A method for extracting dynamic characteristics of fan structure from vibration signals

Abstract

The present invention relates to a method for extracting dynamic characteristics of a fan structure from a vibration signal: S1, conduct vibration and noise tests on typical working conditions of the fan to form an optimized measurement point arrangement; S2, establish a finite element model of a fan volute, conduct measurements of the natural frequency of the fan volute in a static state, and correct the finite element model of the fan volute; S3, impose constraints on the corrected finite element model to form a standard finite element model and constraint conditions; S4, conduct a speed-up / speed-down vibration test of the fan, separate and identify the natural frequency and vibration mode of the test data; S5, compare the dynamic characteristic data of the operating modal parameter identification with the dynamic characteristic data of the finite element model with constraint conditions added, refer to the vibration mode of the structure under static and constraint conditions, and extract the dynamic characteristic data of the fan volute structure. The present invention extracts dynamic characteristics from the vibration signal of the fan structure in the operating state (speed-up and speed-down), overcoming the problem that the dynamic characteristics are difficult to directly obtain under the installation conditions of the main structure of the fan.

Description

Technical Field

[0001] The invention belongs to the technical field of ship acoustic stealth, and in particular relates to a method for extracting structural dynamic characteristics from a fan structure vibration signal. Background Art

[0002] In the field of ship acoustic stealth, the testing and analysis technology for the vibration line spectrum of rotating equipment has gradually matured, and the characteristics of the excitation source have gradually been mastered. However, the method for extracting data on the dynamic characteristics of fan structures is relatively weak, and a method for determining the dynamic characteristics of the structure has not yet been formed. The vibration response of the rotating structure contains the line spectrum of the structural dynamic characteristics, which can cause resonance in certain specific cases. Therefore, in order to fully grasp the source of the fan structure response line spectrum, an analysis and judgment method is urgently needed to determine it. The method for extracting the dynamic characteristics of the fan structure has the following difficulties:

[0003] 1) How to select vibration measurement points as input for structural dynamic characteristics data.

[0004] 2) The peak value of the line spectrum of the structural dynamic characteristics is relatively small. How to select reasonable working conditions and distinguish the dynamic characteristics from the rotational motion line spectrum.

[0005] 3) The technology based on vibration line spectrum separation needs to be used comprehensively.

[0006] 4) How to combine numerical analysis technology with experimental technology to understand the characteristics of the dynamic characteristics of fan structures. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a method for extracting the dynamic characteristics of the fan structure from a vibration signal in view of the problem that it is difficult to separate the inherent dynamic characteristics of the fan structure from the operating line spectrum after installation in the above-mentioned prior art. The method obtains the structural dynamic characteristics data from the structural vibration measurement data through measurement point optimization, static measurement, constraint application, dynamic measurement and data analysis.

[0008] The technical solution adopted by the present invention to solve the above-mentioned technical problems is:

[0009] A method for extracting dynamic characteristics of a fan structure from a vibration signal, characterized by comprising the following steps:

[0010] S1. Optimization of measurement point arrangement: Conduct vibration and noise tests on typical working conditions of the fan, compare different measurement schemes, and use the principal component analysis method to form an optimized measurement point arrangement;

[0011] S2. Static measurement and numerical calculation: Establish a finite element model of the fan volute, measure the natural frequency of the fan volute in a static state, use the modal parameter identification method to obtain the natural frequency and vibration mode of the fan volute in a static state, and compare them with the finite element calculation results to correct the finite element model of the fan volute;

[0012] S3. Apply constraints: Consider the influence of constraint conditions on the natural frequency and vibration mode of the structure, apply constraints to the modified finite element model, and finally form a standard finite element model and constraint conditions of the fan volute;

[0013] S4. Dynamic measurement: Conduct speed-up / speed-down vibration tests on the fan, and separate and identify the natural frequency and vibration mode of the test data by combining order analysis and operation modal parameter identification technology;

[0014] S5. Data analysis: Compare the dynamic characteristic data of the operating modal parameter identification with the dynamic characteristic data of the finite element model with additional constraints, refer to the vibration mode of the structure under static and constrained conditions, and extract the dynamic characteristic data of the fan volute structure.

[0015] In the above scheme, in step S1, the principal component analysis method is used to form an optimized measurement point arrangement, which specifically includes the following steps:

[0016] S1.1, the vibration signal measured at each point is X = {x 1 ,x 2 ,…,x n} T , E{X}=μ, D{X}=V≥0, where, x n is the signal of each measuring point, D is the variance, V is the variance value, E is the biased estimate, and μ is the bias parameter;

[0017] S1.2. Seek a new set of variables. First, find a linear combination y of the components of X. 1 , so that y 1 It has the largest variance, thus reflecting as much information about the changes in X as possible; then find the second linear combination y of the components of X 2 , in order to make y 2 In with y 1 The variance is the largest under unrelated conditions, and the cycle continues until the information of X is basically extracted;

[0018] S1.3. The above new variable y 1 ,y 2 It is called the principal component of X. By obtaining the principal component, the optimal number of measurement points for extracting effective signal features can be obtained.

[0019] In the above scheme, step S2 specifically includes the following steps:

[0020] S2.1. First, establish the test geometric model of the fan volute, and use the finite element method to establish the numerical model;

[0021] S2.2. Carry out the measurement of the natural frequency of the fan volute in a static state. The specific measurement method is: put the fan volute in an elastic soft connection hanging state, that is, a static free state, and use the hammer multi-point excitation method to obtain the force spectrum of the excitation force and the coherence function and frequency response function of the vibration signal;

[0022] S2.3. Using the above force spectrum, coherence function and frequency response function as input, modal parameter identification is performed using a modal parameter identification method to obtain the natural frequencies and vibration modes of each order of the fan volute in a static free state;

[0023] S2.4. Compare the obtained natural frequency and vibration mode with the calculation results of the finite element model, and modify the finite element model of the fan volute.

[0024] In the above scheme, the modal parameter identification method adopts a multi-reference LSCE method, which starts from the impulse response function of the system, calculates the poles and residues according to the complex exponential relationship between the impulse response function and the poles and residues, and then obtains the modal parameters of the system.

[0025] In the above scheme, step S3 specifically includes the following steps:

[0026] S3.1. Connect the fan volute and motor with bolts, connect the motor feet with the base through vibration isolators, and fasten the motor base with the test foundation through anchor bolts, which should be consistent with the installation status of the fan on the actual ship;

[0027] S3.2, measure and identify the natural frequency and vibration mode of the fan volute using the same method as S2 to obtain the differences caused by the change of constraint conditions;

[0028] S3.3. Taking the natural frequency and vibration mode under the actual installation state as the target, the finite element model of S2 is further modified by adjusting the form and type of constraints, and finally a standard finite element model and constraint conditions are formed.

[0029] In the above scheme, step S4 specifically includes the following steps:

[0030] S4.1. Perform speed-up / speed-down vibration tests on the fan, obtain vibration response data of each vibration measurement point, conduct order analysis, and obtain line spectra related to the fan volute and rotation;

[0031] S4.2, using the obtained line spectrum of the vibration response as input to carry out the operation modal analysis, obtain the 1st to 4th order operation modes of the fan volute under the actual installation state, and obtain the dynamic characteristic data for operation modal parameter identification;

[0032] S4.3. Using the measured parameters during operation as additional constraints, further modify the finite element model of S3 to obtain the dynamic characteristic data of the fan volute.

[0033] In the above scheme, the order analysis includes order analysis based on time domain transformation, order analysis based on Vold-Kalman filter, and order analysis based on equal-angle resampling.

[0034] In the above scheme, in step S4, the method for identifying the operating modal parameters adopts a multi-reference LSCE method, which starts from the impulse response function of the system, calculates the poles and residues according to the complex exponential relationship between the impulse response function and the poles and residues, and then obtains the modal parameters of the system.

[0035] The beneficial effects of the present invention are:

[0036] The present invention proposes a method for extracting dynamic characteristics from the vibration signal of the fan structure in the operating state (speed increase and speed decrease), which overcomes the problem that the dynamic characteristics are difficult to directly obtain under the installation condition of the main structure of the fan. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0038] Figure 1 is a comparison curve of the difference in the number of measurement points of the fan base vibration level in the embodiment of the present invention;

[0039] Figure 2 is a comparison curve of the direction difference of the vibration magnitude of the fan volute in the embodiment of the present invention;

[0040] Figure 3 This is the line spectrum of the fan in the embodiment of the present invention under the steady-state operating condition of the common speed of 2982 rpm;

[0041] Figure 4 is a test geometric model of a fan volute in an embodiment of the present invention;

[0042] Figure 5 is a finite element model of a fan volute in an embodiment of the present invention;

[0043] Figure 6 is the frequency response function of the fan volute in a stationary state in an embodiment of the present invention;

[0044] Figure 7 are the vibration modes of each order of the fan volute obtained through order analysis in the embodiment of the present invention;

[0045] Figure 8 It is a waterfall diagram of the vibration response line spectrum under the speed reduction process operating condition in an embodiment of the present invention;

[0046] Fig. 9 These are the first to fourth order operating modes of the fan volute in the actual installation state for operating modal parameter identification in the embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0048] The method for extracting the dynamic characteristics of a fan structure from a vibration signal proposed in the present invention comprises the following steps:

[0049] S1. Optimization of measurement point layout: Conduct vibration and noise tests on typical working conditions of the fan, compare different measurement schemes, and use the principal component analysis method to form an optimized measurement point layout.

[0050] In this embodiment, four measurement point arrangements were selected for the test, see Table 1.

[0051] Table 1 Vibration measurement point arrangement scheme

[0052]

[0053] Comparison of data measured at different measurement points of the base structure is shown in Figure 1 ,Through curve comparison, the three measurement point arrangement methods have ,different magnitudes in the 160Hz, 200Hz, 630Hz, 800Hz, and 1000Hz frequency bands, but the trends remain consistent.

[0054] The differences in the measurement points in each direction obtained by the fourth arrangement are shown in Figure 2 And the typical characteristic line spectrum is shown in Figure 3 .

[0055] Through the principal component analysis method, under the constraint condition of ensuring optimal data acquisition, the optimal measurement point arrangement scheme for the base structure is Scheme 2; the optimal measurement point arrangement scheme for the motor casing and fan volute structure is Scheme 3.

[0056] S2. Static measurement and numerical calculation: Establish a finite element model of the fan volute, measure the natural frequency of the fan volute in a static state, use the modal parameter identification method to obtain the natural frequency and vibration mode of the fan volute in a static state, and compare them with the finite element calculation results to correct the finite element model of the fan volute.

[0057] In this embodiment, a test geometric model of the fan volute is first established. Figure 4 , and the finite element method was used to establish the numerical model. Figure 5 .

[0058] The natural frequency of the fan volute in a static state is measured. The specific measurement method is to make the fan volute in an elastic soft connection hanging state (i.e. static free state), and use the hammer multi-point excitation method to obtain the force spectrum of the excitation force and the coherence function and frequency response function of the vibration signal. The frequency response function is shown in Figure 6; Use the above force spectrum, coherence function, and frequency response function as input to identify modal parameters and obtain the natural frequencies and vibration modes of each order of the fan volute in the static free state, see Figure 7 ; Compare the obtained natural frequency and vibration mode with the calculation results of the finite element model, and correct the finite element model of the fan volute.

[0059] S3. Apply constraints: Considering the impact of constraints on the natural frequency and vibration mode of the structure, constraints are applied to the modified finite element model, and finally a standard finite element model and constraints of the fan volute are formed.

[0060] In this embodiment, the actual installation state of the fan mainly includes the installation of the vibration isolator on the motor base, the connection and fixation of the fan volute with the motor mounting flange, and the measurement and identification of the natural frequency and vibration mode of the fan volute and the motor housing by the same method as S2 to obtain the difference caused by the change of the constraint conditions. With the natural frequency and vibration mode under the actual installation state as the target, the finite element model of S2 is further modified by adjusting the form and category of the constraint, and finally a standard finite element model and constraint conditions are formed.

[0061] S4. Dynamic measurement: Conduct speed-up / speed-down vibration tests on the fan, and separate and identify the natural frequencies and vibration modes of the test data by combining order analysis and operation modal parameter identification technology.

[0062] In this embodiment, the fan is subjected to a speed-up / speed-down vibration test, the vibration response data of each vibration measurement point is obtained, and an order analysis is performed to obtain the line spectrum related to the fan volute and rotation. The line spectrum under the speed-down process operating condition is shown in Figure 8 .

[0063] The obtained vibration response line spectrum is used as input to carry out the operation modal analysis, and the 1st to 4th order operation modes of the fan volute under the actual installation state are obtained, see Fig. 9 9-1~9-4 (It should be noted that Fig. 9 The serial number in is the node number of the geometric model, and its clarity does not affect the understanding of the solution), and then the dynamic characteristic data of the operational modal parameter identification is obtained.

[0064] The measured parameters such as the inlet and outlet wind pressure during operation are used as additional constraints to further modify the finite element model of S3 and obtain the dynamic characteristic data of the fan volute.

[0065] S5. Data analysis: Compare the dynamic characteristic data of the operating modal parameter identification with the dynamic characteristic data of the finite element model with additional constraints (inlet and outlet wind pressure, etc.) obtained in S4, and extract the dynamic characteristic data of the fan volute structure by referring to the vibration mode of the structure under static and constrained conditions.

[0066] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A method for extracting dynamic characteristics of fan structure from vibration signals, characterized in that: The following steps are involved: S1. Optimization of measurement point arrangement: Conduct vibration and noise tests on typical working conditions of the fan, compare different measurement schemes, and use the principal component analysis method to form an optimized measurement point arrangement; S2. Static measurement and numerical calculation: Establish a finite element model of the fan volute, measure the natural frequency of the fan volute in a static state, use the modal parameter identification method to obtain the natural frequency and vibration mode of the fan volute in a static state, and compare them with the finite element calculation results to correct the finite element model of the fan volute; S3. Apply constraints: Consider the influence of constraint conditions on the natural frequency and vibration mode of the structure, apply constraints to the modified finite element model, and finally form a standard finite element model and constraint conditions of the fan volute; S4. Dynamic measurement: Conduct speed-up / speed-down vibration tests on the fan, and separate and identify the natural frequency and vibration mode of the test data by combining order analysis and operation modal parameter identification technology; The specific steps include: S4.

1. Perform speed-up / speed-down vibration tests on the fan, obtain vibration response data of each vibration measurement point, conduct order analysis, and obtain line spectra related to the fan volute and rotation; S4.2, using the obtained line spectrum of the vibration response as input to carry out the operation modal analysis, obtain the 1st to 4th order operation modes of the fan volute under the actual installation state, and obtain the dynamic characteristic data for operation modal parameter identification; S4.3, further modify the finite element model of S3 using the measured parameters during operation as additional constraints to obtain the dynamic characteristic data of the fan volute; S5. Data analysis: Compare the dynamic characteristic data of the operating modal parameter identification with the dynamic characteristic data of the finite element model with additional constraints, refer to the vibration mode of the structure under static and constrained conditions, and extract the dynamic characteristic data of the fan volute structure.

2. The method for extracting dynamic characteristics of fan structure from vibration signals according to claim 1 is characterized in that: In step S1, the principal component analysis method is used to form an optimized measurement point arrangement, which specifically includes the following steps: S1.1, the vibration signal measured at each point is X = {x1, x2, ..., x n } T , E{X}=μ, D{X}=V≥0, where, x n is the signal of each measuring point, D is the variance, V is the variance value, E is the biased estimate, and μ is the bias parameter; S1.

2. Seek a new set of variables. First, find a linear combination y1 of the components of X, so that y1 has the largest variance, so as to reflect as much information about the changes in X as possible; then find the second linear combination y2 of the components of X, so that y2 has the largest variance under the condition that it is unrelated to y1, and repeat until the information of X is basically extracted; S1.

3. The new variables y1 and y2 are called the principal components of X. By obtaining the principal components, the optimal number of measurement point arrangements for extracting effective signal features can be obtained.

3. The method for extracting dynamic characteristics of fan structure from vibration signals according to claim 1, characterized in that: Step S2 specifically includes the following steps: S2.

1. First, establish the test geometric model of the fan volute, and use the finite element method to establish the numerical model; S2.

2. Carry out the measurement of the natural frequency of the fan volute in a static state. The specific measurement method is: put the fan volute in an elastic soft connection hanging state, that is, a static free state, and use the hammer multi-point excitation method to obtain the force spectrum of the excitation force and the coherence function and frequency response function of the vibration signal; S2.

3. Using the above force spectrum, coherence function and frequency response function as input, modal parameter identification is performed using a modal parameter identification method to obtain the natural frequencies and vibration modes of each order of the fan volute in a static free state; S2.

4. Compare the obtained natural frequency and vibration mode with the calculation results of the finite element model, and modify the finite element model of the fan volute.

4. The method for extracting dynamic characteristics of fan structure from vibration signals according to claim 3 is characterized in that: The modal parameter identification method adopts a multi-reference LSCE method, which starts from the impulse response function of the system, calculates the poles and residues according to the complex exponential relationship between the impulse response function and the poles and residues, and then obtains the modal parameters of the system.

5. The method for extracting dynamic characteristics of fan structure from vibration signals according to claim 1, characterized in that: Step S3 specifically includes the following steps: S3.

1. Connect the fan volute and motor with bolts, connect the motor feet with the base through vibration isolators, and fasten the motor base with the test foundation through anchor bolts, which should be consistent with the installation status of the fan on the actual ship; S3.2, measure and identify the natural frequency and vibration mode of the fan volute using the same method as S2 to obtain the differences caused by the change of constraint conditions; S3.

3. Taking the natural frequency and vibration mode under the actual installation state as the target, the finite element model of S2 is further modified by adjusting the form and type of constraints, and finally a standard finite element model and constraint conditions are formed.

6. The method for extracting dynamic characteristics of fan structure from vibration signals according to claim 1, characterized in that: The order analysis includes order analysis based on time domain transformation, order analysis based on Vold-Kalman filter, and order analysis based on equal angle resampling.

7. The method for extracting dynamic characteristics of fan structure from vibration signals according to claim 1, characterized in that: In step S4, the method for identifying the operating modal parameters adopts a multi-reference LSCE method, which starts from the impulse response function of the system, calculates the poles and residues according to the complex exponential relationship between the impulse response function and the poles and residues, and then obtains the modal parameters of the system.

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