A rapid three-dimensional acoustic elasticity prediction and line spectrum identification method for ship machinery noise based on online equipment vibration monitoring.

Abstract

This invention relates to a method for rapid three-dimensional acoustoelastic prediction and line spectrum identification of ship mechanical noise based on online equipment vibration monitoring. The method includes: establishing a refined three-dimensional acoustoelastic calculation model for the overall ship vibration and acoustic radiation calculation of the equipment-vibration isolator-base-shell; applying a unit sweep force at the equipment's center of gravity node to obtain the transfer function from the average vibration acceleration spectrum of each equipment's base surface to the radiated noise; multiplying the average vibration acceleration spectrum monitored online in real time with the transfer function to obtain the ship mechanical noise under real-time excitation of each equipment; performing incoherent superposition of the radiated noise from each equipment to achieve rapid three-dimensional acoustoelastic prediction of ship mechanical noise; extracting the line spectrum peak value, and determining the main source of the line spectrum peak value by calculating the energy contribution of each equipment to the line spectrum peak value. This method achieves high-precision online prediction and automatic identification of the line spectrum source of ship mechanical noise, with high computational efficiency and strong engineering applicability.

Description

Technical Field

[0001] This invention relates to the field of online prediction technology for underwater radiated noise caused by vibration of ship equipment, and in particular to a rapid three-dimensional acoustic elasticity prediction and line spectrum identification method for ship mechanical noise based on online monitoring of equipment vibration. Specifically, it addresses how to combine vibration data monitored on the equipment base panel with the three-dimensional acoustic elasticity theory of ships to achieve online prediction of ship mechanical noise and identify the line spectrum source of mechanical noise. Background Technology

[0002] During normal ship operation, long-term monitoring of vibration and noise is crucial for ensuring vibration safety and maintaining comfort and concealment under noise conditions throughout the ship's entire lifecycle. Online underwater noise monitoring systems monitor and forecast radiated noise levels under various navigation conditions in real time, diagnose anomalies in stealth performance, promptly identify abnormal noise sources, and provide scientific decision support. Linear spectrum vibration noise carries vital information about ships in the water and is an important method for underwater acoustic equipment to detect them. Ship mechanical noise, generated by vibrations and radiated noise in the water due to the excitation of the hull by ship machinery during operation, is a significant source of linear spectrum noise. Its intensity and spectral characteristics directly affect the ship's quietness, equipment reliability, and crew comfort.

[0003] Currently, online monitoring and forecasting of ship vibration and noise have been initiated, and online noise monitoring systems have been installed. While ship acoustic monitoring and forecasting technology has made some progress in recent years, improvements are still needed in several areas, particularly in real-time rapid acoustic forecasting and noise source spectrum analysis. There is an urgent need for a method for rapid three-dimensional acoustoelastic forecasting and spectrum identification of ship mechanical noise based on online equipment vibration monitoring. Summary of the Invention

[0004] Addressing the shortcomings of existing production technologies, this applicant specifically addresses how to quantitatively assess mechanical noise based on vibration data monitored on ship equipment base panels using the three-dimensional acoustoelastic theory of ships. It provides a method for rapid prediction and line spectrum identification of ship mechanical noise based on online equipment vibration monitoring using the three-dimensional acoustoelastic theory.

[0005] The technical solution adopted in this invention is as follows: A rapid three-dimensional acoustoelastic prediction and line spectrum identification method for ship mechanical noise based on online equipment vibration monitoring includes the following steps: S1. Establish a three-dimensional acoustoelastic fine calculation model for the whole ship vibration acoustic radiation calculation of equipment-vibration isolator-base-shell; S2, apply a unit sweep force to the center of gravity node of the equipment, perform acoustoelastic calculation, and obtain the transfer function from the average vibration acceleration spectrum of the monitoring points on the base surface of each piece of equipment to the radiated noise. S3 multiplies the average vibration acceleration spectrum on the base panel of each device monitored online in real time with the sound radiation transfer function calculated in S2 to obtain the ship mechanical noise under real-time excitation of each device. S4. The radiated noise results of each device are incoherently superimposed to obtain the underwater radiated noise of the ship under the excitation of all devices, so as to realize the rapid three-dimensional acoustic elastic prediction of ship mechanical noise based on online monitoring of equipment vibration. S5. Extract the peak value of the underwater radiated noise of the ship under the excitation of the equipment, and determine the equipment with the largest energy contribution to the peak value of the line spectrum by calculating the energy contribution of each equipment.

[0006] As a further improvement to the above technical solution: In S1, the equipment is simulated using a plate with infinite stiffness and infinitesimal density. The center of the equipment is located on the plate, and the weight is simulated using a point mass element located at the center of gravity. The vibration isolator between the plate and the base is simulated using a triaxial spring.

[0007] In S2, after the acoustic elasticity calculation is completed, the average acceleration spectrum response of the monitoring points on the base panel under the excitation of each device and the corresponding radiated noise under the excitation of the ship equipment are extracted. The radiated noise is divided by the average acceleration spectrum response of the monitoring points to obtain the transfer function from the average vibration acceleration spectrum of the monitoring points on the base surface of each device to the radiated noise.

[0008] In S3, the number of vibration monitoring points for each device is less than the total number of connection points between the device and the base panel, and the number of monitoring points is greater than or equal to 1.

[0009] In S4, the phase between different device excitations is not considered. The radiated noise results of each device are incoherently superimposed, and the transfer function from the vibration of the measuring point on the base surface of each device to the radiated noise only needs to be calculated once.

[0010] In S5, the peak values ​​of the line spectrum are identified by the strict judgment method of the maximum value. That is, when the radiated noise at a certain frequency point is greater than the radiated noise values ​​of the two frequencies before and after it, it is determined to be a peak value of the line spectrum. The peak values ​​of the mechanical noise line spectrum and their corresponding frequencies are identified. Then, the peak values ​​of the spectrum are sorted from largest to smallest. Finally, the energy contribution coefficient of each device to the peak values ​​of the spectrum is calculated, and the device with the largest energy contribution coefficient to each peak value is identified.

[0011] In S1, the three-dimensional acoustic elasticity fine calculation model was established using the ship three-dimensional acoustic elasticity software THAFTS-Acoustic.

[0012] The beneficial effects of this invention are as follows: (i) This invention has noise prediction capabilities that combine high accuracy and high efficiency: This invention is the first to introduce the three-dimensional acoustoelastic theory of ships into the field of online prediction of mechanical noise. By establishing a refined calculation model of the entire ship's vibration and sound radiation, encompassing equipment, vibration isolators, base, and shell, it can accurately describe the coupling relationship between ship structural vibration and underwater radiated noise, achieving a prediction accuracy significantly higher than traditional empirical formulas or simplified numerical methods. Simultaneously, through a simplified equipment modeling strategy (simulating equipment with a flat plate of infinitely high stiffness and infinitely low density, and vibration isolators with triaxial springs), the model complexity is effectively reduced while maintaining accuracy, thus improving computational efficiency.

[0013] (ii) This invention has the capability of rapid online forecasting: This invention proposes a "transfer function" mechanism: A transfer function from the average vibration acceleration spectrum of monitoring points on the base panel of each device to the radiated noise is obtained in advance through a single acoustoelastic calculation. During online monitoring, the radiated noise result under device excitation can be quickly obtained by multiplying the real-time acquired vibration acceleration spectrum by the transfer function, eliminating the need for repeated complex acoustoelastic calculations. This method significantly shortens the noise prediction calculation time, meeting the real-time monitoring requirements of ships in operation.

[0014] (III) This invention has the advantages of requiring fewer monitoring points and having strong engineering applicability: This invention allows for fewer vibration monitoring points per device than the total number of connection points between the device and the base panel, with at least one monitoring point required. This feature significantly reduces the number and cost of sensor deployments, simplifies the hardware structure of the online monitoring system, reduces engineering implementation difficulty, and offers excellent engineering applicability and economic efficiency.

[0015] (iv) This invention has the ability to support the incoherent superposition of noise from multiple devices: This invention employs a conservative and simplified engineering approach, disregarding the phase relationship between excitations from different devices, and incoherently superimposes the radiated noise results of each device (energy superposition). This method avoids complex phase matching problems, reduces computational complexity, and simultaneously meets the conservative requirements for overall noise level assessment in engineering, making it suitable for real-world scenarios where multiple devices operate simultaneously on ships.

[0016] (v) This invention has the ability to automatically identify line spectrum peaks and locate noise sources: This invention automatically identifies the peak values ​​and corresponding frequencies of mechanical noise lines using a rigorous maximum value judgment method, and further calculates the energy contribution coefficient of each device to each peak value, accurately locating the device that contributes the most to the peak value. This function provides a scientific basis for fault diagnosis, anomaly source identification, and stealth performance assessment of ship mechanical noise, significantly improving the intelligence level of ship noise online monitoring systems.

[0017] (vi) This invention features one-time modeling and long-term use, reducing the cost of repetitive calculations: The transfer function from vibration to radiated noise at each measuring point on the base surface of the device only needs to be calculated once, and can be reused in subsequent online monitoring processes. This mechanism avoids redundant modeling and calculation, reduces the computational resource consumption of the system during long-term operation, and has good scalability and economy. Attached Figure Description

[0018] Figure 1a This is a schematic diagram of the fine calculation model of the whole ship vibration and sound radiation in an embodiment of the present invention (whole ship model).

[0019] Figure 1b This is a schematic diagram of the fine calculation model of the sound radiation of the whole ship vibration in an embodiment of the present invention (wet surface element model of the external flow field for acoustic elastic calculation).

[0020] Figure 1c This is a schematic diagram of the fine calculation model of the sound radiation of the whole ship vibration in an embodiment of the present invention (wet surface element model of the internal flow field of acoustic elastic calculation).

[0021] Figure 2 This is a schematic diagram of the connection model between the ship's internal equipment and the base in an embodiment of the present invention.

[0022] Figure 3 This is a schematic diagram of device loading and measurement points in an embodiment of the present invention.

[0023] Figure 4 This is a schematic diagram of the ship's mechanical noise and peak line spectrum in an embodiment of the present invention.

[0024] Figure 5 This is a schematic diagram of the calculation results of the line spectrum source of the example model in the embodiment of the present invention. Detailed Implementation

[0025] The specific embodiments of the present invention will now be described with reference to the accompanying drawings.

[0026] Example 1: This embodiment proposes a rapid three-dimensional acoustoelastic prediction and line spectrum identification method for ship mechanical noise based on vibration data monitored on the base panel of ship equipment and utilizing the three-dimensional acoustoelastic theory of ships for quantitative assessment.

[0027] Includes the following steps: S1. A detailed three-dimensional acoustoelastic calculation model for the vibration and acoustic radiation of the entire ship, including equipment, vibration isolators, base, and hull, was established using the ship three-dimensional acoustoelastic software THAFTS-Acoustic. S2. Using the ship three-dimensional acoustic elasticity software THAFTS-Acoustic, a unit sweep force is applied to the center of gravity node of the equipment to perform acoustic elasticity calculations and obtain the transfer function from the average vibration acceleration spectrum of the monitoring points on the base surface of each piece of equipment to the radiated noise. S3 multiplies the average vibration acceleration spectrum on the base panel of each device monitored online in real time with the sound radiation transfer function calculated in S2 to obtain the ship mechanical noise under real-time excitation of each device. S4. The radiated noise results of each device are incoherently superimposed to obtain the underwater radiated noise of the ship under the excitation of all devices, so as to realize the rapid three-dimensional acoustic elastic prediction of ship mechanical noise based on online monitoring of equipment vibration. S5 extracts the peak value of the underwater radiated noise of the ship under the excitation of the equipment, and determines the main source of the peak value of the line spectrum by calculating the energy contribution of each device to the peak value of the line spectrum.

[0028] In S1, the equipment is simulated using a plate with infinite stiffness and infinitesimal density. The center of the equipment is located on the plate, and the weight is simulated using a point mass element located at the center of gravity. The vibration isolator between the plate and the base is simulated using a triaxial spring.

[0029] In S2, after the acoustic elasticity calculation is completed, the average acceleration spectrum response of the monitoring points on the base panel under the excitation of each device and the corresponding radiated noise under the excitation of the ship equipment are extracted. The radiated noise is divided by the average acceleration spectrum response of the monitoring points to obtain the transfer function from the average vibration acceleration spectrum of the monitoring points on the base surface of each device to the radiated noise.

[0030] In S3, the number of vibration monitoring points for each device can be less than the total number of connection points between the device and the base panel, and the number of monitoring points can be greater than or equal to 1.

[0031] In S4, a conservative and simplified engineering approach is adopted, which does not consider the phase between different equipment excitations. The radiated noise results of each equipment are incoherently superimposed, and the transfer function from the vibration of the measuring point on the base surface of each equipment to the radiated noise only needs to be calculated once. This enables rapid three-dimensional acoustic elastic prediction of ship machinery noise based on online monitoring of equipment vibration.

[0032] In S5, a strict maximum judgment method is used, that is, if the radiated noise at a certain point is greater than the radiated noise values ​​of the two frequency points before and after it, it is determined to be a line spectrum peak. The line spectrum peaks and corresponding frequencies of mechanical noise are identified. Then, they are sorted from largest to smallest according to the spectral peaks. Finally, the energy contribution coefficient of each device to the spectral peak is calculated, and the device with the largest energy contribution coefficient to each line spectrum peak is identified, thus realizing the line spectrum identification of ship mechanical noise.

[0033] This embodiment significantly improves noise prediction efficiency by pre-calculating the transfer function, requiring only simple multiplication and addition operations during the online phase. It also enables automatic identification of the source of the line spectrum, providing an effective technical means for ship noise control and fault diagnosis.

[0034] Example 2: This embodiment provides a rapid three-dimensional acoustoelastic prediction method for ship mechanical noise based on online equipment vibration monitoring. The specific implementation steps are as follows: Step 1: Using the shipboard 3D acoustoelastic software Thafts-acoustic, a detailed 3D acoustoelastic calculation model of the entire ship's vibration and acoustic radiation, including the equipment, vibration isolators, base, and hull, is established (see Figure 1). The equipment is simulated using a plate with infinite stiffness and infinitesimal density; specifically, the plate's elastic modulus is set to 10. 15 Pa level, density 10 -15 kg / m 3 The weight of the equipment is simulated using point mass elements located at the center of gravity, with the center of gravity of the equipment situated on a flat plate. A set of center-of-gravity nodes is established for each piece of equipment, named G1, G2...Gn, where n is the equipment number. The vibration isolator between the equipment and the base plate is simulated using a triaxial spring (see...). Figure 2 Establish a set of monitoring points for each device on the base panel, named A. 11 A 12 ...A mn m is the equipment number, and n is the base panel monitoring point number corresponding to this equipment.

[0035] Step 2: Using the ship three-dimensional acoustic elasticity software THAFTS-Acoustic, a unit sweep force is applied to the center of gravity node of the equipment to perform acoustic elasticity calculations and extract the acceleration amplitude spectrum A of the monitoring points on the base panel under excitation of each piece of equipment. 11 ( f ), A 12 ( f ...A mn ( f ) and the corresponding equipment's radiated noise source level P1 ( f P2( f ...P m ( f Dividing the radiated noise by the average acceleration spectrum response at the monitoring point yields the transfer function H from the average vibration acceleration spectrum at the monitoring point on the base surface of each device to the radiated noise. m ( f ), H m ( f )= P m ( f ) / ((A m1 ( f)+A m2 ( f )+...+A mn ( f )) / n).

[0036] Step 3: Analyze the average vibration acceleration spectrum (A) on the base panel of each device under real-time online monitoring. m1_test ( f )+A m2_test ( f )+..+A mn_test ( f )) / n), and the acoustic radiation transfer function H calculated in the second step. m ( f Multiplying these together, we obtain the ship's mechanical noise P under real-time excitation of each device. m_test ( f ); Step 4: Incoherently superimpose the radiated noise results of each device to obtain the underwater radiated noise P( ) of the ship under the excitation of all devices. f ), P( f )=(P 1_test ( f ) 2 +P 2_test ( f ) 2 +...+ P m_test ( f ) 2 ) 0.5 This enables rapid three-dimensional acoustic elastic prediction of ship mechanical noise based on online equipment vibration monitoring; Step 5: Use a rigorous method to determine the maximum value. That is, if the radiated noise at a certain point is greater than the radiated noise values ​​of the two frequency points before and after it, it is determined to be a line spectrum peak. Identify all spectral peaks and their corresponding frequencies of mechanical noise. f peak_i i is the peak number. Then they are sorted from largest to smallest spectral peak value. Finally, the energy contribution coefficient of each device to the peak spectrum was calculated. C mi ( f peak_i )=P m_test ( f peak_i ) / ((P 1_test ( f peak_i ) 2 +P 2_test ( f peak_i )2 +...+ P m_test ( f peak_i ) 2 ) 0.5 This method identifies the device that contributes the most to the peak energy of each line spectrum, thus enabling the identification of the line spectrum device source of ship mechanical noise.

[0037] A detailed calculation model of the vibration and acoustic radiation of the entire ship, including equipment, vibration isolators, base, and hull, was established using the three-dimensional acoustic elasticity software THAFTS-Acoustic. The model has high prediction accuracy. At the same time, a simplified and effective modeling method for the connection between the equipment model and the base was proposed, balancing the model complexity and computational efficiency.

[0038] By pre-calculating the transfer function from the average vibration acceleration spectrum of each monitoring point on the base surface of the equipment to the radiated noise, the noise prediction result can be quickly obtained by multiplying the real-time monitored vibration data with the transfer function, avoiding repeated complex acoustic elasticity calculations and significantly shortening the calculation time.

[0039] This embodiment supports online monitoring, which can acquire the dynamic changes of ship mechanical noise in real time, and is suitable for noise monitoring during ship operation.

[0040] The requirement for the number of monitoring points is low (minimum 1), which reduces the cost and complexity of sensor deployment and makes it suitable for practical engineering applications.

[0041] It can identify the peak values ​​and frequencies of mechanical noise in the line spectrum and locate the main noise source equipment through energy contribution analysis, significantly improving the efficiency of ship equipment fault diagnosis.

[0042] The above description is an explanation of the present invention and not a limitation thereof. The scope of the present invention is defined by the claims. Within the scope of protection of the present invention, any form of modification may be made.

Claims

1. A method for rapid three-dimensional acoustoelastic prediction and line spectrum identification of ship machinery noise based on online equipment vibration monitoring, characterized in that: The following steps are included: S1. Establish a three-dimensional acoustoelastic fine calculation model for the whole ship vibration acoustic radiation calculation of equipment-vibration isolator-base-shell; S2, apply a unit sweep force to the center of gravity node of the equipment, perform acoustoelastic calculation, and obtain the transfer function from the average vibration acceleration spectrum of the monitoring points on the base surface of each piece of equipment to the radiated noise. S3 multiplies the average vibration acceleration spectrum on the base panel of each device monitored online in real time with the sound radiation transfer function calculated in S2 to obtain the ship mechanical noise under real-time excitation of each device. S4. The radiated noise results of each device are incoherently superimposed to obtain the underwater radiated noise of the ship under the excitation of all devices, so as to realize the rapid three-dimensional acoustic elastic prediction of ship mechanical noise based on online monitoring of equipment vibration. S5. Extract the peak value of the underwater radiated noise of the ship under the excitation of the equipment, and determine the equipment with the largest energy contribution to the peak value of the line spectrum by calculating the energy contribution of each equipment.

2. The method for rapid three-dimensional acoustoelastic prediction and line spectrum identification of ship machinery noise based on online equipment vibration monitoring as described in claim 1, characterized in that: In S1, the equipment is simulated using a plate with infinite stiffness and infinitesimal density. The center of the equipment is located on the plate, and the weight is simulated using a point mass element located at the center of gravity. The vibration isolator between the plate and the base is simulated using a triaxial spring.

3. The method for rapid three-dimensional acoustoelastic prediction and line spectrum identification of ship machinery noise based on online equipment vibration monitoring as described in claim 1, characterized in that: In S2, after the acoustic elasticity calculation is completed, the average acceleration spectrum response of the monitoring points on the base panel under the excitation of each device and the corresponding radiated noise under the excitation of the ship equipment are extracted. The radiated noise is divided by the average acceleration spectrum response of the monitoring points to obtain the transfer function from the average vibration acceleration spectrum of the monitoring points on the base surface of each device to the radiated noise.

4. The method for rapid three-dimensional acoustic elastic prediction and line spectrum identification of ship machinery noise based on online equipment vibration monitoring as described in claim 1, characterized in that: In S3, the number of vibration monitoring points for each device is less than the total number of connection points between the device and the base panel, and the number of monitoring points is greater than or equal to 1.

5. The method for rapid three-dimensional acoustoelastic prediction and line spectrum identification of ship machinery noise based on online equipment vibration monitoring as described in claim 1, characterized in that: In S4, the phase between different device excitations is not considered. The radiated noise results of each device are incoherently superimposed, and the transfer function from the vibration of the measuring point on the base surface of each device to the radiated noise only needs to be calculated once.

6. The method for rapid three-dimensional acoustic elastic prediction and line spectrum identification of ship machinery noise based on online equipment vibration monitoring as described in claim 1, characterized in that: In S5, the peak values ​​of the line spectrum are identified by the strict judgment method of the maximum value. That is, when the radiated noise at a certain frequency point is greater than the radiated noise values ​​of the two frequencies before and after it, it is determined to be a peak value of the line spectrum. The peak values ​​of the mechanical noise line spectrum and their corresponding frequencies are identified. Then, the peak values ​​of the spectrum are sorted from largest to smallest. Finally, the energy contribution coefficient of each device to the peak values ​​of the spectrum is calculated, and the device with the largest energy contribution coefficient to each peak value is identified.

7. The method for rapid three-dimensional acoustic elastic prediction and line spectrum identification of ship machinery noise based on online equipment vibration monitoring as described in claim 1, characterized in that: In S1, the three-dimensional acoustic elasticity fine calculation model was established using the ship three-dimensional acoustic elasticity software THAFTS-Acoustic.

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