WO2019044496A1 - Procédé et dispositif de surveillance de l'état d'un roulement à rouleaux - Google Patents

Procédé et dispositif de surveillance de l'état d'un roulement à rouleaux Download PDF

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Publication number
WO2019044496A1
WO2019044496A1 PCT/JP2018/030301 JP2018030301W WO2019044496A1 WO 2019044496 A1 WO2019044496 A1 WO 2019044496A1 JP 2018030301 W JP2018030301 W JP 2018030301W WO 2019044496 A1 WO2019044496 A1 WO 2019044496A1
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WO
WIPO (PCT)
Prior art keywords
rolling bearing
damage
vibration data
length
state
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Application number
PCT/JP2018/030301
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English (en)
Japanese (ja)
Inventor
正嗣 北井
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Ntn株式会社
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Publication of WO2019044496A1 publication Critical patent/WO2019044496A1/fr

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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

Definitions

  • the present invention relates to a method and a device for monitoring the state of a rolling bearing.
  • Patent Document 1 discloses a rolling bearing having a simple configuration efficiently and accurately by using a background noise component having no peak such as a rotational speed proportional component in a frequency spectrum.
  • a diagnostic method of a rolling bearing capable of determining the presence or absence of an abnormality is disclosed.
  • Patent Document 1 discloses a configuration for detecting damage when periodical peaks occur in the frequency spectrum of vibration data of a rolling bearing.
  • Patent Document 1 Japanese Patent Laid-Open No. 2004-347401
  • the present invention has been made to solve the problems as described above, and its object is to estimate the remaining life of a rolling bearing according to the degree of damage.
  • the rolling bearing state monitoring method includes the steps of creating a classifier by machine learning and monitoring the rolling bearing state.
  • the rolling bearing includes a rotating wheel, a stationary wheel, and a plurality of rolling elements.
  • the plurality of rolling elements are disposed between the rotating wheel and the stationary wheel, and move along the raceway surface of the stationary wheel as the rotating wheel rotates.
  • the step of creating a classifier includes the steps of calculating a feature amount set of the first to third vibration data, and machine learning of the feature amount set of the feature amount set of the first to third vibration data to create a classifier.
  • the first vibration data is vibration data of the rolling bearing in a state in which the damage of the first length is formed in the circumferential direction of the raceway surface.
  • the second vibration data is vibration data of the rolling bearing in a state in which damage of a second length smaller than the first length is formed in the circumferential direction of the raceway surface.
  • the third vibration data is vibration data of the rolling bearing when the raceway surface is normal.
  • the number of times of loading until the first length of damage is formed in the circumferential direction of the raceway surface is the first number of times of loading.
  • the number of times of loading until the second length of damage is formed in the circumferential direction of the raceway surface is the second number of times of loading less than the first number of times of loading.
  • the step of monitoring the state includes the steps of calculating a feature amount set of the fourth vibration data, calculating the first to third precisions, and calculating the remaining life of the rolling bearing.
  • the fourth vibration data is vibration data of the rolling bearing measured during monitoring.
  • the first to third relevance factors are calculated by the classifier.
  • the first to third adaptation rates are adaptation rates between the feature amount set of the fourth vibration data and the feature amount sets of the first to third oscillation data.
  • the remaining life of the rolling bearing is calculated using the first and second number of loads and the first to third fitness factors. The remaining life is the number of remaining loads of the rolling bearing until the first length of damage is formed in the circumferential direction of the raceway surface.
  • the state monitoring method of a rolling bearing it is possible to estimate the remaining life of the rolling bearing by using a classifier created by machine learning of a feature amount set of vibration data.
  • FIG. 1 It is a figure which shows collectively the sectional view of the rolling bearing monitored by the state monitoring apparatus which concerns on embodiment, and a state monitoring apparatus. It is a functional block diagram of the state monitoring apparatus of FIG. It is a figure which shows the feature-value set calculated by the control part. It is a flowchart which shows the outline
  • FIG. 1 is a view showing a cross-sectional view of a state monitoring device 1 and a rolling bearing 10 monitored by the state monitoring device 1 according to the embodiment.
  • the rolling bearing 10 includes an inner ring 12, an outer ring 14, and a plurality of rolling elements 18.
  • Rolling bearing 10 includes, for example, self-aligning roller bearings, tapered roller bearings, cylindrical roller bearings, and ball bearings.
  • the rolling bearing 10 may be a single row or multiple rows.
  • the inner ring 12 is a stationary ring externally fitted to the non-rotating shaft 11.
  • the outer ring 14 is a rotating ring provided on the outer peripheral side of the inner ring 12 and integrally rotating with a rotating body (not shown).
  • Each of the plurality of rolling elements 18 is interposed between the inner ring 12 and the outer ring 14 while being held at equal intervals to the adjacent rolling elements 18 by a cage (not shown).
  • the state monitoring device 1 monitors the state of the rolling bearing by measuring the physical quantity of the rolling bearing 10 by the vibration sensor.
  • Examples of physical quantities of the rolling bearing 10 measured by the vibration sensor include acceleration, velocity, displacement, sound, AE (Acoustic Emission), and power.
  • a load area that receives a radial load from the plurality of rolling elements 18 is formed on the upper side in the vertical direction (X-axis direction) of the outer peripheral surface (track surface) of the inner ring 12 which is a stationary wheel.
  • the radial load is maximum at the center of the load area. Therefore, damage to the raceway surface often occurs first at the center of the load area.
  • the vibration of the outer ring 14 may increase in accordance with the progress of the damage generated on the raceway surface. As a result, for example, an adverse effect such as an abnormal contact between a component supported by the rolling bearing 10 and a component adjacent thereto may occur.
  • a set of feature quantities and a plurality of machine-learned sets are machine-learned by machine-learning feature sets of vibration data of a rolling bearing in which damage having different lengths in the circumferential direction is formed on the raceway surface.
  • a classifier is calculated to calculate the matching rate with the feature quantity set of. The remaining life of the rolling bearing 10 can be estimated by using the classifier.
  • FIG. 2 is a functional block diagram of the state monitoring device 1 of FIG.
  • the state monitoring device 1 includes a vibration sensor 20, an LPF (Low Pass Filter) 31, a BPF (Band Pass Filter) 32, a HPF (High Pass Filter) 33, and a control unit 40. , Storage unit 50.
  • the vibration sensor 20 measures vibration data of the rolling bearing 10, and outputs the measured data to the control unit 40, the LPF 31, the BPF 32, and the HPF 33.
  • the LPF 31 outputs vibration data of a frequency smaller than a predetermined frequency to the control unit 40.
  • the LPF 31 outputs vibration data of a frequency of less than 1000 Hz to the control unit 40.
  • the BPF 32 outputs vibration data of a frequency within a predetermined range to the control unit 40.
  • the BPF 32 outputs vibration data of a frequency of 1000 Hz or more and 5000 Hz or less to the control unit 40.
  • the HPF 33 outputs vibration data of a frequency higher than a predetermined frequency to the control unit 40.
  • the HPF 33 outputs vibration data of a frequency higher than 5000 Hz to the control unit 40.
  • the control unit 40 machine-learnings the feature value set of the vibration data of the rolling bearing in which the damage whose circumferential length is different in the circumferential direction is formed in the learning period, and creates a classifier.
  • the classifier calculates the matching rate between a certain feature amount set and each machine-learned feature amount set.
  • Classifiers can include, for example, support vector machines, neural networks, naive Bayes, and decision trees.
  • the control unit 40 calculates a feature amount set of vibration data measured at the sampling time in the monitoring period, and calculates the remaining life of the rolling bearing.
  • the control unit 40 includes a computer such as a central processing unit (CPU).
  • the storage unit 50 stores, for example, vibration data, feature quantities calculated from the vibration data, and a classifier created by machine learning.
  • FIG. 3 is a diagram showing a feature amount set calculated by the control unit 40.
  • the feature amount set includes the feature amounts F1 to F60 shown in FIG.
  • the control unit 40 determines each of the time domain (F1 to F5), the frequency domain (F6 to F10), and the quefrance domain (F11 to F15) from the vibration data from the vibration sensor 20.
  • the effective value, the maximum value, the crest factor, the kurtosis, and the skewness are calculated as feature amounts.
  • control unit 40 determines the effective value, maximum value, crest factor, peak in each of the time domain (F16 to F20), the frequency domain (F21 to F25), and the quefrance domain (F26 to F30)
  • the degree and skewness are calculated as feature quantities.
  • the control unit 40 determines the effective value, maximum value, crest factor, and peak in each of the time domain (F31 to F35), the frequency domain (F36 to F40), and the quefrance domain (F41 to F45)
  • the degree and skewness are calculated as feature quantities.
  • the control unit 40 determines the effective value, maximum value, crest factor, peak in each of the time domain (F46 to F50), the frequency domain (F51 to F55), and the quefrance domain (F56 to F60)
  • the degree and skewness are calculated as feature quantities.
  • FIG. 4 is a flowchart showing an outline of the estimation processing of the remaining life of the rolling bearing performed by the control unit 40.
  • the step is simply described as S.
  • the control unit 40 creates a classifier at S1.
  • S1 is a process performed in the learning period.
  • the control unit 40 calculates the remaining life of the rolling bearing using a classifier at S2.
  • S2 is a process performed in the monitoring period.
  • the classifier created by machine learning may be created at the start of the monitoring period, and the learning period and the monitoring period may not be continuous.
  • the circumferential length (damage length) of the damage formed on the raceway surface is classified into three levels of damage levels 0 to 2.
  • FIG. 5 is a view schematically showing how the center of the load area is normal.
  • FIG. 6 is a view schematically showing how damage at damage level 1 occurs near the center of the load area.
  • FIG. 7 is a view schematically showing how damage at level 2 damage occurs near the center of the load area.
  • the center of the load area of the rolling bearing 10 is enlarged and shown, and the outer peripheral surface of the inner ring 12 and the inner peripheral surface of the outer ring 14 are straight for ease of illustration. It is drawn on.
  • the damage level is 0 when the vicinity of the center of the load area is normal.
  • the condition that the center of the load area is normal means that no damage occurs near the center of the load area or the length of the damage is smaller than a threshold.
  • the ratio of the damage length L1 of damage level 1 shown in FIG. 6 to the damage length L2 of damage level 2 shown in FIG. 7 is 40% or more and 60% or less.
  • a plurality of rolling elements 18 pass damage level 2 damage, two or more rolling elements 18 simultaneously receive no load from the inner ring 12 and the outer ring 14. .
  • FIG. 8 is a diagram showing the result of actually measuring the relationship between the effective value of the vibration data of the rolling bearing 10 and the number of loads.
  • the number of times of loading is 0, N1, and N2 (> N1)
  • the lengths of damage occurring in the center of the load area are the damage levels 0 to 2 Do.
  • the number of times of loading is the total number of times that the plurality of rolling elements 18 loaded a radial load to the center of the load area (passed through the center of the load area) after the rolling bearing 10 started operation.
  • the term “remaining life” refers to the number of times of loading (the number of remaining loads) from a certain measurement time during status monitoring until damage of damage level 2 is formed.
  • the remaining life NL0 of the rolling bearing 10 at the damage level 0 (the number of loads is 0) is N2.
  • the remaining life NL1 of the damage level 1 rolling bearing is N2-N1.
  • the remaining life NL2 of the damage level 2 rolling bearing is zero.
  • FIG. 9 is a flowchart specifically showing the flow of the process (S1 in FIG. 4) performed in the learning period.
  • the control unit 40 calculates a feature amount set of vibration data of a rolling bearing whose damage length is damage level 2, and advances the process to S12.
  • control unit 40 calculates a feature amount set of vibration data of the rolling bearing whose damage length is damage level 1, and advances the process to S13.
  • control unit 40 calculates a feature amount set of vibration data of a rolling bearing whose damage length is damage level 0, and advances the process to S14.
  • control unit 40 performs machine learning on the feature amount set of damage levels 0 to 2 to create a classifier, and advances the process to S15.
  • the classifier calculates the matching rate between a certain feature set and the feature set for each damage level.
  • control unit 40 associates the feature amount sets of damage levels 0 to 2 with the remaining lives NL0 to NL2 and stores them in the storage unit 50, and ends the processing.
  • S11 to S13 may be performed before S14, and need not be performed in the order shown in FIG. Also, S11 to S13 may be performed not simultaneously but concurrently.
  • FIG. 10 is a flowchart specifically showing the flow of the process (S2 in FIG. 9) performed in the monitoring period.
  • the process shown in FIG. 10 is executed at each sampling time by a main routine (not shown).
  • the control unit 40 calculates a feature amount set of vibration data (measurement data) measured at sampling time in S21, and advances the process to S22.
  • the control unit 40 uses the classifier created in the learning period to calculate the relevance ratios R0 to R2 between the feature data set of the measurement data and each of the three feature data sets that have been machine-learned. Go to
  • the matching rate R0 is a matching rate between the feature quantity set of measurement data and the feature quantity set of damage level 0.
  • the matching rate R1 is a matching rate between the feature quantity set of measurement data and the feature quantity set of damage level 1.
  • the matching rate R2 is a matching rate between the feature amount set of measurement data and the feature amount set of damage level 2.
  • the sum of the relevance factors R0 to R2 is one.
  • control unit 40 calculates the remaining life NL until the length of the damage at the sampling time becomes the length of the damage level 2 using the following equation (1), and returns the process to the main routine.
  • control unit 40 If remaining life NL is smaller than the threshold, control unit 40 notifies the user of, for example, a message prompting replacement of the rolling bearing.
  • FIG. 11 is a view showing the relationship between the number of times of loading of the rolling bearing and the remaining life estimated by the state monitoring device according to the embodiment. As shown in FIG. 11, the remaining life corresponding to the state of damage caused to the raceway surface which develops with the increase of the number of times of loading is estimated.
  • the inner ring is a stationary ring and the outer ring is a rotating ring has been described.
  • the inner ring may be a rotating ring and the outer ring may be a stationary ring.
  • the length of the damage is divided into three levels, and the feature amount set of each level is machine-learned in the learning period.
  • the length of injury may be divided into four or more levels.
  • the feature value set of vibration data may include feature values other than the effective value, maximum value, crest factor, kurtosis, and skewness, for example, Short Time Fourier Transform (STFT) data May be included.
  • STFT Short Time Fourier Transform
  • the raceway surface of the rolling bearing may be artificially damaged, and the feature amount set in the state in which the damage is formed may be machine-learned.
  • the number of loads until the damage is formed may be set by past operation results or simulation of the same type or similar rolling bearing. it can.
  • FIG. 12 shows the relationship between the damage levels 0 to 2 and the remaining life in the case where the rolling bearing is actually operated to form a damage and the case where the damage is artificially formed.
  • a broken line C20 indicates the correspondence when the rolling bearing is actually operated to form a damage
  • a broken line C21 indicates the correspondence when the damage is artificially formed.
  • the change of the remaining life with respect to the change of each damage level 0 to 2 is the same tendency at the broken lines C20 and C21. Even when artificially formed damage is used, the remaining life of the rolling bearing can be estimated with the same degree of accuracy as when using the damage formed by actually operating the rolling bearing.
  • the remaining life of the rolling bearing can be estimated by using the classifier created by machine learning of the feature amount set of the vibration data.

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  • General Physics & Mathematics (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Abstract

La présente invention permet l'estimation de la durée de vie restante d'un roulement à rouleaux. Un procédé de surveillance de l'état d'un roulement à rouleaux selon la présente invention comprend : une étape de création d'un classificateur au moyen d'un apprentissage automatique; et une étape de surveillance de l'état du roulement à rouleaux. L'étape de création du classificateur comprend : des étapes (S11-S13) de calcul d'un ensemble de caractéristiques concernant des premières à troisièmes données de vibration; et une étape (S14) de création du classificateur par apprentissage automatique des premières à troisièmes données de vibration. Les premières données de vibration sont des données de vibration concernant le roulement à rouleaux dans un état dans lequel un endommagement d'une première longueur est formé dans la direction circonférentielle d'une surface de chemin de roulement. Les deuxièmes données de vibration sont des données de vibration concernant le roulement à rouleaux dans un état dans lequel un endommagement d'une deuxième longueur plus courte que la première longueur est formé dans la direction circonférentielle de la surface de chemin de roulement. Les troisièmes données de vibration sont des données de vibration concernant le roulement à rouleaux lorsque la surface de chemin de roulement est normale. L'étape de surveillance de l'état comprend une étape d'utilisation du classificateur afin de calculer la durée de vie restante avant que la longueur des dommages atteigne la deuxième longueur.
PCT/JP2018/030301 2017-08-31 2018-08-14 Procédé et dispositif de surveillance de l'état d'un roulement à rouleaux WO2019044496A1 (fr)

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Cited By (1)

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JP2021056124A (ja) * 2019-09-30 2021-04-08 国立大学法人大阪大学 余寿命予測システム、余寿命予測装置、および余寿命予測プログラム

Families Citing this family (4)

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JP7430317B2 (ja) 2019-09-30 2024-02-13 国立大学法人大阪大学 余寿命予測システム、余寿命予測装置、および余寿命予測プログラム
JP2021056153A (ja) * 2019-10-01 2021-04-08 国立大学法人大阪大学 余寿命予測装置、余寿命予測システム、および余寿命予測プログラム
WO2021117752A1 (fr) * 2019-12-11 2021-06-17 Ntn株式会社 Procédé de surveillance d'état de palier à roulement et dispositif de surveillance d'état de palier à roulement
JP7375584B2 (ja) * 2020-01-30 2023-11-08 オムロン株式会社 シミュレーション装置、方法、プログラム、及び診断システム

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JP2003005822A (ja) * 2001-06-25 2003-01-08 Mitsubishi Chemicals Corp 設備管理システム
US20100280772A1 (en) * 2007-10-24 2010-11-04 Abb Research Ltd. Method for detection and automatic identification of damage to rolling bearings
JP2011107093A (ja) * 2009-11-20 2011-06-02 Jx Nippon Oil & Energy Corp 振動体の異常診断装置及び異常診断方法
JP2016062258A (ja) * 2014-09-17 2016-04-25 インターナショナル・ビジネス・マシーンズ・コーポレーションＩｎｔｅｒｎａｔｉｏｎａｌ Ｂｕｓｉｎｅｓｓ Ｍａｃｈｉｎｅｓ Ｃｏｒｐｏｒａｔｉｏｎ 検出装置、検出方法、およびプログラム
JP2017026020A (ja) * 2015-07-22 2017-02-02 Ntn株式会社 転がり軸受の状態監視装置及び転がり軸受の状態監視方法

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JP2003005822A (ja) * 2001-06-25 2003-01-08 Mitsubishi Chemicals Corp 設備管理システム
US20100280772A1 (en) * 2007-10-24 2010-11-04 Abb Research Ltd. Method for detection and automatic identification of damage to rolling bearings
JP2011107093A (ja) * 2009-11-20 2011-06-02 Jx Nippon Oil & Energy Corp 振動体の異常診断装置及び異常診断方法
JP2016062258A (ja) * 2014-09-17 2016-04-25 インターナショナル・ビジネス・マシーンズ・コーポレーションＩｎｔｅｒｎａｔｉｏｎａｌ Ｂｕｓｉｎｅｓｓ Ｍａｃｈｉｎｅｓ Ｃｏｒｐｏｒａｔｉｏｎ 検出装置、検出方法、およびプログラム
JP2017026020A (ja) * 2015-07-22 2017-02-02 Ntn株式会社 転がり軸受の状態監視装置及び転がり軸受の状態監視方法

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Publication number Priority date Publication date Assignee Title
JP2021056124A (ja) * 2019-09-30 2021-04-08 国立大学法人大阪大学 余寿命予測システム、余寿命予測装置、および余寿命予測プログラム
WO2021065449A1 (fr) * 2019-09-30 2021-04-08 国立大学法人大阪大学 Système de prédiction de durée de vie résiduelle, dispositif de prédiction de durée de vie résiduelle et programme de prédiction de durée de vie résiduelle
JP7290221B2 (ja) 2019-09-30 2023-06-13 国立大学法人大阪大学 余寿命予測システム、余寿命予測装置、および余寿命予測プログラム

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