal segment is calculated by using the signal value of each vibration signal in the target signal segment of each vibration signal, that is, the information entropy of the signal value in the target signal segment is calculated by using the probability that the signal values of all vibration signals in the target signal segment appear, and is used as the local information entropy of the target signal segment, the local information entropy of the target signal segment is used as the local information entropy of the corresponding vibration signal, and the calculation of the information entropy is not described in detail herein, so that the local information entropy of each vibration signal is obtained by using the method for obtaining the local information entropy of the vibration signal.
The vibration signals are collected with more noise, so that even normal signals with the same periodicity have fluctuation, the vibration signals with the same periodicity refer to vibration signals with the same corresponding position in different vibration periods, for example, peak signals of each vibration period belong to the same periodicity, because the collected vibration signals have interference of noise signals, and certain error exists in the process of collecting the signals, if the periodicity of the vibration signals is determined by using the rule that the vibration signals with completely equal local entropy belong to the same periodic vibration signals, effective periodicity cannot be found due to noise interference, and local information entropy obtained by calculation of the vibration signals with the same periodicity is not completely equal, so that the influence of noise is required to be reduced in a certain fault tolerance range. Namely, the vibration signals belonging to the same periodicity are determined by calculating the information entropy fluctuation range of each vibration signal, so that more vibration signals possibly belonging to the same periodicity can be reserved, and the periodicity result is more accurate.
Specifically, calculating variances of signal values of all vibration signals in a target signal section of each vibration signal, acquiring a mean value of the variances of all vibration signals as a target variance, and acquiring a mean value of local information entropy of all vibration signals as a target local information entropy; subtracting the target variance from the target local information entropy to obtain the minimum value of the information entropy fluctuation range of the vibration signal, adding the target variance to the target local information entropy to obtain the maximum value of the information entropy fluctuation range of the vibration signal, and using the interval formed by the maximum value and the minimum value of the information entropy fluctuation range as the information entropy fluctuation range of the vibration signal, namely the information entropy fluctuation range isWherein->Target local information entropy representing vibration signal, +.>Representing the target variance of the vibration signal.
If the local information entropy fluctuation of the surrounding vibration signals of the vibration signals is larger, the instability is larger, and the corresponding information entropy value latitude should be larger, so that the local information entropy of the vibration signals is adjusted by using the variance to obtain the information entropy fluctuation range.
S3, acquiring a vibration signal corresponding to a local information entropy in an information entropy fluctuation range of the vibration signal, and marking the vibration signal as an initial target vibration signal; the initial target vibration signal is divided into a plurality of different periodic target vibration signals using a time difference between any two of the initial target vibration signals.
Specifically, vibration signals belonging to the information entropy fluctuation range in all the vibration signals are obtained as initial target vibration signals, and the vibration signals with partial noise points removed are obtained.
Under the condition that the rotation speed of the bearing is unchanged, the time of one rotation is also unchanged, namely the rotation period is unchanged, and the rolling bearing does not have self-repairing capability, so that the rolling bearing can always exist after faults occur, and the faults of the rolling bearing can be more and more obvious along with long-time use, the local information entropy value can be increased, the faults of the rolling bearing can show periodicity, however, noise has stronger unstable factors, and therefore the periodicity of the local information entropy of a vibration signal can be utilized to remove noise points.
Specifically, the time difference between any two initial target vibration signals is obtained, all initial target vibration signals with equal time difference and rotation period and time interval being the rotation period are obtained as target vibration signals belonging to the same period, and all the target vibration signals with the periodicity can be obtained by traversing all the initial target vibration signals.
All the obtained target vibration signals are normal signals and abnormal signals in all the vibration signals after the noise point is removed.
S4, performing curve fitting on local information entropy of each target vibration signal belonging to the same periodicity to obtain a fitted curve; and clustering the slope of each point on each fitting curve to obtain slopes of a plurality of categories, and determining whether the current rolling bearing is abnormal or not by utilizing the difference value between the average values of the slopes of each category.
Abnormal signals can be screened out by utilizing the local information entropy of the vibration signals with the same periodicity. Specifically, a least square method curve fitting is performed on local information entropy of vibration signals belonging to the same periodicity, and a fitting curve is obtained. If the rolling bearing has faults, fault abnormality is more and more obvious along with long-time use of the rolling bearing, and the local information entropy value is increased, so that a fitting curve of the local information entropy approximates to a monotonically increasing curve.
If the increasing rate of the fitting curve is slow and relatively consistent, the local information entropy of the vibration signal caused by normal abrasion of the rolling bearing is considered to be gradually increased; if a part with local high-speed increment appears in the fitting curve of the local information entropy, the rolling bearing is indicated to have faults.
Specifically, slope values of each point on a fitting curve are obtained, all slopes are classified by using a k-means algorithm to obtain two types of slopes, the two types of slopes are respectively averaged and the obtained average value is subjected to difference to obtain two types of slope difference values, an abnormal threshold r=0.5 is set, r is an over-parameter, an implementer can adjust according to a specific implementation scene, and if the slope difference value is greater than the abnormal threshold 0.5, the vibration signal of the current rolling bearing is considered to be abnormal, namely the rolling bearing has a fault; and if the slope difference value is not greater than the abnormal threshold value 0.5, judging that the vibration signal of the current rolling bearing is abnormal, namely the rolling bearing is not faulty.
The invention also discloses a rolling bearing fault recognition system, as shown in fig. 2, which comprises: the device comprises a data acquisition module, a data processing module, a data extraction module and a detection and identification module. The data acquisition module is used for acquiring all vibration signals of the rolling bearing to be detected from the beginning of running to the current period, and selecting a signal segment with a set time length as a target signal segment of each vibration signal by taking the vibration signal as a center; the data processing module is used for obtaining the local information entropy of each vibration signal by utilizing the signal values of all the vibration signals in the target signal section of the vibration signal; acquiring variances of signal values of all vibration signals in a target signal section of each vibration signal, and obtaining an information entropy fluctuation range of the vibration signal by utilizing the variances and local information entropy corresponding to each vibration signal; the data extraction module is used for acquiring a vibration signal corresponding to a local information entropy in the information entropy fluctuation range of the vibration signal and marking the vibration signal as an initial target vibration signal; dividing the initial target vibration signal into a plurality of target vibration signals with different periodicity by using the time difference between any two initial target vibration signals; the detection and identification module is used for performing curve fitting on local information entropy of each target vibration signal belonging to the same periodicity to obtain a fitted curve; and clustering the slope of each point on each fitting curve to obtain multiple types of slopes, and determining whether the current rolling bearing is abnormal or not by utilizing the difference value between the average values of the slopes of each type.
In summary, the invention provides a rolling bearing fault identification method and system, which are characterized in that an information entropy fluctuation range is determined through a variance and a local information entropy, vibration signals in the information entropy fluctuation range in all vibration signals are extracted by using the information entropy fluctuation range, the local information entropy is expanded into the information entropy fluctuation range by using the variance, and the information entropy is used as a fault tolerance range of data acquisition errors, so that normal points can be reserved as far as possible when noise points are removed; screening an initial target vibration signal by utilizing an information entropy fluctuation range to obtain a vibration signal from which part of noise points are removed; the periodic vibration signals are screened out through the rotation period, so that the vibration signals without periodic regular noise can be filtered out, and the follow-up fault identification result is more accurate; after the interference of noise points is removed, curve fitting is carried out on the local information entropy of all vibration signals, and whether the rolling bearing in the current period is abnormal or not is judged by utilizing the slope, so that the automatic analysis of the fault identification of the rolling bearing is realized.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (6)
1. A rolling bearing fault identification method is characterized in that:
acquiring all vibration signals of the rolling bearing to be detected from the beginning of running to the current period;
taking each vibration signal as the center of a time window, selecting a time window with a time sequence length longer than the rotation period of the rolling bearing, and collectively combining all vibration signals in the time window of each vibration signal with a target signal segment of the vibration signal;
obtaining local information entropy of each vibration signal by using signal values of all vibration signals in a target signal section of the vibration signal;
acquiring variances of signal values of all vibration signals in a target signal section of each vibration signal, and obtaining an information entropy fluctuation range of the vibration signal by utilizing the variances and local information entropy corresponding to each vibration signal;
acquiring a vibration signal corresponding to a local information entropy in an information entropy fluctuation range of the vibration signal and marking the vibration signal as an initial target vibration signal;
dividing the initial target vibration signal into a plurality of target vibration signals with different periodicity by using the time difference between any two initial target vibration signals;
performing curve fitting on local information entropy of each target vibration signal belonging to the same periodicity to obtain a fitted curve;
clustering the slope of each point on each fitting curve to obtain multiple types of slopes, and determining whether the current rolling bearing is abnormal or not by utilizing the difference value between the average values of the slopes of each type;
the step of obtaining the information entropy fluctuation range of the vibration signals by utilizing the variance and the local information entropy corresponding to each vibration signal comprises the following steps:
respectively acquiring the mean value of the variances of all vibration signals and the mean value of the local information entropy as a target variance and a target local information entropy;
subtracting the target variance from the target local information entropy to obtain the minimum value of the information entropy fluctuation range of the vibration signal;
utilizing the target local information entropy plus the target variance as the maximum value of the information entropy fluctuation range of the vibration signal;
and taking a section formed by the maximum value and the minimum value of the information entropy fluctuation range as the information entropy fluctuation range of the vibration signal.
2. A method of identifying a rolling bearing failure in accordance with claim 1 wherein the length of the target signal segment is greater than the rolling bearing rotation period.
3. The method of claim 1, wherein the step of dividing the initial target vibration signal into a plurality of different periodic target vibration signals using a time difference between any two of the initial target vibration signals comprises:
acquiring the time difference between any two initial target vibration signals;
the vibration signals with equal time differences and rotation periods and time intervals of the rotation periods are divided into target vibration signals of the same period.
4. The method for identifying faults of rolling bearings according to claim 1, wherein the slope of each point on the fitted curve is classified by a k-means algorithm to obtain two types of slopes.
5. The method of claim 4, wherein the step of determining whether the current rolling bearing is abnormal using the difference between the slope means of each type comprises:
acquiring the average value of all slopes in each type of slope, and acquiring the difference value between the average values of the two types of slopes;
when the difference value is larger than a preset abnormal threshold value, the rolling bearing is considered to have faults;
and when the difference value is not larger than the preset abnormal threshold value, the rolling bearing is considered to have no fault.
6. A rolling bearing failure recognition system, comprising:
and a data acquisition module: the method comprises the steps of acquiring all vibration signals of a rolling bearing to be detected from the beginning of running to the current period, taking each vibration signal as the center of a time window, selecting a time window with a time sequence length longer than the rotation period of the rolling bearing, and collectively combining all vibration signals in the time window of each vibration signal with a target signal segment of the vibration signal;
and a data processing module: the method comprises the steps of obtaining local information entropy of each vibration signal by using signal values of all vibration signals in a target signal section of the vibration signal; acquiring variances of signal values of all vibration signals in a target signal section of each vibration signal, and obtaining an information entropy fluctuation range of the vibration signal by utilizing the variances and local information entropy corresponding to each vibration signal;
and a data extraction module: the vibration signal corresponding to the local information entropy in the information entropy fluctuation range of the vibration signal is recorded as an initial target vibration signal; dividing the initial target vibration signal into a plurality of target vibration signals with different periodicity by using the time difference between any two initial target vibration signals;
and a detection and identification module: the method comprises the steps of performing curve fitting on local information entropy of each target vibration signal belonging to the same periodicity to obtain a fitted curve; clustering the slope of each point on each fitting curve to obtain multiple types of slopes, and determining whether the current rolling bearing is abnormal or not by utilizing the difference value between the average values of the slopes of each type;
the step of obtaining the information entropy fluctuation range of the vibration signals by utilizing the variance and the local information entropy corresponding to each vibration signal comprises the following steps:
respectively acquiring the mean value of the variances of all vibration signals and the mean value of the local information entropy as a target variance and a target local information entropy;
subtracting the target variance from the target local information entropy to obtain the minimum value of the information entropy fluctuation range of the vibration signal;
utilizing the target local information entropy plus the target variance as the maximum value of the information entropy fluctuation range of the vibration signal;
and taking a section formed by the maximum value and the minimum value of the information entropy fluctuation range as the information entropy fluctuation range of the vibration signal.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104468728A (en) * | 2014-11-06 | 2015-03-25 | 北京邮电大学 | Service selecting method based on information entropy and variance |
| CN108645620A (en) * | 2018-07-26 | 2018-10-12 | 北京工业大学 | A kind of Fault Diagnosis of Rolling Element Bearings method based on comentropy and Multiscale Morphological |
| CN108760300A (en) * | 2018-04-19 | 2018-11-06 | 西安工业大学 | A method of intelligent fault diagnosis being carried out to it according to bearing vibration signal |
| CN114705426A (en) * | 2022-01-06 | 2022-07-05 | 杭州爱华仪器有限公司 | Early fault diagnosis method for rolling bearing |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10416126B2 (en) * | 2013-07-02 | 2019-09-17 | Computational Systems, Inc. | Machine fault prediction based on analysis of periodic information in a signal |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104468728A (en) * | 2014-11-06 | 2015-03-25 | 北京邮电大学 | Service selecting method based on information entropy and variance |
| CN108760300A (en) * | 2018-04-19 | 2018-11-06 | 西安工业大学 | A method of intelligent fault diagnosis being carried out to it according to bearing vibration signal |
| CN108645620A (en) * | 2018-07-26 | 2018-10-12 | 北京工业大学 | A kind of Fault Diagnosis of Rolling Element Bearings method based on comentropy and Multiscale Morphological |
| CN114705426A (en) * | 2022-01-06 | 2022-07-05 | 杭州爱华仪器有限公司 | Early fault diagnosis method for rolling bearing |
Non-Patent Citations (1)
| Title |
|---|
| 构造概念格的权值优化改进算法;朱文君等;《传感器与微系统》;20170420(第04期);全文 * |
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