CN111767671A - Unbalance parameter identification method suitable for multi-face rotor - Google Patents

Unbalance parameter identification method suitable for multi-face rotor Download PDF

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CN111767671A
CN111767671A CN202010566363.7A CN202010566363A CN111767671A CN 111767671 A CN111767671 A CN 111767671A CN 202010566363 A CN202010566363 A CN 202010566363A CN 111767671 A CN111767671 A CN 111767671A
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运侠伦
庞哲凯
梅雪松
姜歌东
刘斌
许睦旬
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Xian Jiaotong University
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Abstract

An unbalance parameter identification method suitable for a multi-face rotor comprises the steps of firstly establishing a dynamic model of a rotor shaft system to obtain theoretical unbalance response, and then combining actual unbalance response to construct an optimization target; then determining each particle dimension of the particle swarm, initializing the particle swarm, including randomly initializing the position and the speed of the particle swarm, and determining the chaotic inertial weight factor of each dimension according to the chaotic attenuation principle; performing particle swarm optimization, updating the particle speed and the particle position, iterating until the total iteration frequency reaches 80%, and entering a chaotic particle swarm optimization stage; determining a tth generation optimal particle, performing ten chaotic mapping processing by taking the tth generation optimal particle as an initial value to obtain a chaotic particle swarm, performing particle swarm optimization on the chaotic particle swarm to obtain the chaotic optimal particle, and randomly replacing one particle in an original particle swarm; repeating the steps until the set iteration times, wherein each parameter of the obtained optimal particle is the unbalance parameter; the invention meets the requirements of high-precision and simple-operation dynamic balance occasions of the rotor.

Description

Unbalance parameter identification method suitable for multi-face rotor
Technical Field
The invention relates to the technical field of rotor dynamic balance, in particular to an unbalance parameter identification method suitable for a multi-face rotor.
Background
The stability of the rotor operation is an important standard for measuring the quality of the rotary mechanical performance, and in an actual field, the rotor can be unstable due to the interference of a plurality of factors, so that the dynamic unbalance of the rotor is caused. In order to ensure the normal operation of the rotor, the performance of the rotor needs to be evaluated periodically, and the influence caused by the imbalance fault is reduced. The numerous faults that cause rotor imbalance can be broadly classified into two categories according to their causes: static faults and dynamic faults. The static faults refer to unbalance faults still existing when the equipment stops running, such as faults of uneven rotor mass distribution, mass center deviation and the like caused by machining errors, assembly errors and the like. Dynamic faults are unbalance faults caused by damage of parts of equipment under long-term operation, such as abrasion of a rotor, bearing faults and the like. For static faults, field balancing is generally performed after the rotor is assembled, and dynamic faults need to be eliminated by adopting a real-time compensation method. For the existing large-scale rotating machinery, such as large-scale steam turbine units, generator units and the like, the rotor imbalance correction is usually completed by adopting a multi-face balancing technology.
Accurate unbalance parameter identification is a prerequisite for dynamic balancing of the rotor. In order to accurately identify the imbalance parameters, methods have been proposed, which can be roughly divided into two types according to different research objects, one is an influence coefficient method based on experiments and a series of improved influence coefficient methods, and the other is an imbalance identification method based on models. The two methods are essentially two complementary unbalance parameter identification methods, the former method focuses on calibrating the influence coefficient of the system through multiple tests, and the method is suitable for the field of balancing of large-batch small-class rotors due to simple and convenient operation. The latter is an unbalance identification method based on a mathematical model of a research object, so that a plurality of tests of an influence coefficient method are omitted, and the method is suitable for identifying unbalance parameters of a specific rotor. However, both of the two methods have the problem of imbalance parameter calculation accuracy, and the number of the measuring point surfaces for rotor balance detection is smaller than that of the surfaces to be balanced, so that the ill-conditioned problem of the calculation equation caused by the imbalance parameter calculation is generally solved by increasing the number of tests, but the method still has certain limitation. Therefore, a method for identifying the imbalance parameters of the rotor without multiple tests and with high precision is urgently needed.
The intelligence is the trend of future manufacturing development, and the intelligent connotation is spontaneity, namely, the algorithm or the equipment has the capability of continuous self discovery and self updating, and the particle swarm optimization algorithm is just the intelligent algorithm. The particle swarm optimization algorithm is an optimization algorithm simulating the population of a living being, and the optimization algorithm is an optimal algorithm by continuously performing self-adjustment in the system by linking individuals and a collective. In the problem of rotor unbalance parameter identification, the accuracy of unbalance parameter solution directly determines the quality of a method, some students indirectly realize high identification accuracy by continuously improving the accuracy of a solution model, and some students continuously improve a solution algorithm to obtain a parameter value with higher accuracy, but the method is difficult to be practically applied due to the increased calculation complexity caused by excessive pursuit of accuracy. And the particle swarm optimization algorithm is simple and convenient to calculate, and the solving precision is high, so that the method is very suitable for the problem of identification of the unbalanced parameters of the rotor.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide an unbalance parameter identification method suitable for a multi-face rotor, which can meet the requirements of high-precision and simple-operation dynamic balance occasions of the rotor.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an unbalance parameter identification method suitable for a multi-face rotor comprises the following steps:
step 1, establishing a dynamic model of a rotor shaft system to obtain a theoretical unbalance response equation:
Z＝AeiΩt
wherein A is omega2[-MΩ2+JΩ2+K+iΩC]-1Q and omega are revolution speed, M is mass matrix of the system, J is inertia matrix of the system, K is rigidity matrix of the system, C is damping matrix of the system,
Figure BDA0002547773790000021
wherein QξFor the distribution of the moving coordinate system ξ to unbalance, QηIs the unbalanced distribution of the moving coordinate system η;
step 2, obtaining theoretical unbalance response Z according to the step 1, and obtaining actual unbalance response according to actual measurement
Figure BDA0002547773790000022
Constructing an optimization target:
Figure BDA0002547773790000023
wherein n is the number of the set measuring points; meanwhile, giving an optimization range phi;
step 3, determining the dimensions of each particle of the particle swarm:
p＝[x1,θ1,x2,θ2,…,xN,θN]
wherein N is the number of balance surfaces of the system;
step 4, initializing a particle swarm, including randomly initializing the position and the speed of the particle swarm, and determining the chaos inertial weight factor of each dimension according to the chaos attenuation principle:
Figure BDA0002547773790000031
wherein, wi tThe inertial weight factor, w, of the t-th generation of particle iimaxAnd wiminFor the set maximum and minimum inertial weight factors, T is the total number of iterations, rtAttenuation radius of the t-th generation, UtA tent mapping sequence for the t generation;
step 5, performing particle swarm optimization, updating the particle speed and the particle position, continuously iterating until the total iteration frequency reaches 80%, and entering a chaotic particle swarm optimization phase;
step 6, determining the t generation optimal particle p in the chaotic particle swarm optimization stageglobal_bestAnd with the particles as initial values, performing tend chaotic mapping treatment to obtain chaotic particle swarms:
p＝pglobal_best×U
wherein, U is a ten chaotic mapping sequence;
performing particle swarm optimization on the chaotic particle swarm to obtain chaotic optimal particles pchaos_global_bestRandomly replacing one particle in the original particle swarm;
step 7, repeating the step 5 and the step 6 until reaching the set iteration times to obtain the optimal particle pglobal_bestThe parameters of (1) are the imbalance parameters.
Compared with the prior art, the invention has the following beneficial technical effects:
1. according to the method, based on a dynamic model of a rotor system, after a characteristic equation of unbalance response is obtained, a particle swarm optimization solution idea is introduced to replace the original unbalance identification direct solution process, so that the ill-conditioned problem existing in the multi-surface rotor unbalance identification is effectively solved;
2. combining the chaos optimization idea and the particle swarm optimization algorithm, adopting uniformly distributed tend mapping as a mapping function, chaotizing multi-dimensional variables regarded as particles in all dimensions, and entering the chaos particle swarm optimization process when the iteration times reach 80% of the total times, thereby greatly increasing the unbalance identification precision;
3. the invention does not need complex calculation process, has simple and convenient operation and is very suitable for the practical application occasions that the measuring surface is smaller than the balance surface.
Drawings
FIG. 1 is a schematic view of a rotor system model according to an embodiment of the present invention
Fig. 2 is a schematic diagram of a rotor system measurement according to an embodiment of the present invention.
FIG. 3 is a flow chart of an embodiment of the present invention.
FIG. 4 is a diagram illustrating the effect of the test point S1 before and after balancing according to the embodiment of the present invention.
FIG. 5 is a diagram illustrating the effect of the test point S2 before and after balancing according to the embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and examples.
As shown in fig. 1, the rotor model used in the dynamic balance experimental system of the embodiment is a single-span four-balance-surface double-measurement-point rotor model, the rotor has a full length of 560mm, each 8mm shaft segment is divided into one unit, and the unit is divided into 70 units from left to right; b1 and B2 are rolling bearing supports located at the node 8 and the node 65, P1, P2, P3 and P4 are weighted balance faces located at the node 18, the node 30, the node 42 and the node 56, S1 and S2 are vibration measuring sensors, and unbalance responses of a measuring system are located at the node 24 and the node 48.
As shown in fig. 2, the experimental platform of the rotor system in the embodiment is composed of a motor speed regulator, a multi-balance-surface rotor system, a sensor, a signal preprocessor, a signal acquisition instrument, an upper computer and the like; in an experiment, the multi-balance-surface rotor system adopts a Termiki RK4 experiment table, a high-precision displacement sensor of the Germany-Miyi is selected as the sensor and used for measuring the run-out of the rotor, and meanwhile, the Termiki RK4 experiment table is provided with a groove used for measuring the actual rotating speed, so that the signal acquisition instrument outputs 5 paths of signals to the upper computer software for signal analysis.
As shown in fig. 3, a method for identifying an imbalance parameter of a multi-faceted rotor includes the following steps:
step 1, establishing a dynamic model of a rotor shaft system to obtain a theoretical unbalance response equation:
Z＝AeiΩt
wherein A is omega2[-MΩ2+JΩ2+K+iΩC]-1Q and omega are revolution speed, M is mass matrix of the system, J is inertia matrix of the system, K is rigidity matrix of the system, C is damping matrix of the system,
Figure BDA0002547773790000051
wherein QξFor the distribution of the moving coordinate system ξ to unbalance, QηIs the unbalanced distribution of the moving coordinate system η;
step 2, obtaining theoretical unbalance response Z according to the step 1, and obtaining actual unbalance response according to actual measurement
Figure BDA0002547773790000052
Constructing an optimization target:
Figure BDA0002547773790000053
wherein n is the number of the set measuring points; meanwhile, giving an optimization range phi;
step 3, determining the dimensions of each particle of the particle swarm:
p＝[x1,θ1,x2,θ2,…,xN,θN]
wherein N is the number of balance surfaces of the system;
step 4, initializing a particle swarm, including randomly initializing the position and the speed of the particle swarm, and determining the chaos inertial weight factor of each dimension according to the chaos attenuation principle:
Figure BDA0002547773790000054
wherein, wi tThe inertial weight factor, w, of the t-th generation of particle iimaxAnd wiminFor the set maximum and minimum inertial weight factors, T is the total number of iterations, rtAttenuation radius of the t-th generation, UtA tent mapping sequence for the t generation;
step 5, performing particle swarm optimization, updating the particle speed and the particle position, continuously iterating until the total iteration frequency reaches 80%, and entering a chaotic particle swarm optimization phase;
step 6, determining the t generation optimal particle p in the chaotic particle swarm optimization stageglobal_bestAnd with the particles as initial values, performing tend chaotic mapping treatment to obtain chaotic particle swarms:
p＝pglobal_best×U
wherein, U is a ten chaotic mapping sequence;
performing particle swarm optimization on the chaotic particle swarm to obtain chaotic optimal particles pchaos_global_bestRandomly replacing one particle in the original particle swarm;
step 7, repeating the steps 5 and 6 until reaching the set iteration times to obtain the optimal particle pglobal_bestThe parameters of (1) are the imbalance parameters.
And carrying out unbalance correction according to the calculated unbalance parameters, specifically comprising the following steps: if weighting correction is adopted, selecting a proper correction screw or other correction mass blocks according to the calculated mass parameters, and then carrying out weighting correction according to the calculated phase at the balance surface; if the deduplication correction is adopted, the deduplication correction is carried out on the position where the phase is rotated by 180 degrees in the reverse direction of the phase calculated on the balance plane according to the calculated quality parameters.
The dynamic balance implementation effect of the embodiment is as follows:
as shown in FIGS. 4 and 5, the weighted correction experiment is carried out at the rotating speed of the rotor system from 240r/min to 2800r/min, and it can be seen from the graph that the amplitude of the rotor system is obviously reduced after the balance, and the reduction amplitude is maximum at the rotating speed of 2040r/min, the amplitude at the measuring point of the sensor S1 is reduced from 83.12 μm to 4.93 μm, the reduction amplitude is 94.07%, and the amplitude at the measuring point of the sensor S2 is reduced from 64.65 μm to 2.89 μm, and the reduction amplitude is 95.93%. The experimental result verifies the effectiveness of the unbalance parameter identification method.
The above examples are merely illustrative of the present invention and should not be construed as limiting the scope of the present invention, and any design similar or identical to the present invention is within the scope of the present invention.

Claims (1)

1. An unbalance parameter identification method suitable for a multi-face rotor is characterized by comprising the following steps:
step 1, establishing a dynamic model of a rotor shaft system to obtain a theoretical unbalance response equation:
Z＝AeiΩt
wherein A is omega2[-MΩ2+JΩ2+K+iΩC]-1Q and omega are revolution speed, M is mass matrix of the system, J is inertia matrix of the system, K is rigidity matrix of the system, C is damping matrix of the system,
Figure FDA0002547773780000011
wherein QξFor the distribution of the moving coordinate system ξ to unbalance, QηIs the unbalanced distribution of the moving coordinate system η;
step 2, obtaining theoretical unbalance response Z according to the step 1, and obtaining actual unbalance response according to actual measurement
Figure FDA0002547773780000012
Constructing an optimization target:
Figure FDA0002547773780000013
wherein n is the number of the set measuring points; meanwhile, giving an optimization range phi;
step 3, determining the dimensions of each particle of the particle swarm:
p＝[x1,θ1,x2,θ2,…,xN,θN]
wherein N is the number of balance surfaces of the system;
step 4, initializing a particle swarm, including randomly initializing the position and the speed of the particle swarm, and determining the chaos inertial weight factor of each dimension according to the chaos attenuation principle:
Figure FDA0002547773780000014
wherein, wi tThe inertial weight factor, w, of the t-th generation of particle iimaxAnd wiminFor the set maximum and minimum inertial weight factors, T is the total number of iterations, rtAttenuation radius of the t-th generation, UtA tent mapping sequence for the t generation;
step 5, performing particle swarm optimization, updating the particle speed and the particle position, continuously iterating until the total iteration frequency reaches 80%, and entering a chaotic particle swarm optimization phase;
step 6, determining the t generation optimal particle p in the chaotic particle swarm optimization stageglobal_bestAnd with the particles as initial values, performing tend chaotic mapping treatment to obtain chaotic particle swarms:
p＝pglobal_best×U
wherein, U is a ten chaotic mapping sequence;
performing particle swarm optimization on the chaotic particle swarm to obtain chaotic optimal particles pchaos_global_bestRandomly replacing one particle in the original particle swarm;
step 7, repeating the step 5 and the step 6 until reaching the set iteration times to obtain the optimal particle pglobal_bestThe parameters of (1) are the imbalance parameters.
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Publication number Priority date Publication date Assignee Title
CN103972908A (en) * 2014-05-23 2014-08-06 国家电网公司 Multi-target reactive power optimization method based on adaptive chaos particle swarm algorithm
CN108133258A (en) * 2016-12-01 2018-06-08 南京理工大学 A kind of mixing global optimization method
CN107240923A (en) * 2017-08-10 2017-10-10 广东工业大学 A kind of Method for Reactive Power Optimization in Power and device
CN108493951A (en) * 2018-03-21 2018-09-04 中南大学 A kind of multi-objective reactive optimization method based on Chaos particle swarm optimization algorithm

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