CN114383847B - Rolling bearing full-life state monitoring method based on digital twinning - Google Patents

Rolling bearing full-life state monitoring method based on digital twinning Download PDF

Info

Publication number
CN114383847B
CN114383847B CN202210288242.XA CN202210288242A CN114383847B CN 114383847 B CN114383847 B CN 114383847B CN 202210288242 A CN202210288242 A CN 202210288242A CN 114383847 B CN114383847 B CN 114383847B
Authority
CN
China
Prior art keywords
bearing
fault
rolling
model
stage
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202210288242.XA
Other languages
Chinese (zh)
Other versions
CN114383847A (en
Inventor
郭亮
宗珠毓秀
高宏力
韩佳霖
李世超
张江泉
何季刚
由智超
潘江
马贵林
伍广
段志琴
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Southwest Jiaotong University
Original Assignee
Southwest Jiaotong University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Southwest Jiaotong University filed Critical Southwest Jiaotong University
Priority to CN202210288242.XA priority Critical patent/CN114383847B/en
Publication of CN114383847A publication Critical patent/CN114383847A/en
Application granted granted Critical
Publication of CN114383847B publication Critical patent/CN114383847B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

Landscapes

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

Abstract

The invention discloses a rolling bearing full-life state monitoring method based on digital twinning, which comprises the following steps of: step one, signal acquisition; step two, establishing a virtual dynamic model; step three, mapping and matching; step four, parameter prediction; step five, simulating signals; the invention generates a digital twin model with high fidelity and high precision characteristics of equivalent information of a virtual space and a physical entity, the model captures and stores the measurement information of a physical entity model of a bearing, so that the prediction of the fault size and the comprehensive equivalent stiffness of the bearing in the whole life cycle and the monitoring of the bearing state are facilitated, the physical characteristics and the degradation information of the entity model are interacted with the information of a dynamic model of the virtual bearing, the fault width change and the comprehensive equivalent stiffness change of the bearing are evaluated, the real-time update and the dynamic evolution of the production, management and operation and maintenance in the whole life cycle of the bearing are realized, and the whole life cycle digitization degree of the whole mechanical equipment is improved.

Description

Rolling bearing full-life state monitoring method based on digital twinning
Technical Field
The invention relates to the technical field of bearing health monitoring, in particular to a digital twin-based rolling bearing full-life state monitoring method.
Background
As a new technology serving for fault prediction and health management, a digital twin can realize system health management, fault diagnosis and intelligent maintenance, which is firstly proposed by the university of Michigan in America in a product life cycle management course and then is used as a key technology of intelligent manufacturing, the digital twin is widely concerned by students and enterprises at home and abroad and is developed and practiced, Shangguand proposes a satellite system fault diagnosis and health monitoring method based on the digital twin, which utilizes signal processing and data mining to obtain implicit information of a system operation state, provides a reasonable fault diagnosis algorithm and maintenance strategy on the basis of data fusion, Tao constructs a five-dimensional digital twin model commonly used for complex equipment, can effectively carry out data interaction fault diagnosis and maintenance design, and GrievesM proposes how to synchronously connect physical product data with information contained in a virtual product, the method has the advantages that a digital factory is simulated by using the information, how products are manufactured is predicted, long-term reliable operation of product equipment is ensured, Zhuang provides an intelligent production management and control framework based on digital twins, information acquisition of a physical assembly shop, construction of a data twins assembly model and provision of prediction service and management for a satellite assembly shop by the built model are achieved, Benjamin provides a comprehensive reference model and outlines application of the reference model in geometric transformation management, a concept framework is provided, industrial engineering design is enriched, Tuegel EJ utilizes the digital twins model to calculate local damage and material state evolution of an airplane, and therefore structural integrity of the airplane is guaranteed by predicting the structural life of the airplane;
the bearing is taken as a key part in mechanical equipment, the running state of a rolling bearing has important influence on the service life and the whole performance of complex machinery and mechanisms, the bearing dynamics research is usually inclined to establish a simulation model in a virtual space, the whole-service-life bearing is rarely analyzed, the interaction research of the virtual space and a physical space is lacked, Gupta researches the motion of a retainer and the performance of the bearing based on the relative position relation among all parts, on the basis of the model proposed by Gupta, Cao proposes a dynamic model of local defect vibration of a cylindrical roller bearing and researches the vibration response of a single-defect, multi-defect and composite fault bearing, Ahmadi AM overcomes the limitation that rolling elements are taken as point mass, the fault defect shape is taken as a rectangular pit in consideration of the influence of Hertz contact force and damping, the method improves the accuracy of predicting low-frequency and high-frequency events, liu breaks away from the limitation of a traditional rectangular fault defect model and researches a dynamic model considering the coupling of time-varying displacement excitation and time-varying contact stiffness excitation of lubrication traction, Kulkarni PG provides a dynamic model for predicting the vibration behavior of a bearing under the influence of local defects and simulates the influence of radial load, defect size, position and the like on a bearing time domain and a bearing frequency domain, however, the above article is only limited to the dynamic analysis of a normal bearing or a fault bearing in a virtual space, and the bidirectional connection between a real space and a virtual space in a full life cycle is not established.
Disclosure of Invention
The invention aims to provide a rolling bearing full-life state monitoring method based on digital twinning, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a rolling bearing full-life state monitoring method based on digital twinning comprises the following steps: step one, signal acquisition; step two, establishing a virtual dynamic model; step three, mapping and matching; step four, parameter prediction; step five, simulating signals;
in the first step, collecting physical environment full-life vibration acceleration data for later use through a bearing test bed in a system level;
in the second step, after the data collected in the first step, a virtual dynamic model of the rolling bearing is established by considering time-varying fault displacement excitation and lubrication;
in the third step, a segmentation searching method according to the change of the fault evolution speed is provided to search for matched fault dynamic response, and the relation between the vibration signal and the defect size and the relation between the vibration signal and the comprehensive equivalent stiffness are revealed by the vibration data in the first step and the vibration data generated by the virtual model in the second step through data connection matching in the unit level, so that a bearing digital twin model is obtained;
in the fourth step, the bearing fault width change and the comprehensive equivalent stiffness change in the degradation stage obtained in the third step are matched to obtain an evolution rule of a full life cycle through a radial basis function neural network;
in the fifth step, after the life cycle evolution rule is obtained from the fourth step, the predicted defect displacement excitation and stiffness evolution trend is input into a rolling bearing life cycle dynamic model to obtain a life cycle vibration acceleration signal, the obtained data is mapped into corresponding data of a physical space, then a bearing digital twin model is obtained according to the establishment, and the model is used for detecting the life cycle state of the bearing.
Preferably, in the second step, in order to study the dynamic characteristics of the rolling bearing, assuming that the outer ring of the bearing is a rigid body and is fixedly supported, the inner ring is fixedly connected with the rotating shaft and rotates counterclockwise, considering the slip phenomenon between the rolling body and the inner and outer rings, considering the bearing lubrication influence, neglecting the influence of the inertia force of the rolling body, considering the rolling body as nonlinear spring damping, and loading the external radial load to the bearing through the center of the rotating shaft and further loading the external radial load to the simplified spring damping system in the vertical direction.
Preferably, in the second step, as a deformation amount caused by the fault, when the bearing is in a healthy stage, the deformation amount is 0, and when the bearing is in a fault stage, a fault model is established to maximally meet the actual condition of the fault of the bearing, and the generated time-varying displacement excitation is specifically calculated as:
Figure GDA0003657812670000031
wherein theta isdIs a fault zone span angle, theta0To the fault starting angle, HdmaxTo be rolledThe maximum amount of deflection released by the body upon failure of the outer ring is as follows:
Figure GDA0003657812670000032
the rolling deformation amount is:
Figure GDA0003657812670000033
the deformation of the outer ring is as follows:
Figure GDA0003657812670000034
wherein W is the fault width, rbIs the roller radius, r0Is the outer raceway radius;
predicting the evolution defect through a rolling bearing full life cycle vibration signal and a radial basis function neural network, and obtaining the contact deformation of the ith rolling body as follows:
Ai＝xsinθi+ycosθi-(Cr+hii+hoi+ H) equation 5
Wherein x is the horizontal displacement of the bearing, y is the vertical displacement of the bearing, CrIs the radial clearance of the bearing, hiiIs the thickness of a central oil film between the ith rolling body and the inner ring, hoiThe thickness of a central oil film between the ith rolling body and the outer ring is calculated as follows:
Figure GDA0003657812670000041
wherein U is a dimensionless speed parameter, G is a dimensionless material parameter, e is a load parameter, k is a load coefficient, RixIs the equivalent radius of the rolling body and the inner ring in the x direction, RoxThe equivalent radius of the rolling body and the outer ring in the x direction is shown.
Preferably, in the fourth step, the bearing fault width and the comprehensive equivalent stiffness in the degradation stage and the maximum value of the sample signal are input into the radial basis function neural network to obtain the evolution law of the bearing fault width and the comprehensive equivalent stiffness in the whole life cycle.
Preferably, in the fifth step, the dynamic model of the full life of the rolling bearing is as follows:
Figure GDA0003657812670000042
wherein m is the equivalent mass of the bearing, x is the horizontal displacement of the bearing, y is the vertical displacement of the bearing, c is the equivalent damping of the bearing,
Figure GDA0003657812670000044
and
Figure GDA0003657812670000045
axial and radial forces to which the bearing is subjected, FHxAnd FHyThe contact force components of the rolling elements and the inner and outer rings are respectively shown, and the calculation formula is as follows:
Figure GDA0003657812670000043
wherein KtTo combine equivalent stiffness, thetaiIs the angular position of the ith rolling element, n is the load deformation index, gammaiThe parameters for determining whether the ith roller contacts the raceway are defined as:
Figure GDA0003657812670000051
preferably, in the third step, the method for establishing the bearing digital twin model comprises the following steps:
1) inputting: degraded bearing vibration sample set at N moments
Figure GDA0003657812670000052
Where Δ t denotes the separation of two adjacent samples, a set of simulated signal samples
Figure GDA0003657812670000053
The maximum expected error beta, the group number k of each stage, the fault width evolution step delta and the comprehensive equivalent stiffness evolution step tau;
2) calculating initial fault width W of fault germination stage11Initial stiffness K at fault initiation11Initial failure width W of failure degradation stage21Initial stiffness K at the fault degradation stage21Initial fault width W at near failure stage of fault31Initial stiffness K at near failure stage of failure31；
3) Inputting the grouped defect widths and the comprehensive equivalent stiffness in the fault initiation stage, the fault degradation stage and the fault near failure stage into formulas 1-9 to obtain a group of numerical values of which the error is minimum by comparing the simulation signal peak value and the experimental acceleration signal peak value, and assuming that the real fault size and the comprehensive equivalent stiffness of the bearing at the moment are the group of numerical values.
Compared with the prior art, the invention has the beneficial effects that: the invention provides a digital twin model with high fidelity, high reliability and high precision characteristics, which can generate equivalent information of a virtual space and a physical entity, the model can capture and store the measurement information of a physical entity model of a bearing, can predict the fault size and the comprehensive equivalent stiffness of a bearing in the whole life cycle, further monitor the state of the bearing, perform information interaction on the physical characteristics and the degradation information of the entity model and a dynamic model of the virtual bearing, evaluate the change of the fault width and the change of the comprehensive equivalent stiffness of the bearing, realize the real-time update and the dynamic evolution of the production, management and operation and maintenance in the whole life cycle of the bearing, improve the precision and the efficiency of the fault prediction and the health management and improve the digitization degree of the whole life cycle of the whole mechanical equipment.
Drawings
FIG. 1 is an analytical model diagram of a rolling bearing of the present invention, wherein (a) is a structural diagram and (b) is a simplified diagram of a spring damping system;
FIG. 2 is a schematic enlarged view of a partial defect of the outer ring of the present invention;
FIG. 3 is an architectural diagram of a digital twin model of the present invention;
FIG. 4 is a block diagram of a neural network of the present invention;
FIG. 5 is a flow chart of the method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1-5, an embodiment of the present invention is shown: a rolling bearing full-life state monitoring method based on digital twinning comprises the following steps: step one, signal acquisition; step two, establishing a virtual dynamic model; step three, mapping and matching; step four, parameter prediction; step five, simulating signals;
in the first step, collecting physical environment full-life vibration acceleration data for later use through a bearing test bed in a system level;
wherein in the second step, after the data collected in the first step, a virtual dynamic model of the rolling bearing is established by considering time-varying fault displacement excitation and lubrication, in order to research the dynamic characteristics of the rolling bearing, an outer ring of the bearing is assumed to be a rigid body and fixedly supported, an inner ring is fixedly connected with a rotating shaft and rotates anticlockwise, the phenomenon of slipping between the rolling body and the inner ring and the outer ring are considered, the lubricating effect of the bearing is considered, the influence of the inertia force of the rolling body is ignored, the rolling body is considered to be nonlinear spring damping, an external radial load is loaded on the bearing through the center of the rotating shaft and further loaded to a simplified spring damping system in the vertical direction to serve as the deformation caused by faults, when the bearing is in a healthy stage, the deformation is 0, when the bearing is in a fault stage, a fault model is established to be maximally consistent with the actual condition of the bearing fault, and the generated time-varying displacement excitation is specifically calculated as follows:
Figure GDA0003657812670000071
wherein theta isdIs a fault zone span angle, theta0For the starting angle of the fault, HdmaxThe maximum deformation of the rolling elements released by the outer ring failure is as follows:
Figure GDA0003657812670000072
the rolling deformation amount is:
Figure GDA0003657812670000073
the deformation of the outer ring is as follows:
Figure GDA0003657812670000074
wherein W is the fault width, rbIs the roller radius, r0Is the radius of the outer raceway;
predicting the evolution defect through a rolling bearing full life cycle vibration signal and a radial basis function neural network, and obtaining the contact deformation of the ith rolling body as follows:
Ai＝xsinθi+ycosθi-(Cr+hii+hoi+ H) equation 5
Wherein x is the horizontal displacement of the bearing, y is the vertical displacement of the bearing, CrIs the radial clearance of the bearing, hiiIs the thickness of a central oil film between the ith rolling body and the inner ring, hoiThe thickness of a central oil film between the ith rolling body and the outer ring is calculated as follows:
Figure GDA0003657812670000075
wherein U is a dimensionless speed parameter, G is a dimensionless material parameter, e is a load parameter, k is a load coefficient, RixIs the equivalent radius of the rolling body and the inner ring in the x direction, RoxThe equivalent radius of the rolling body and the outer ring in the x direction;
in the third step, a segmented searching method according to the change of the fault evolution speed is provided to find out the matched fault dynamic response, the vibration data in the first step and the vibration data generated by the virtual model in the second step are connected and matched in a unit layer through data to reveal the relation between the vibration signal and the defect size and the relation between the vibration signal and the comprehensive equivalent stiffness, so as to obtain a bearing digital twin model, and the establishment method of the bearing digital twin model comprises the following steps:
1) inputting: degraded bearing vibration sample set at N moments
Figure GDA0003657812670000081
Where Δ t denotes the separation of two adjacent samples, a set of simulated signal samples
Figure GDA0003657812670000082
The maximum expected error beta, the group number k of each stage, the fault width evolution step delta and the comprehensive equivalent stiffness evolution step tau;
2) calculating initial fault width W of fault germination stage11Initial stiffness K at fault initiation11Initial failure width W of failure degradation stage21Initial stiffness K at the fault degradation stage21Initial fault width W at near failure stage of the fault31Initial stiffness K at near failure stage of failure31；
3) Inputting the grouped defect widths and the comprehensive equivalent stiffness in the fault initiation stage, the fault degradation stage and the fault near failure stage into formulas 1-9 to obtain a group of numerical values with the minimum error between the simulation signal peak value and the experimental acceleration signal peak value, and assuming that the real fault size and the comprehensive equivalent stiffness of the bearing at the moment are the group of numerical values;
in the fourth step, the bearing fault width change and the comprehensive equivalent stiffness change in the degradation stage obtained by matching in the third step are used for obtaining an evolution rule of the full life cycle through the radial basis function neural network, and the bearing fault width, the comprehensive equivalent stiffness and the maximum value of the sample signal in the degradation stage are input into the radial basis function neural network to obtain the bearing fault width and the comprehensive equivalent stiffness evolution rule under the full life cycle;
in the fifth step, after obtaining the life cycle evolution rule from the fourth step, inputting the predicted defect displacement excitation and stiffness evolution trend into a rolling bearing life cycle dynamic model to obtain a life cycle vibration acceleration signal, mapping the obtained data into corresponding data of a physical space, then obtaining a bearing digital twin model according to establishment, detecting the life cycle state of the bearing by using the model, wherein the life cycle evolution rule of the rolling bearing is as follows:
Figure GDA0003657812670000083
wherein m is the equivalent mass of the bearing, x is the horizontal displacement of the bearing, y is the vertical displacement of the bearing, c is the equivalent damping of the bearing,
Figure GDA0003657812670000093
and
Figure GDA0003657812670000094
axial and radial forces to which the bearing is subjected, FHxAnd FHyThe contact force components of the rolling elements and the inner and outer rings are respectively calculated according to the following formula:
Figure GDA0003657812670000091
wherein KtTo combine equivalent stiffness, thetaiIs the angular position of the ith rolling element, n is the load deformation index, gammaiThe parameters for determining whether the ith roller contacts the raceway are defined as:
Figure GDA0003657812670000092
based on the above, the invention has the advantages that the invention provides a digital twin model with high fidelity, high reliability and high precision characteristics, which can generate equivalent information of a virtual space and a physical entity, the model can capture and store the measurement information of the physical entity model of the bearing, can predict the fault size and the comprehensive equivalent stiffness of the bearing in the whole life cycle, further monitor the state of the bearing, perform information interaction on the physical characteristics and the degradation information of the entity model and the dynamic model of the virtual bearing, evaluate the fault width change and the comprehensive equivalent stiffness change of the bearing, realize the real-time update and the dynamic evolution of the production, management and operation and maintenance in the whole life cycle of the bearing, improve the precision and the efficiency of fault prediction and health management, and improve the digitization degree of the whole life cycle of the whole mechanical equipment.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.

Claims (6)

1. A rolling bearing full-life state monitoring method based on digital twinning comprises the following steps: step one, signal acquisition; step two, establishing a virtual dynamic model; step three, mapping and matching; step four, parameter prediction; step five, simulating signals; the method is characterized in that:
in the first step, collecting physical environment full-life vibration acceleration data for later use through a bearing test bed in a system level;
in the second step, after the data collected in the first step, a virtual dynamic model of the rolling bearing is established by considering time-varying fault displacement excitation and lubrication;
in the third step, a segmentation searching method according to the change of the fault evolution speed is provided to find out the matched fault dynamic response, and the vibration data generated by the vibration data in the first step and the vibration data generated by the virtual model in the second step are matched in a unit layer through data connection to reveal the relation between the vibration signal and the defect size and the relation between the vibration signal and the comprehensive equivalent stiffness, so as to obtain a bearing digital twin model;
in the fourth step, the bearing fault width change and the comprehensive equivalent stiffness change in the degradation stage obtained in the third step are matched to obtain an evolution rule of a full life cycle through a neural network;
in the fifth step, after the life cycle evolution rule is obtained from the fourth step, the predicted defect displacement excitation and stiffness evolution trend is input into a rolling bearing life cycle dynamic model to obtain a life cycle vibration acceleration signal, the obtained data is mapped into corresponding data of a physical space, then a bearing digital twin model is obtained according to the establishment, and the model is used for detecting the life cycle state of the bearing.
2. The method for monitoring the full-life state of the rolling bearing based on the digital twin as claimed in claim 1, characterized in that: in the second step, for researching the dynamic characteristics of the rolling bearing, assuming that the outer ring of the bearing is a rigid body and is fixedly supported, the inner ring is fixedly connected with the rotating shaft and rotates anticlockwise, considering the slip phenomenon between the rolling body and the inner ring and the outer ring, considering the lubricating effect of the bearing, neglecting the influence of the inertia force of the rolling body, considering the rolling body as nonlinear spring damping, loading the external radial load to the bearing through the center of the rotating shaft, and further loading the external radial load to the simplified spring damping system in the vertical direction.
3. The method for monitoring the full-life state of the rolling bearing based on the digital twin as claimed in claim 1, wherein the method comprises the following steps: in the second step, as a deformation caused by the fault, when the bearing is in a healthy stage, the deformation is 0, when the bearing is in a fault stage, a fault model is established to maximally accord with the actual condition of the fault of the bearing, and the generated time-varying displacement excitation is specifically calculated as follows:
Figure FDA0003676267530000021
wherein theta isdIs a fault zone span angle, theta0For the starting angle of the fault, HdmaxMaximum deflection, θ, of the rolling bodies released by failure of the outer ringiThe angular position of the ith rolling element is as follows:
Figure FDA0003676267530000022
the rolling deformation amount is:
Figure FDA0003676267530000023
the deformation of the outer ring is as follows:
Figure FDA0003676267530000024
where W is the fault width, rbIs the roller radius, r0Is the radius of the outer raceway;
predicting the evolution defect through a rolling bearing full life cycle vibration signal and a radial basis function neural network, and obtaining the contact deformation of the ith rolling body as follows:
Ai＝xsinθi+ycosθi-(Cr+hii+hoi+ H) equation 5
Wherein x is the horizontal displacement of the bearing, y is the vertical displacement of the bearing, CrRadial clearance of the bearing, hiiIs the thickness of a central oil film between the ith rolling element and the inner ring, h0iThe thickness of a central oil film between the ith rolling body and the outer ring is calculated as follows:
Figure FDA0003676267530000025
wherein U is a dimensionless speed parameter and G is a dimensionless speed parameterClass material parameter, e is load parameter, k is load coefficient, RixIs the equivalent radius of the rolling body and the inner ring in the x direction, RoxThe equivalent radius of the rolling body and the outer ring in the x direction is shown.
4. The method for monitoring the full-life state of the rolling bearing based on the digital twin as claimed in claim 1, wherein the method comprises the following steps: and in the fourth step, the bearing fault width and the comprehensive equivalent stiffness in the degradation stage and the maximum value of the sample signal are input into the radial basis function neural network to obtain the evolution law of the bearing fault width and the comprehensive equivalent stiffness in the whole life cycle.
5. The method for monitoring the full-life state of the rolling bearing based on the digital twin as claimed in claim 3, wherein the method comprises the following steps: in the fifth step, the dynamic model of the whole service life of the rolling bearing is as follows:
Figure FDA0003676267530000031
wherein m is the equivalent mass of the bearing, x is the horizontal displacement of the bearing, y is the vertical displacement of the bearing, c is the equivalent damping of the bearing, QxAnd QyAxial and radial forces to which the bearing is subjected, FHxAnd FHyThe contact force components of the rolling elements and the inner and outer rings are respectively shown, and the calculation formula is as follows:
Figure FDA0003676267530000032
wherein KtTo combine equivalent stiffness, thetaiIs the angular position of the ith rolling element, n is the load deformation index, gammaiThe parameters for determining whether the ith roller contacts the raceway are defined as:
Figure FDA0003676267530000033
6. the method for monitoring the full-life state of the rolling bearing based on the digital twin as claimed in claim 5, wherein the method comprises the following steps: in the third step, the establishment method of the bearing digital twin model comprises the following steps:
1) inputting: degraded bearing vibration sample set at N moments
Figure FDA0003676267530000034
t ═ Δ t × {0,1.. N-1}, where Δ t denotes the separation of two adjacent samples, the set of simulated signal samples
Figure FDA0003676267530000035
The maximum expected error beta, the group number k of each stage, the fault width evolution step delta and the comprehensive equivalent stiffness evolution step tau;
2) calculating initial fault width W of fault germination stage11Initial stiffness K at fault initiation11Initial failure width W at failure degradation stage21Initial stiffness K at the fault degradation stage21Initial fault width W at near failure stage of fault31Initial stiffness K at near failure stage of failure31；
3) Inputting the grouped defect widths and the comprehensive equivalent stiffness in the fault initiation stage, the fault degradation stage and the fault near failure stage into formulas 1-9 to obtain a group of numerical values of which the error is minimum by comparing the simulation signal peak value and the experimental acceleration signal peak value, and assuming that the real fault size and the comprehensive equivalent stiffness of the bearing at the moment are the group of numerical values.
CN202210288242.XA 2022-03-23 2022-03-23 Rolling bearing full-life state monitoring method based on digital twinning Active CN114383847B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210288242.XA CN114383847B (en) 2022-03-23 2022-03-23 Rolling bearing full-life state monitoring method based on digital twinning

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210288242.XA CN114383847B (en) 2022-03-23 2022-03-23 Rolling bearing full-life state monitoring method based on digital twinning

Publications (2)

Publication Number Publication Date
CN114383847A CN114383847A (en) 2022-04-22
CN114383847B true CN114383847B (en) 2022-07-12

Family

ID=81205885

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210288242.XA Active CN114383847B (en) 2022-03-23 2022-03-23 Rolling bearing full-life state monitoring method based on digital twinning

Country Status (1)

Country Link
CN (1) CN114383847B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118246293A (en) * 2024-05-20 2024-06-25 海信家电集团股份有限公司 Pipeline life cycle optimizing equipment and method

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110823575A (en) * 2019-11-09 2020-02-21 北京工业大学 Bearing life prediction method based on performance degradation dictionary structure and similarity
CN112487584A (en) * 2020-12-03 2021-03-12 天津工业大学 Dynamics-based rolling bearing digital twin modeling method
CN112762100A (en) * 2021-01-14 2021-05-07 哈尔滨理工大学 Bearing full-life-cycle monitoring method based on digital twinning
CN113465920A (en) * 2021-06-08 2021-10-01 西安交通大学 Cloud, fog and edge end cooperative bearing state monitoring and management method and system
CN113567132A (en) * 2021-09-01 2021-10-29 郑州轻工业大学 Motor rolling bearing fault model construction method based on digital twinning technology

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021108680A1 (en) * 2019-11-25 2021-06-03 Strong Force Iot Portfolio 2016, Llc Intelligent vibration digital twin systems and methods for industrial environments
CN106126850A (en) * 2016-07-04 2016-11-16 湖南科技大学 A kind of rolling bearing surface damage faulty power modeling method
CN110530973B (en) * 2019-07-31 2020-08-18 西安交通大学 Crack detection and diagnosis method for aircraft engine turbine disk driven by digital twin
CN111307455B (en) * 2020-03-06 2022-03-01 西南交通大学 Train bogie bearing fault monitoring method and system based on dictionary learning
CN113092115B (en) * 2021-04-09 2022-10-11 重庆大学 Digital twin model construction method of digital-analog combined drive full-life rolling bearing
CN113221280B (en) * 2021-05-14 2022-12-09 西安交通大学 Rolling bearing modeling and model updating method and system based on digital twinning

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110823575A (en) * 2019-11-09 2020-02-21 北京工业大学 Bearing life prediction method based on performance degradation dictionary structure and similarity
CN112487584A (en) * 2020-12-03 2021-03-12 天津工业大学 Dynamics-based rolling bearing digital twin modeling method
CN112762100A (en) * 2021-01-14 2021-05-07 哈尔滨理工大学 Bearing full-life-cycle monitoring method based on digital twinning
CN113465920A (en) * 2021-06-08 2021-10-01 西安交通大学 Cloud, fog and edge end cooperative bearing state monitoring and management method and system
CN113567132A (en) * 2021-09-01 2021-10-29 郑州轻工业大学 Motor rolling bearing fault model construction method based on digital twinning technology

Also Published As

Publication number Publication date
CN114383847A (en) 2022-04-22

Similar Documents

Publication Publication Date Title
CN113567132B (en) Motor rolling bearing fault model construction method based on digital twinning technology
CN113092115B (en) Digital twin model construction method of digital-analog combined drive full-life rolling bearing
CN112487584A (en) Dynamics-based rolling bearing digital twin modeling method
CN109027017B (en) method for evaluating wear state of space rolling bearing
Li et al. Early performance degradation of ceramic bearings by a twin-driven model
CN103292995B (en) Rolling bearing detection and fault diagnosis device and method
CN114383847B (en) Rolling bearing full-life state monitoring method based on digital twinning
CN116793682A (en) Bearing fault diagnosis method based on iCORAL-MMD and anti-migration learning
CN103617364B (en) Large slewing bearing residual service life prediction method based on small samples
CN110232249A (en) A kind of rolling bearing method for predicting residual useful life
CN115563853A (en) Rolling bearing fault diagnosis method based on digital twinning
Wang et al. Development and stability analysis of a high‐speed train bearing system under variable speed conditions
Deng et al. Physics-informed lightweight temporal convolution networks for fault prognostics associated to bearing stiffness degradation
Wang et al. Digital twin-driven fault diagnosis service of rotating machinery
CN116786604A (en) Strip mill monitoring method based on digital twin model
CN116561904A (en) Rolling bearing dynamics modeling and vibration characteristic analysis method
CN116127583A (en) Inverse unit load method for reconstructing bending stiffness of bridge structure
CN110596242A (en) Bridge crane girder local damage positioning method
Bai et al. Research on steel structure damage detection based on TCD-CNN method
Zhou et al. Digital Twin-Driven Bearing Defect Model and Life Cycle Prediction Research
Shi et al. A Model-Data Combination Driven Digital Twin Model for Few Samples Fault Diagnosis of Rolling Bearings
Herrmann et al. A Living Lab for Structural Health Monitoring at the Nibelungen Bridge Worms for Transfer Learning of Structural Dynamics in
von Elling et al. Model-Based Spindle Bearing Monitoring Using Vibration Sensors and Artificial Neural Networks
Wang et al. A review of current condition monitoring and fault diagnosis methods for slewing bearings
Ding et al. A linear regression model for the identification of unbalance changes in rotating machines

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CB03 Change of inventor or designer information
CB03 Change of inventor or designer information

Inventor after: Han Jialin

Inventor after: Ma Guilin

Inventor after: Wu Guang

Inventor after: Duan Zhiqin

Inventor after: Zong Zhuyuxiu

Inventor after: Gao Hongli

Inventor after: Guo Liang

Inventor after: Li Shichao

Inventor after: Zhang Jiangquan

Inventor after: He Jigang

Inventor after: You Zhichao

Inventor after: Pan Jiang

Inventor before: Guo Liang

Inventor before: Ma Guilin

Inventor before: Wu Guang

Inventor before: Duan Zhiqin

Inventor before: Zong Zhuyuxiu

Inventor before: Gao Hongli

Inventor before: Han Jialin

Inventor before: Li Shichao

Inventor before: Zhang Jiangquan

Inventor before: He Jigang

Inventor before: You Zhichao

Inventor before: Pan Jiang