CN113567132A - Motor rolling bearing fault model construction method based on digital twinning technology - Google Patents
Motor rolling bearing fault model construction method based on digital twinning technology Download PDFInfo
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Abstract
The invention discloses a motor rolling bearing three-dimensional simulation fault model construction method based on a digital twinning technology, which comprises the steps of firstly, aiming at the influence of geometric structure parameters such as bearing radial clearance on vibration data in the construction process of a three-dimensional simulation model, proposing that the geometric structure parameters of the three-dimensional simulation model continuously approach the parameter values of an actual physical model by adopting a K-center-point-based parameter optimization method, and realizing the effective matching of the geometric structure parameters of a virtual simulation model and the actual physical model parameters; secondly, the construction of a virtual space test system model and the deviation correction of vibration data are realized by constructing a virtual test system vibration model; and finally, monitoring and diagnosing the state of the motor rolling bearing by utilizing the deep neural network. The fault simulation model provided by the invention can enable the vibration characteristics of the three-dimensional simulation model to approach the vibration characteristics of the actual physical model, and provides effective data support for dynamic fault deduction and performance prediction of the rolling bearing.
Description
Technical Field
The invention relates to the technical field of mechanical fault diagnosis, in particular to a motor rolling bearing fault model building method based on a digital twinning technology.
Background
The rapid development of digitalization and intellectualization of modern manufacturing industry leads to the promotion of intelligent operation and maintenance and health management technology of mechanical equipment. The intelligent diagnosis technology of deep learning provides a technical basis for fault diagnosis of a complex mechanical system, but the defects and the shortage of fault samples in engineering practice limit effective popularization of deep learning in practical application. Although the theoretical fault model constructed by the data mining technology and the deep neural network can provide a sufficient data sample set, the fault model is not beneficial to the dynamic deduction and reasoning of mechanical parts, and the explanation of a mechanical fault mechanism is lacked. Therefore, the three-dimensional fault simulation model based on the digital twin technology is established, not only can the dynamic deduction of the mechanical fault be realized, but also the data sample of the deep learning technology in the field of mechanical fault diagnosis can be guaranteed.
How to realize the consistency of the virtual space rolling bearing fault model vibration data and the physical space actual measurement vibration data is a technical problem to be solved in the technical field. The following two problems need to be solved: the method is characterized in that the accuracy of geometric structure parameters of the bearing in the construction process of a reference model is first. The geometric structure parameter values of the bearing can be obtained by referring a manual and a drawing, but some parameters such as the theoretical value of the radial clearance of the bearing are range values, the structural parameters of actual equipment can be influenced by installation, load, operation, detection modes and the like to generate radial clearance errors, the geometric structure parameters have different values in the construction process of the three-dimensional model, and the physical action relations such as contact force and the like are different to influence vibration; the second is the consistency of the vibration data of the digital twin model. When the fault diagnosis model based on the digital twin is matched with the physical actual model based on the sensing data, if the influence of the sensor serving as a detection device on the vibration data is not considered, the processing process of the detection devices such as the vibration sensor on the vibration data of the physical model is ignored, the deviation of the compared data is easy to cause larger, the running speed of the digital twin model is reduced, and the storage pressure is increased.
Disclosure of Invention
Aiming at the problems, the invention discloses a motor rolling bearing fault model construction method based on a digital twinning technology. The invention aims to provide a motor rolling bearing fault model construction method based on a digital twinning technology, and a relatively accurate calculation result is obtained from a simulation model. The invention mainly adopts a method for constructing the fault model of the motor rolling bearing by combining geometric structure parameter optimization and vibration model construction so as to obtain a more accurate simulation calculation result. In order to achieve the purpose, the invention provides a motor rolling bearing fault model building method based on a digital twin technology, which comprises the following steps:
s1: acquisition and pretreatment of vibration data of actual physical model of motor rolling bearing
S1-1: monitoring actual vibration signals and working condition parameters in the actual operation process of a motor bearing and acquiring vibration data z (t) of an actual physical model;
s1-2: carrying out denoising pretreatment on the actual vibration signal;
s1-3: calculating the vibration characteristic value of the actual physical model as fR(c0) Wherein c is0The bearing radial play is an actual physical model;
s2: establishing a three-dimensional simulation model of a motor rolling bearing, wherein the modeling method comprises the following steps:
s2-1: obtaining the geometric dimensions and structural characteristics of different component parts of the motor bearing by referring to drawings and manuals;
s2-2: obtaining parameters of parts of different components of the bearing by looking up a nameplate and a manual;
s2-3: setting physical action relations of different component parts of the bearing respectively;
s2-4: selecting bearing geometric structure parameter variable value cj；
S2-5: establishing a three-dimensional simulation model of a motor rolling bearing in a fault-free mode;
s2-6: calculating a simulation vibration signal of the rolling bearing and a bearing vibration characteristic value thereof through the three-dimensional simulation model, and recording the vibration characteristic value of the three-dimensional simulation model of the bearing as fV(cj)；
S3: optimization and matching of geometric structure parameter variables of bearing simulation model based on K-center point
S3-1: setting a geometric structure parameter variable set O of the simulated three-dimensional model as { c ═ c1，…，ckIn which c is1Is a lower limit value of a parameter variable, ckThe upper limit value of a parameter variable;
s3-2: selection cjIs the current center point of the object O;
s3-3: calculating the variable values c of different geometric structure parameters in the bearing geometric structure parameter variable set OjCorresponding vibration characteristic value fV(cj) F of vibration characteristic value from actual physical modelR(c0) Square of distance
S3-4: constructing an objective function
S3-5: when the geometric structure parameters of the bearing continuously approach the parameter values of the actual physical model, the iteration is continuously increased to ensure that the objective function mu is increasednThe value tends to be stable;
s3-6: when in use
When the condition is met, determining the geometric structure parameter value of the bearing; otherwise, continuing the search until a condition is satisfied, wherein ε is a given sufficiently small amount;
s4: determining the three-dimensional simulation reference model of the rolling bearing in the failure-free mode according to the step S2 and the step S3
S5: three-dimensional fault simulation reference model for constructing motor rolling bearing
S5-1: acquiring fault forms of different fault types, fault positions, fault degrees and the like of the actual physical model of the motor rolling bearing in the step S1 in a dismounting and detecting mode;
s5-2: constructing a rolling bearing three-dimensional fault simulation model according to the characteristic parameters and the actual physical fault model obtained in the steps S2 and S3;
s5-3: simulating and calculating vibration signals x (t) of different faults of the rolling bearing;
s6: the actual physical test system is considered as an undistorted test system as a whole, and a virtual test system vibration model is constructed to approach a vibration signal obtained by the test device in an actual physical space, so that the vibration deviation generated by the detection device is reduced
S6-1: constructing a vibration model y (t) ═ Ax (t-t)0) Wherein the gain A is the amplitude ratio of the vibration data z (t) of the actual physical model in step S1 to the vibration signal x (t) calculated by the simulation fault model in step S5, and the lag time t0The time difference between the vibration data z (t) of the actual physical model in S1 and the vibration signal x (t) calculated by the simulation fault model in S5;
s6-2: constructing a virtual vibration data space Y (t) of a motor rolling bearing under multiple working conditions and multiple faults;
s7: state monitoring and diagnosis of motor rolling bearing by using deep neural network
S7-1: vibration data of the motor rolling bearing under different working conditions and faults are obtained through S1, and a vibration data space Z (t) of an actual physical model is constructed;
s7-2: randomly selecting sample data of 25% of the vibration data space of the actual physical model for training, testing the residual sample data, determining a deep neural network diagnosis model, and determining a threshold value according to the average diagnosis accuracy rate and the standard deviation thereof;
s7-3: diagnosing the virtual space simulation vibration data Y (t) obtained by calculation in the step S6 by adopting a deep neural network diagnosis model in the step S7-2;
s7-4: judging whether the diagnosis accuracy rate reaches a threshold value, if not, returning to the adjustment and correction of the geometric structure parameters of the three-dimensional simulation fault model of the rolling bearing by adopting a parameter optimization method based on a K-central point to obtain a fault simulation model of the motor rolling bearing with higher fidelity;
s7-5: if the threshold value is reached, establishing a simulation fault database of the motor rolling bearing digital twin fault model, and providing data support for quantitative diagnosis and performance prediction of the motor rolling bearing.
Further, in step S1-2, the preprocessing method is FIR filter or wavelet de-noising.
Further, in step S2-1, the geometric dimension and the structural characteristic include an outer ring diameter, an inner ring diameter, a width, a rolling element diameter, the number of rolling elements, a bearing pitch diameter, a contact angle, an OR curvature, an IR curvature, an outer raceway radius, and an inner raceway radius; the parameters of the part include material, density, Poisson's ratio, Young's modulus.
Further, in step S2-3, the physical action relationship includes a contact method and a friction property of the rolling element and the outer ring, the rolling element and the inner ring, the rolling element and the cage, and the like.
Further, in step S2-4, the bearing geometry parameter variables include a value of a play variable, a waviness, and a bearing geometry parameter variable such as a clearance between the rolling element and the cage hole.
Further, in step S2-4, the bearing geometry parameter variable is the value c of the bearing radial play variablej，j＝1，…， k。
Compared with the prior art, the invention has the following effects:
the invention aims to provide a method for constructing a three-dimensional simulation fault model of a motor rolling bearing based on a digital twinning technology, which solves the problem of deviation of vibration data caused by geometric structure parameters and a detection system in a bearing simulation model. According to the K-center-point-based parameter optimization method, the fidelity of the digital twin model of the rolling bearing is improved through the matching of the geometric structure parameters of the virtual simulation model and the parameters of the actual physical model, and the influence of the geometric structure parameters on the vibration data of the bearing is further reduced; the vibration model construction technology provided by the invention can realize the construction of a virtual space test system model and the deviation correction of vibration data.
Drawings
To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the figures needed to be used in the description and implementation of the prior art.
FIG. 1 is a flow chart of a motor rolling bearing fault model construction method based on a digital twinning technology.
FIG. 2 is a schematic diagram of a parameter optimization method according to the present invention.
FIG. 3 is a schematic diagram of the vibration model construction of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the present invention. It is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by those skilled in the art without any inventive work are within the scope of the present invention.
A motor rolling bearing fault model building method based on a digital twinning technology comprises the following steps:
s1: and acquiring and preprocessing vibration data of the actual physical model of the motor rolling bearing.
S1-1: and monitoring an actual vibration signal and working condition parameters in the actual operation process of the motor bearing and acquiring vibration data z (t). Because the physical model is interfered by surrounding environment, a detection device and the like in the actual operation process, the obtained vibration signal contains a large amount of noise signals, the actual measurement vibration signal needs to be subjected to denoising pretreatment, and the pretreatment method comprises an FIR filter, wavelet denoising and the like, but is not limited to the methods;
s1-2: assuming that the bearing radial clearance of the actual physical model is c0Calculating the radial clearance c by actually measuring the vibration signal0Recording the vibration characteristic value of the actual physical model as f according to the vibration characteristic value of the bearingR(c0)。
S2: a three-dimensional simulation model of the rolling bearing is constructed, and the modeling method comprises the following steps:
s2-1: the geometric dimensions and structural characteristics of different component parts of the motor bearing at least comprise the diameter of an outer ring, the diameter of an inner ring, the width, the diameter of a rolling body, the number of the rolling bodies, the pitch diameter of the bearing, a contact angle, OR curvature, IR curvature, the radius of an outer raceway, the radius of an inner raceway and other parameters are consulted and obtained through a drawing and a manual;
s2-2: different components of the bearing at least comprise parameters such as material, density, Poisson ratio, Young modulus and the like, and are consulted and obtained through a nameplate and a manual;
s2-3: the physical action relations of different components of the bearing at least comprise contact methods and friction properties of a rolling body and an outer ring, the rolling body and an inner ring, the rolling body and a retainer and the like which are respectively arranged.
S2-4: according to the manual and the drawing, the theoretical value of the radial play of the bearing is a range value, and the radial play of the actual physical model is influenced by installation, load, operation and the like to generate errors. In order to enable the vibration characteristics of the three-dimensional simulation model to approach the vibration characteristics of the actual physical model, the radial clearance of the bearing is required to be close to the radial clearance value of the actual physical model when the simulation model is built, so that the digital twin effect is achieved, and the optimization and matching of the radial clearance of the bearing are realized by adopting a K-center point-based method in the implementation case. Selecting the radial clearance variable value c of the bearingj，j＝1，…，k。
S2-5: establishing a three-dimensional simulation model of the motor rolling bearing in a fault-free mode,
s2-6: calculating the simulation vibration signal of the rolling bearing and the vibration characteristic value of the rolling bearing through a three-dimensional simulation model, and recording the variable value of the radial clearance of the bearing as cjWhen it is in the range of f, its vibration characteristic value isV(cj)。
S3: and optimizing and matching the geometric structure parameter variable of the bearing simulation model based on the K-center point.
S3-1: setting a bearing radial clearance variable set O of a simulation three-dimensional model as { c ═ c1，…，ckWithin a range of valuesUpper and lower limit values of radial play of the bearing model in the manual, c1A lower limit value of the radial play of the bearing, ckThe upper limit value of the radial play of the bearing;
s3-2: selection cjIs the current center point of the object O;
s3-3: calculating different radial clearance values c in the bearing geometric structure parameter radial clearance variable set OjCorresponding vibration characteristic value fV(cj) F from measured vibration characteristic valueR(c0) Square of distance
S3-4: constructing an objective function
S3-5: when bearing geometry parameters such as radial play cjRadial clearance c continuously approaching to actual physical model0While, the target function mu is continuously increased in an iterative mannernThe value tends to be stable;
s3-6: when in use
When the condition is satisfied, the radial clearance value c of the bearingjDetermining; otherwise, the search continues until a condition is satisfied, where ε is given a sufficiently small amount.
S4: and establishing a three-dimensional simulation reference model of the rolling bearing in a fault-free mode.
S5: and acquiring fault types, fault positions, fault degrees and other fault forms of different component parts of the bearing in a mode of disassembling and checking the actual physical model to construct a three-dimensional simulation fault model of the rolling bearing. And simulating and calculating vibration signals of different faults of the rolling bearing.
S6: considering the deviation of the sensor as a detection device to the amplitude and the phase of the vibration data of the actual physical model, the implementation case constructs a vibration model by simulating the dynamic characteristics of the detection device, so that the vibration data generated by the simulation model approaches the vibration data output by the detection device, and the vibration deviation generated by the detection device is further reduced; meanwhile, the construction of the vibration model can simplify a digital twin model, so that the running speed is improved, and the storage pressure is reduced.
And (3) taking the whole actual physical test system into consideration as a non-distortion test system, and constructing a vibration model of the virtual test system.
S6-1: constructing a vibration model y (t) ═ Ax (t-t)0) Wherein the gain A is the amplitude ratio of the sensor data z (t) of the actual physical fault model in the step (1) to the vibration data x (t) calculated by the simulation fault model in the step (5), and the lag time t0The time difference between the sensor data z (t) of the actual physical fault model in the step (1) and the vibration data x (t) calculated by the simulation fault model in the step (5);
s6-2: and constructing virtual vibration data space Y (t) of the motor rolling bearing under different working conditions and different faults.
S7: and monitoring and diagnosing the state of the motor rolling bearing by using the deep neural network.
S7-1: acquiring vibration data of a motor rolling bearing under different working conditions and faults through the step (1), and constructing a vibration data space Z (t) of an actual physical model;
s7-2: randomly selecting sample data of 25% of the vibration data space of the actual physical model for training, testing the residual sample data, determining a deep neural network diagnosis model, and determining a threshold value according to the average diagnosis accuracy rate and the standard deviation thereof;
s7-3: diagnosing the virtual space simulation vibration data Y (t) obtained by calculation in the step (6) by adopting the deep neural network diagnosis model in the step 2);
s7-4: judging whether the diagnosis accuracy rate reaches a threshold value, if not, returning to the adjustment and correction of other geometric structure parameter variables of the three-dimensional simulation fault model of the rolling bearing by adopting a parameter optimization method based on a K-central point to obtain a fault simulation model of the motor rolling bearing with higher fidelity;
s7-5: if the threshold value is reached, establishing a simulation fault database of the motor rolling bearing digital twin fault model, and providing data support for quantitative diagnosis and performance prediction of the motor rolling bearing.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the equivalent replacement or change according to the technical solution and the modified concept of the present invention should be covered by the scope of the present invention.
Claims (6)
1. A motor rolling bearing fault model building method based on a digital twinning technology is characterized by comprising the following steps:
s1: acquisition and pretreatment of vibration data of actual physical model of motor rolling bearing
S1-1: monitoring actual vibration signals and working condition parameters in the actual operation process of a motor rolling bearing and acquiring vibration data z (t) of an actual physical model;
s1-2: carrying out denoising pretreatment on the actual vibration signal;
s1-3: calculating the vibration characteristic value of the actual material resource model as fR(c0) Wherein c is0The bearing radial play is an actual physical model;
s2: establishing a three-dimensional simulation model of a motor rolling bearing, wherein the modeling method comprises the following steps:
s2-1: obtaining the geometric dimensions and structural characteristics of different component parts of the motor bearing by referring to drawings and manuals;
s2-2: obtaining parameters of parts of different components of the bearing by looking up a nameplate and a manual;
s2-3: setting physical action relations of different component parts of the bearing respectively;
s2-4: selecting bearing geometric structure parameter variable value cj；
S2-5: establishing a three-dimensional simulation model of a motor rolling bearing in a fault-free mode;
s2-6: calculating a simulation vibration signal of the rolling bearing and a bearing vibration characteristic value thereof through the three-dimensional simulation model, and recording the vibration characteristic value of the three-dimensional simulation model of the bearing as fV(cj)；
S3: optimization and matching of geometric structure parameter variables of bearing simulation model based on K-center point
S3-1: setting a geometric structure parameter variable set O of the simulated three-dimensional model as { c ═ c1，…，ckIn which c is1Is a lower limit value of a parameter variable, ckThe upper limit value of a parameter variable;
s3-2: selection cjIs the current center point of the object O;
s3-3: calculating the variable values c of different geometric structure parameters in the bearing geometric structure parameter variable set OjCorresponding vibration characteristic value fV(cj) F of vibration characteristic value from actual physical modelR(c0) Square of distance
S3-4: constructing an objective function
S3-5: when the geometric structure parameters of the bearing continuously approach the parameter values of the actual physical model, the iteration is continuously increased to ensure that the objective function mu is increasednThe value tends to be stable;
s3-6: when in use
When the condition is met, determining the geometric structure parameter value of the bearing; otherwise, continuing the search until a condition is satisfied, wherein ε is a given sufficiently small amount;
s4: determining the three-dimensional simulation reference model of the rolling bearing in the failure-free mode according to the step S2 and the step S3
S5: three-dimensional fault simulation reference model for constructing motor rolling bearing
S5-1: acquiring fault forms of different fault types, fault positions, fault degrees and the like of the actual physical model of the motor rolling bearing in the step S1 in a dismounting and detecting mode;
s5-2: constructing a rolling bearing three-dimensional fault simulation model according to the characteristic parameters and the actual physical fault model obtained in the steps S2 and S3;
s5-3: simulating and calculating vibration signals x (t) of different faults of the rolling bearing;
s6: the actual physical test system is considered as an undistorted test system as a whole, and a virtual test system vibration model is constructed to approach a vibration signal obtained by the test device in an actual physical space, so that the vibration deviation generated by the detection device is reduced
S6-1: constructing a vibration model y (t) ═ Ax (t-t)0) Wherein the gain A is the amplitude ratio of the vibration data z (t) of the actual physical model in step S1 to the vibration signal x (t) calculated by the simulation fault model in step S5, and the lag time t0The time difference between the vibration data z (t) of the actual physical model in S1 and the vibration signal x (t) calculated by the simulation fault model in S5;
s6-2: constructing a virtual vibration data space Y (t) of a motor rolling bearing under multiple working conditions and multiple faults;
s7: state monitoring and diagnosis of motor rolling bearing by using deep neural network
S7-1: vibration data of the motor rolling bearing under different working conditions and faults are obtained through S1, and a vibration data space Z (t) of an actual physical model is constructed;
s7-2: randomly selecting sample data of 25% of the vibration data space of the actual physical model for training, testing the residual sample data, determining a deep neural network diagnosis model, and determining a threshold value according to the average diagnosis accuracy rate and the standard deviation thereof;
s7-3: diagnosing the virtual space simulation vibration data Y (t) obtained by calculation in the step S6 by adopting a deep neural network diagnosis model in the step S7-2;
s7-4: judging whether the diagnosis accuracy rate reaches a threshold value, if not, returning to the adjustment and correction of the geometric structure parameters of the three-dimensional simulation fault model of the rolling bearing by adopting a parameter optimization method based on a K-central point to obtain a fault simulation model of the motor rolling bearing with higher fidelity;
s7-5: if the threshold value is reached, establishing a simulation fault database of the motor rolling bearing digital twin fault model, and providing data support for quantitative diagnosis and performance prediction of the motor rolling bearing.
2. The method for constructing the fault model of the rolling bearing of the motor based on the digital twin technology as claimed in claim 1, wherein in step S1-2, the preprocessing method is FIR filter or wavelet de-noising.
3. The method for constructing the fault model of the motor rolling bearing based on the digital twinning technology as claimed in claim 1, wherein in step S2-1, the geometric dimensions and the structural characteristics include an outer ring diameter, an inner ring diameter, a width, a rolling body diameter, a rolling body number, a bearing pitch diameter, a contact angle, an OR curvature, an IR curvature, an outer raceway radius, and an inner raceway radius; the parameters of the part include material, density, Poisson's ratio, Young's modulus.
4. The method for constructing the fault model of the motor rolling bearing based on the digital twin technology as claimed in claim 1, wherein in step S2-3, the physical action relationship comprises a contact method and a friction property between the rolling body and the outer ring, between the rolling body and the inner ring, between the rolling body and the cage, and the like.
5. The method for constructing the fault model of the motor rolling bearing based on the digital twinning technology as claimed in claim 1, wherein in step S2-4, the bearing geometry parameter variables include a value of a play variable, a waviness, and a bearing geometry parameter variable such as a gap between a rolling element and a cage hole.
6. The method for constructing the fault model of the motor rolling bearing based on the digital twinning technology as claimed in claim 1, wherein in step S2-4, the bearing geometric structure parameter variable is the value c of the bearing radial play variablej，j＝1，…，k。
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| CN114383847B (en) * | 2022-03-23 | 2022-07-12 | 西南交通大学 | Rolling bearing full-life state monitoring method based on digital twinning |
| CN115575121A (en) * | 2022-09-26 | 2023-01-06 | 河南科技大学 | Method for constructing dynamic model of rolling bearing |
| CN117539168A (en) * | 2024-01-09 | 2024-02-09 | 江西江投能源技术研究有限公司 | Hydraulic turbine cavitation diagnosis system and method based on semi-physical simulation |
| CN117539168B (en) * | 2024-01-09 | 2024-03-26 | 江西江投能源技术研究有限公司 | Hydraulic turbine cavitation diagnosis system and method based on semi-physical simulation |
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