Rotor unbalance state recognition method based on test and simulation data fusion

Abstract

The present application belongs to the technical field of aero-engine vibration monitoring, and particularly relates to a rotor unbalance state recognition method based on test and simulation data fusion, comprising: dividing the rotor into multiple structural units, determining the unbalance state recognition parameters and quantity; determining the minimum number of measurement speeds, and generating a speed sequence; establishing a rotor system model, and establishing a one-way mapping relationship from the unbalance parameters to be recognized to the cross-section vibration response of the vibration measuring point; processing the vibration data collected at each speed in the speed sequence to obtain the amplitude and phase of the speed base frequency component at each measuring point; and simultaneously solving the mapping relationship and the test vibration data at each speed to establish an unbalance parameter equation and solve it. The present application can solve the problem that the number of measuring points that can be arranged on the aero-engine is small, and sufficient data cannot be obtained to solve the unbalance state, and has important significance for monitoring the complex rotor unbalance state under working conditions and quickly locating faults.

Description

Technical Field

[0001] This invention belongs to the field of aero-engine vibration monitoring technology, specifically relating to a rotor imbalance state identification method based on the fusion of experimental and simulation data. Background Technology

[0002] The rotational inertial excitation load generated by the asymmetrical mass distribution (i.e., imbalance) of the rotor structure during operation is a major cause of excessive vibration and even failure in rotor systems under high-speed rotation. Currently, dynamic balancing is commonly used to reduce rotor imbalance. Modern advanced aero-engines have lightweight structures but heavy operating loads, making them prone to rotor imbalance due to deformation, slippage, or other reasons during operation. This imbalance changes with the working cycle. Therefore, there is an urgent need for an imbalance identification method based on vibration data for monitoring rotor imbalance during engine operation and for rapid fault location.

[0003] The limitations of existing unbalance state identification methods are mainly reflected in two aspects: 1) They usually assume that the asymmetric form of the rotor structure mass distribution is mass eccentricity, ignoring the tilt of the wheel disk's inertial principal axis. Therefore, they can only identify the unbalance quantity at one or two specified cross-sectional locations, and cannot analyze the mass distribution state of complex rotor systems; 2) Aero engines have compact structures, and the internal space limits the number and location of measurement points. The number of measurable vibration quantities is usually less than the number of unbalance state parameters to be determined, that is, the equation set to be determined is in an underdetermined state. Existing methods usually add assumptions, which leads to distortion of the identification results in some cases.

[0004] The purpose of this invention is to design a rotor imbalance state identification method based on the fusion of experimental and simulation data. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a rotor imbalance state identification method based on the fusion of experimental and simulation data, which solves the problem of limited vibration measurement points for aero-engine rotors, making it difficult to determine the imbalance state.

[0006] This invention is implemented by providing a rotor imbalance state identification method based on the fusion of experimental and simulation data, comprising the following steps:

[0007] Step 1: Divide the rotor structure into units and determine the unbalanced state parameters and quantity of each unit to be identified;

[0008] Step 2: Based on the vibration monitoring points arranged inside the engine and the quantitative relationship of the unbalanced state parameters to be identified, determine the minimum rotor vibration measurement speed that can avoid underdetermined equations, and select the test speed;

[0009] Step 3: Establish a finite element model of the rotor system based on the unbalanced state parameters to be identified, which is used to calculate the vibration response of the rotor under different speeds caused by unit unbalanced excitation. Loading nodes are set at the centroid of the rotor structural unit to apply unbalanced forces and torques.

[0010] Step 4: Based on the test speed selected in Step 2, calculate the rotor vibration response under unit unbalanced force and torque excitation in sequence, and establish a one-way mapping relationship between the unbalanced state parameters to be identified and the vibration response of the measuring point section at each test speed.

[0011] Step 5: Use the FFT algorithm to process the test vibration data at each test speed selected in Step 2 to obtain the amplitude and phase of the fundamental frequency component of the speed at the measurement point.

[0012] Step 6: Using the unidirectional mapping relationship obtained in Step 4 at each test speed and the amplitude and phase of the fundamental frequency component of the speed at the measurement point obtained in Step 5, establish the solution equation for the unbalanced state parameters to be identified.

[0013] Step 7: Use the least squares method to solve for the unbalanced state parameters to be identified.

[0014] Preferably, in step 1, the specific method for determining the parameters and number of unbalanced states to be identified is as follows:

[0015] Based on the characteristics of the rotor structural unit, the rotor structural unit is divided into mass structural units and elastic structural units. The centroid offset e and the phase β of the centroid offset e of each mass structural unit are then defined. e The principal axis tilt angle τ and the phase angle β of the principal axis tilt angle τ. τ As parameters for the unbalanced state to be identified;

[0016] Let p be the number of mass structural units, and each mass structural unit has 4 unbalanced state parameters to be identified. Then the total number of unbalanced state parameters to be identified for the rotor structural unit is 4p. Converting the unbalanced state parameters to complex form, for the j-th mass structural unit, the following operation can be performed:

[0017]

[0018] Where, 'e' is the natural logarithm, 'i' is the imaginary unit, and the tilde "~" indicates that the quantity is complex. The parameters of the unbalanced state to be identified are combined into a vector form: It is a 2p×1 vector.

[0019] Further optimization, in step 2, the method for determining the minimum rotor vibration measurement speed and selecting the test speed is as follows:

[0020] The number of measurement points inside the engine is denoted as q. Typically, the number of measurement points is small and insufficient to solve for the parameters to be identified. Therefore, rotor vibration needs to be measured at multiple speeds to obtain sufficient data. The minimum number of speeds, k, can be obtained using the following formula:

[0021]

[0022] Where ceil is the floor function, selecting k rotational speeds below the rotor's maximum speed, corresponding to angular velocities: ω1, ω2, ..., ω k .

[0023] Further optimization, in step 3, the specific method for applying excitation to the rotor system model based on the unbalanced state parameters to be identified is as follows:

[0024] First, determine the structural unit number corresponding to the component with a value of 1 in the parameter vector of the unbalanced state to be identified. If this component describes mass eccentricity, then apply an application of magnitude mω at the mass center loading node position of the corresponding rotor structural unit. 2 A rotational force with phase 0, where m is the mass of the rotor structural unit and ω is the specified rotational speed; if this component describes the tilt of the principal inertial axis, then a force of magnitude |(I_m) is applied at the corresponding rotor structural unit's center of mass loading node. d -I p The rotational torque of ω2|, where I d I is the diameter rotational inertia of the rotor structural unit. p Let I be the polar rotational inertia of the structural unit, when I d -I p When I > 0, the loading phase is 0; when I d -I p When <0, the loading phase is 180°.

[0025] Further optimization, in step 4, the specific method for establishing the unidirectional mapping relationship between the parameters of the unbalanced state to be identified and the vibration response of the vibration measuring point section is as follows:

[0026] Under rotational excitation, the vibration response amplitudes of measuring points 1 to q are denoted as x1, x2, ..., xq, respectively. q Phase is The vibration response vector in complex form is represented as follows:

[0027]

[0028] For linear systems, there is a linear mapping relationship between the response and the excitation, which can be expressed in matrix form at a given rotational speed:

[0029]

[0030] Where, mapping matrix Given a q×s matrix with s = 2p columns, the unbalanced state parameters and responses to be identified have different mapping matrices at different test speeds. Expanding the above equation, we can write:

[0031]

[0032] Let the parameter vector of the unbalanced state to be identified be sequentially... One component is 1 and the rest are 0. The vibration response under this unbalanced state is calculated using the rotor system model established in step 3. The vibration response at each measuring point corresponds to the value of a column of elements in the mapping matrix.

[0033] For example, let the j-th component in the unbalanced state parameter vector be 1, and the remaining components be 0:

[0034]

[0035] According to the mapping relationship, we have:

[0036]

[0037] That is, the j-th component of the unbalanced state parameter vector is preset to 1, and after applying excitation to the corresponding node of the rotor system model, the vibration response at the l-th measuring point in the calculation result is... Numerically related to the element ψ in the mapping matrix lj By successively setting l = 1, 2, ..., q and j = 1, 2, ..., s, a mapping matrix for each rotational speed can be obtained.

[0038] Based on the rotational speed sequence ω1,ω2,...,ω in step 2 k The mapping matrices for each test speed were calculated, and denoted as follows:

[0039] Further optimization, in step 6, the specific method for establishing the solution equation for the unbalanced state parameters to be identified is as follows:

[0040] The amplitude and phase of the fundamental frequency component of the rotational speed at the measurement point location in step 5 are represented in complex form and combined into a vector. This vector is then used in the rotational speed sequence ω1, ω2, ..., ω k The vibration response vectors under the following conditions are denoted as follows: Based on the mapping matrix obtained in step 4, the relationship at the j-th rotational speed (j = 1, 2, ..., k) can be established:

[0041]

[0042] Combining the relationships at k rotational speeds, we obtain the following equation:

[0043] in

[0044] Further optimization, in step 7, the specific method for solving the unbalanced state parameters to be identified using the least squares method is as follows:

[0045] The equation derived in step 6 middle, All elements are obtained from step 4 and are known quantities. All elements are obtained in step 5 and are known quantities. The middle element represents the parameter of the unbalanced state to be identified, which is an unknown quantity. The vector has 2p rows. The vector has k·q rows. Since kq > 2p, this equation is overdetermined. It is solved using the least squares method, as shown in the following formula:

[0046]

[0047] Compared with the prior art, the advantages of the present invention are as follows:

[0048] 1) Using the inertial principal axis offset and tilt of the rotor structural unit as rotor imbalance state parameters can concisely and effectively describe the asymmetry of rotor mass distribution. It takes into account the gyroscopic torque generated by the wheel at high speed. Compared with the description method that only uses the magnitude and phase of the imbalance, it is more accurate at high speed.

[0049] 2) By using rotor dynamics simulation results as prior information, we can make full use of vibration data under multiple engine operating conditions, avoid the problem of insufficient known quantities leading to undetermined equations that cannot be solved when the number / location of measurement points is limited, and make it possible to monitor the engine imbalance state in real time during the life cycle.

[0050] 3) While identifying imbalance states can also be achieved by deploying numerous measuring points, this method is resource-intensive and difficult to apply in engineering projects. This invention employs a data fusion approach, where the main workload is focused on establishing mapping relationships, while the computational load in solving for imbalance state parameters is minimal, allowing for faster and more convenient identification of imbalance state parameters. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the rotor system structure to be identified according to an embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of the components of the rotor system to be identified according to an embodiment of the present invention;

[0053] Figure 3 This is a flowchart of the analysis process of the present invention;

[0054] Figure 4 This is a schematic diagram illustrating the definition of the centroid offset and the tilt angle of the principal axis of inertia. Detailed Implementation

[0055] The realization of the objectives, functional characteristics, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art.

[0056] Figure 1 This is a schematic diagram of the rotor system structure to be identified used in this embodiment. The rotor system 1 has three measuring points arranged during operation, namely measuring point 1 (2), measuring point 2 (3), and measuring point 3 (4).

[0057] Figure 2 This is a schematic diagram of the components of the rotor system to be identified used in this embodiment. The rotor system includes: rotor first pivot 5, rotor front journal 6, compressor assembly 7, drum shaft 8, turbine assembly 9, turbine rear journal 10, and rotor second pivot 11.

[0058] This invention provides a method for identifying rotor imbalance states based on the fusion of experimental and simulation data. (Refer to...) Figure 3 The diagram shown is a flowchart illustrating a rotor imbalance state identification method based on the fusion of experimental and simulation data according to an embodiment of the present invention. In this embodiment, the rotor imbalance state identification method based on the fusion of experimental and simulation data includes the following steps:

[0059] Step 1: Divide the rotor structure into units and determine the parameters and number of unbalanced states to be identified.

[0060] A structural unit refers to a unit body formed by dividing the rotor structure according to the structural characteristics of its components and fully considering the mass / stiffness distribution characteristics of each part. In dynamic analysis, the main mechanical properties of concern include stiffness characteristics and mass inertia characteristics. Based on this, structural units can be further divided into two categories: mass structural units and elastic structural units.

[0061] For components of an aero-engine rotor structure, those components whose mass accounts for more than 30% of the total rotor structure mass and whose deformation during rotor movement is negligible are classified as mass structural units. For the rotor system, the mass structural units include: compressor assembly 7 and turbine assembly 9.

[0062] For components of an aero-engine rotor structure, those components whose mass accounts for less than 15% of the total rotor structure mass and whose self-deformation is significant during rotor movement are classified as elastic structural units. For the rotor system, the elastic structural unit includes: the rotor front journal 6, the drum shaft 8, and the turbine rear journal 10.

[0063] The mass structural unit has a relatively large mass and moment of inertia, and is the main source of rotational inertial load in the rotor system, while the elastic structural unit has a smaller mass, and its rotational inertial load can be ignored. Therefore, setting the eccentricity and tilt angle of the principal axis of inertia of the mass structural unit as parameters of rotor imbalance can concisely and accurately describe the asymmetric state of rotor mass distribution. The definition of the eccentricity and tilt angle of a single structural unit is as follows: Figure 4 As shown. A coordinate system O-xyz is established with the centerline of the mass structural unit as the z-axis. The distance between the center of mass and the centroid is the eccentricity, denoted as the mass offset e. The angle between the principal axis of inertia and the z-axis is the tilt angle τ. Projecting the principal axis of inertia (the half-axis in the positive z-axis direction) onto the xOy plane yields ray GP. The angle between the line OG and the positive x-axis is the phase of the mass eccentricity, denoted as β. e The angle between ray GP and the positive x-axis is the phase of the tilt of the principal axis of inertia, denoted as β. τ .

[0064] The rotor system 1 has p = 2 mass structural units, and the number of unbalanced state parameters to be identified is 4p = 8. The vector of parameters to be identified is... Subscript 1 represents the compressor assembly, and subscript 2 represents the turbine assembly.

[0065] Step 2: Determine the minimum rotational speed for rotor vibration measurement and select the test speed. The engine has three measuring points, i.e., q = 3. The minimum rotational speed k is determined by the following formula:

[0066]

[0067] The maximum operating speed of the rotor is 13,000 rpm. In this embodiment, the test speeds are set to 4,000 rpm and 12,000 rpm, corresponding to angular velocities of ω1 = 418.9 rad / s and ω2 = 1256.6 rad / s.

[0068] Step 3: Establish a rotor system model based on the structural parameters of the rotor to be identified, and set a loading node at the center of mass of the structural unit to apply unbalanced forces and moments. The relevant parameters of the mass structural unit are shown in Table 1.

[0069] Table 1 Mass parameters of the test apparatus structural unit

[0070] Mass m (kg) <![CDATA[Diameter moment of inertia I d (kg·m 2 )]]> <![CDATA[Polar moment of inertia I p (kg·m 2 ) <!-- 5 -->]]> air compressor 120 4.61 5.41 turbine 138 3.14 6.11

[0071] Step 4: Based on the rotational speed sequence selected in Step 2, select rotational speeds in sequence, calculate the rotor vibration response under given unbalanced force and torque excitation, and establish a one-way mapping relationship between the unbalanced state parameters to be identified and the vibration response of the vibration measurement point section at each rotational speed.

[0072] For the rotor system, the vibration amplitude and phase of the rotor structure at three measuring points need to be calculated at two speeds, 4000 rpm and 12000 rpm, under four unit imbalance conditions: compressor eccentricity, compressor tilt, turbine eccentricity, and turbine tilt. For the mass eccentricity case, the unit eccentricity is taken as 1 mm; for the inertial principal shaft tilt case, the unit tilt angle is taken as 1 rad. The calculation results of the rotor system are shown in Table 2.

[0073] Table 2 Simulation results under unity unbalanced excitation

[0074]

[0075]

[0076] Convert the data in Table 2 to complex form and construct a mapping matrix. The mapping matrix at 4000 rpm is as follows:

[0077]

[0078] The mapping matrix at 12000 rpm is:

[0079]

[0080] Step 5: The vibration data of the rotor system during the test run were processed using the FFT algorithm to obtain the amplitude and phase of the fundamental frequency component of the rotational speed at each measuring point at 4000 rpm and 12000 rpm. The processing results are shown in Table 3.

[0081] Table 3 Vibration measurement results of the test run

[0082]

[0083] Step 6: Convert the vibration data from the test points into complex form, and establish the equations for solving the unbalanced state parameters by relating the mapping relationships between the 4000 rpm and 12000 rpm states.

[0084]

[0085] Step 7: Use the least squares method to solve for the unbalanced state parameters.

[0086]

[0087] The calculation results are shown in Table 4.

[0088] Table 4. Results of Unbalanced State Parameter Identification

[0089]

[0090] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Any modifications, alterations, substitutions, or variations made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention shall fall within the protection scope defined by the claims of the present invention.

Claims

1. A rotor imbalance state identification method based on the fusion of experimental and simulation data, characterized in that, Includes the following steps: Step 1: Divide the rotor structure into units and determine the unbalanced state parameters and quantity of each unit to be identified; Step 2: Based on the vibration monitoring points arranged inside the engine and the quantitative relationship of the unbalanced state parameters to be identified, determine the minimum rotor vibration measurement speed that can avoid underdetermined equations, and select the test speed; Step 3: Establish a finite element model of the rotor system based on the unbalanced state parameters to be identified, which is used to calculate the vibration response of the rotor under different speeds caused by unit unbalanced excitation. Loading nodes are set at the centroid of the rotor structural unit to apply unbalanced forces and torques. Step 4: Based on the test speed selected in Step 2, calculate the rotor vibration response under unit unbalanced force and torque excitation in sequence, and establish a one-way mapping relationship between the unbalanced state parameters to be identified and the vibration response of the measuring point section at each test speed. Step 5: Use the FFT algorithm to process the test vibration data at each test speed selected in Step 2 to obtain the amplitude and phase of the fundamental frequency component of the speed at the measurement point. Step 6: Using the unidirectional mapping relationship at each test speed obtained in Step 4 and the amplitude and phase of the fundamental frequency component of the speed at the measurement point obtained in Step 5, establish the solution equation for the unbalanced state parameters to be identified. Step 7: Use the least squares method to solve for the unbalanced state parameters to be identified; In step 1, the specific method for determining the parameters and number of unbalanced states to be identified is as follows: Based on the characteristics of the rotor structural units, the rotor structural units are divided into mass structural units and elastic structural units, and the centroid offset of each mass structural unit is calculated. and centroid offset phase Inclination angle of principal axes of inertia and the tilt angle of the principal axis of inertia phase angle As parameters for the unbalanced state to be identified; Let the number of mass structural units be . p Each mass structural unit has four unbalanced state parameters to be identified, therefore the total number of unbalanced state parameters to be identified for the rotor structural unit is four. p The parameters of the unbalanced state to be identified are converted into complex form, that is, for the first... j For each mass structural unit, perform the following operations: ； Where e is the natural logarithm, i is the imaginary unit, and the upper tilde " "" indicates that the quantity is a complex number, and the parameters of the unbalanced state to be identified are combined into a vector form: , for vector.

2. The rotor imbalance state identification method based on the fusion of experimental and simulation data according to claim 1, characterized in that, In step 2, the method for determining the minimum rotational speed for rotor vibration measurement and selecting the test speed is as follows: The number of internal test points of the engine is recorded as follows: q Typically, the number of measurement points is too small to solve for the parameters to be identified. Therefore, it is necessary to measure rotor vibration at multiple speeds to obtain sufficient data. The minimum number of speeds is... k We obtain it from the following formula: ； Where ceil is the floor function, selected below the maximum rotor speed. k The rotational speeds and their corresponding angular velocities are as follows: .

3. The rotor imbalance state identification method based on the fusion of experimental and simulation data according to claim 2, characterized in that, In step 3, the specific method for applying excitation to the rotor system model based on the unbalanced state parameters to be identified is as follows: First, determine the structural unit number corresponding to the component with a value of 1 in the parameter vector of the unbalanced state to be identified. If this component describes mass eccentricity, then apply an application of magnitude [value missing] at the mass center loading node position of the corresponding rotor structural unit. A rotational force with a phase of 0, where The mass of this rotor structural unit, For the specified rotational speed; if this component describes the inertial principal axis tilt, then an application of magnitude [value missing] is applied at the corresponding rotor structural unit's centroid loading node position. The rotational torque, where The diameter of the rotor structural unit is the moment of inertia. Let be the polar rotational inertia of the structural unit, when When the loading phase is 0, The loading phase is 180°.

4. The rotor imbalance state identification method based on the fusion of experimental and simulation data according to claim 2, characterized in that, In step 4, the specific method for establishing the unidirectional mapping relationship between the unbalanced state parameters to be identified and the vibration response of the vibration measuring point section is as follows: Under rotational excitation, measuring point 1~ q The vibration response amplitudes are denoted as follows: Phase is The vibration response vector in complex form is represented as follows: ； For linear systems, there is a linear mapping relationship between the response and the excitation, which can be expressed in matrix form at a given rotational speed: ； Where, mapping matrix for Matrix, number of columns The unbalanced state parameters and responses to be identified have different mapping matrices at different test speeds. Expanding the above equation, we can write: ； Let the parameter vector of the unbalanced state to be identified be sequentially... One component is set to 1, and the rest are set to 0. The vibration response under this unbalanced state is calculated using the rotor system model established in step 3. The vibration response at each measuring point corresponds to the value of a column of elements in the mapping matrix. According to the rotational speed sequence in step 2 The mapping matrices for each test speed were calculated, and denoted as follows: .

5. The rotor imbalance state identification method based on the fusion of experimental and simulation data according to claim 4, characterized in that, In step 6, the specific method for establishing the solution equations for the unbalanced state parameters to be identified is as follows: The amplitude and phase of the fundamental frequency component of the rotational speed at the measurement point location in step 5 are represented in complex form and combined into a vector. This vector is then used in the rotational speed sequence. The vibration response vectors under the following conditions are denoted as follows: Based on the mapping matrix obtained in step 4, establish the first... g The relationship at each rotational speed. g =1,2,..., k : ； Will k Combining the relationships at each rotational speed, we obtain the following equation: 。 6. The rotor imbalance state identification method based on the fusion of experimental and simulation data according to claim 5, characterized in that, In step 7, the specific method for solving the unbalanced state parameters to be identified using the least squares method is as follows: The equation derived in step 6 middle, All elements are obtained from step 4 and are known quantities. All elements are obtained in step 5 and are known quantities. The middle element represents the parameter of the unbalanced state to be identified, which is an unknown quantity. The vector has 2 p OK, Vectors have Okay, because This equation is an overdetermined equation, and it is solved using the least squares method, as shown in the following formula: 。

Images