Underwater propulsion system assembly consistency multi-dimension quantitative evaluation method

Abstract

The application relates to a kind of underwater propulsion system assembly consistency multi-dimension quantification evaluation method.The method comprises: building underwater propulsion system test platform, multiple vibration measuring points are arranged in the inside of propulsion system and shell, acoustic sensor is arranged around the platform, and vibration and sound pressure signals of multiple batches of same type products are collected.Fourier transform is carried out on the signals to obtain vibration and sound pressure spectrum data, rotation frequency and its harmonic are extracted from the vibration spectrum, and the vibration spectrum characteristic index of propeller blade is calculated, while the sound pressure spectrum data are processed to calculate the acoustic spectrum consistency characteristic index.Nine-dimensional feature vector of each batch of products is constructed based on these indexes, a statistical database is established, the type of assembly process deviation is located according to the index value change in the database, and the assembly consistency comprehensive score of each batch of products is calculated by weighted summation, combined with the weight coefficient of the influence of each index on acoustic performance.The application can meet the demand of high-quality batch production of underwater equipment.

Description

Technical Field

[0001] This application relates to the field of underwater propulsion system assembly evaluation technology, and in particular to a multi-dimensional quantitative evaluation method for the consistency of underwater propulsion system assembly. Background Technology

[0002] As a core power component of underwater robots and other equipment, the vibration and acoustic radiation performance of underwater propulsion systems directly determines the acoustic stealth performance and effectiveness of the equipment. The assembly consistency of mass-produced underwater propulsion systems is a key factor in ensuring the stability of their vibration and noise performance. However, in actual manufacturing processes, due to the dispersion of assembly process parameters, significant performance differences often exist between different products.

[0003] Currently, the evaluation of the assembly quality of underwater propulsion systems, both domestically and internationally, mainly relies on the following methods: Preventative control based on geometric dimension inspection ensures assembly quality by strictly controlling the geometric dimensions and form and position tolerances of components. While it can prevent assembly problems to some extent, it has the following drawbacks: dimensional compliance does not equate to performance consistency; the same dimensional accuracy may lead to different dynamic characteristics due to factors such as fit conditions and preload distribution; excessively tightening dimensional tolerances significantly increases manufacturing costs and cannot cover deviations generated during assembly. Final inspection methods based on acoustic testing evaluate product quality by measuring radiated noise characteristics through underwater acoustic testing. Although this method directly reflects final performance, it is a post-production inspection; problems are discovered after the product is fully assembled, resulting in high rectification costs. Furthermore, acoustic testing requires strict environmental conditions and has a long testing cycle, making it unsuitable for rapid quality evaluation in large-scale production. Evaluation methods based on time-domain vibration indices assess assembly quality by measuring the overall level of vibration velocity or acceleration (RMS value). While simple and easy to implement, these methods have significant limitations: the overall time-domain value is insensitive to early faults and minor assembly deviations, cannot identify specific assembly problem types, and is even more difficult to pinpoint specific process parameter deviations. For example, the same overall vibration value may be caused by different reasons such as dynamic imbalance, misalignment, or improper bearing clearance, but time-domain methods cannot effectively distinguish between them. Based on traditional frequency-domain analysis, some studies employ spectral analysis techniques to evaluate quality by comparing the amplitudes of characteristic frequencies. While these methods are improvements over time-domain analysis, they still have significant limitations: they typically focus only on a few key frequency components, ignoring rich frequency-domain information such as spectral morphology, harmonic structure, and sideband characteristics; and they lack a systematic index system and quantitative evaluation standards, making it difficult to accurately quantify assembly consistency.

[0004] The main problems with existing technologies can be summarized as follows: evaluation dimensions are limited, lacking a systematic multi-index evaluation system; diagnostic capabilities are insufficient, failing to establish a correspondence between performance anomalies and specific assembly process parameters; and control costs are high, relying on high-precision machining and post-production inspection, lacking economical and effective process control methods. Therefore, there is an urgent need to develop a new evaluation method that can accurately quantify assembly consistency, possess fault diagnosis capabilities, and is easy to implement in engineering, to address the shortcomings of existing technologies and meet the high-quality mass production requirements of underwater equipment. Summary of the Invention

[0005] Therefore, it is necessary to provide a multi-dimensional quantitative evaluation method for the assembly consistency of underwater propulsion systems that can meet the requirements of high-quality mass production of underwater equipment, in order to address the above-mentioned technical problems.

[0006] A multi-dimensional quantitative evaluation method for assembly consistency of underwater propulsion systems, the method comprising: An underwater propulsion system test platform was built, and multiple vibration measurement points were set up inside the propulsion system and the shell. Acoustic sensors were arranged around the test platform to collect vibration signals and sound pressure signals from multiple batches of the same type of underwater propulsion system. Each acquired vibration signal and sound pressure signal was subjected to Fourier transform to obtain the corresponding vibration spectrum data and sound pressure spectrum data; Key characteristic frequencies are extracted from vibration spectrum data, including rotational frequency and its harmonics, propeller blade passing frequency and its harmonics, bearing characteristic frequencies, and structural modal frequencies. Based on key characteristic frequencies, vibration spectrum characteristic indicators are calculated; sound pressure spectrum data are processed to calculate acoustic spectrum consistency characteristic indicators. A nine-dimensional feature vector is constructed for each batch of products based on the vibration spectrum characteristic index and the acoustic spectrum consistency characteristic index, and a statistical database of batch products is established based on the nine-dimensional feature vector. Based on the numerical changes of various indicators in the statistical database, the assembly process deviation types of the underwater propulsion system are identified. Using a weighted summation method, and combining the weight coefficients of the influence of each indicator on the acoustic performance of the underwater propulsion system in the nine-dimensional feature vector, the overall assembly consistency score of each batch of products is calculated.

[0007] The aforementioned multi-dimensional quantitative evaluation method for the assembly consistency of underwater propulsion systems firstly involves building a test platform, setting up multiple vibration measurement points inside the propulsion system and its hull, and arranging acoustic sensors around the platform to collect vibration and sound pressure signals from multiple batches of products. This achieves multi-dimensional data acquisition from internal vibration excitation to external sound radiation, providing a foundation for a comprehensive evaluation of assembly consistency. Secondly, Fourier transforms are performed on the signals to extract key characteristic frequencies such as rotational frequency and its harmonics, propeller blade passing frequency and its harmonics, bearing characteristic frequencies, and structural modal frequencies. Vibration and acoustic spectral characteristic indicators are calculated, a nine-dimensional feature vector is constructed, and a statistical database is established, forming a systematic multi-indicator evaluation system that overcomes the shortcomings of existing technologies with their single evaluation dimensions. Furthermore, based on the numerical changes of various indicators in the statistical database, key process deviations such as rotor dynamic balance, shaft alignment, bearing clearance, and bolt preload can be accurately located. A clear mapping relationship between spectral characteristics and specific assembly process parameters is established, solving the problem of insufficient diagnostic capabilities in existing technologies. Finally, a weighted summation method was used to calculate the overall score of assembly consistency, which achieved accurate quantification of assembly consistency. Moreover, this method is based on process data collection and analysis, and does not rely excessively on high-precision processing and post-production inspection, thus reducing control costs. It provides an effective technical means to ensure the consistency of acoustic stealth performance of underwater equipment and meets the needs of high-quality mass production. Attached Figure Description

[0008] Figure 1 This is a flowchart illustrating a multi-dimensional quantitative evaluation method for the assembly consistency of an underwater propulsion system in one embodiment. Detailed Implementation

[0009] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0010] In one embodiment, such as Figure 1 As shown, a multi-dimensional quantitative evaluation method for the assembly consistency of an underwater propulsion system is provided, including the following steps: Step 102: Build an underwater propulsion system test platform, set up multiple vibration measurement points inside the propulsion system and hull, and arrange acoustic sensors around the test platform to collect vibration and sound pressure signals from multiple batches of the same type of underwater propulsion system.

[0011] The underwater propulsion system addressed in this application mainly consists of the following components: 1. Power source: Permanent magnet synchronous motor, including motor stator and motor rotor; 2. Transmission shaft system: including motor shaft and thruster shaft, connected by flexible couplings; 3. Support system: including radial bearings and thrust bearings; 4. Key Connections: The motor is connected to the bracket, and the bracket is connected to the housing, all via circumferentially distributed bolts; 5. Load: Thruster.

[0012] Secondly, this application aims to quantify the consistency of several key assembly process parameters of the propulsion system through vibration and acoustic spectrum characteristics: 1. Dynamic balance mass of the motor rotor; 2. Shaft alignment accuracy (including radial offset and angular offset); 3. Thrust bearing clearance; 4. The fit quality between the bushing and the shaft system (including the interference fit and clearance). 5. Uniformity of bolt preload distribution.

[0013] It is known that the test bench for the same model product in this application has been used for testing. m Secondary vibration and acoustic signal acquisition, with separate installations inside the propulsion system and the hull. n A vibration measurement point was used, and an acoustic sensor was arranged around the test bench to measure the sound pressure. The sound power level was calculated by measuring the sound pressure at each point on the measurement envelope surrounding the product. Sampling frequency... Fs The frequency is 20kHz, and the sampling time is 30s.

[0014] The underwater propulsion system test platform was built to achieve comprehensive data acquisition of the underwater propulsion system from internal vibration to measuring points inside the propulsion system and shell to external acoustic radiation to acoustic sensors around the test platform. It covers the complete performance transmission link from the internal excitation source to the external acoustic radiation, providing comprehensive and authentic raw data support for subsequent multi-dimensional and systematic assembly consistency evaluation, and solving the problem of incomplete evaluation caused by traditional methods that only focus on a single dimension.

[0015] Step 104: Perform Fourier transform on each acquired vibration signal and sound pressure signal to obtain the corresponding vibration spectrum data and sound pressure spectrum data.

[0016] The signals collected from each channel are subjected to Fourier transform to obtain spectrum data.

[0017] The vibration signal is represented as The sound pressure signal is represented as Thus, spectral data can be obtained. .

[0018] Converting time-domain signals into frequency-domain spectral data can accurately capture key frequency information directly related to the assembly process, such as frequency conversion and its harmonics, propeller blade passing frequency and its harmonics, bearing characteristic frequency, and structural modal frequency. This lays the foundation for subsequent analysis of rich frequency-domain information such as spectral morphology, harmonic structure, and sideband characteristics. It overcomes the shortcomings of traditional time-domain methods, which are insensitive to early faults and minor assembly deviations, and frequency-domain methods, which only focus on a few major frequency components.

[0019] Step 106: Extract key characteristic frequencies from the vibration spectrum data. Key characteristic frequencies include rotational frequency and its harmonics, propeller blade passing frequency and its harmonics, bearing characteristic frequencies, and structural modal frequencies.

[0020] Step 108: Calculate vibration spectrum characteristic indicators based on key characteristic frequencies; process sound pressure spectrum data and calculate acoustic spectrum consistency characteristic indicators.

[0021] Around the measuring points on the shell, calculate the frequency band energy distribution entropy value of each batch of products according to the following formula. :

[0022] in, B The number of frequency bands can be divided based on 1 / 3 octave bands. For the first i Batch of products b Vibrational energy of each frequency band Indicates the first b The energy percentage of each frequency band.

[0023] index This represents the degree of disorder in the frequency domain distribution of vibration energy, characterizes the overall assembly quality, and is related to multiple assembly process parameters. Products with good assembly quality and consistent state will have energy concentrated in a few characteristic frequency bands, resulting in lower entropy values; conversely, multiple assembly problems will lead to energy dispersion and increased entropy values.

[0024] from m From each batch of products, characteristic frequencies were extracted based on the vibration spectrum around a specific vibration measurement point near the motor drive end. f Amplitude at point A fi , i =1,2,… m And calculate the coefficient of variation of characteristic frequencies. K f To evaluate the consistency of the fine structure of spectral peaks:

[0025] in, for m Batch product amplitude Af The sample mean, is the sample standard deviation. f The rotor fundamental frequency, specifications K f This is the fundamental frequency amplitude stability coefficient, which directly reflects the vibration consistency caused by the rotor residual unbalance.

[0026] Select a benchmark product with superior assembly quality, and calculate the harmonic structure similarity around a measuring point near the support position. Spectral morphology consistency evaluation:

[0027] in, K The harmonic order to be considered is usually taken as 2 to 5. Indicates the first i Batch Product Feature Frequency k First harmonic f k The amplitude, Indicates the benchmark product number k The amplitude of the first harmonic wave, For the first i The mean amplitude of each harmonic of the batch of products. This represents the average amplitude of each harmonic of the benchmark product.

[0028] index S H The similarity of each batch of products to the baseline product in terms of harmonic amplitude distribution was measured, and misalignment significantly altered the second harmonic energy. Depending on the actual product structure, the characteristic frequency can be taken as the rotor fundamental frequency or the blade passage frequency.

[0029] Each batch of products n Each measurement point is paired to form a measurement point pair. The characteristic frequency phase correlation coefficient is calculated according to the following formula. The characteristic frequency is usually the rotational frequency or the blade passage frequency:

[0030] in, S For the logarithm of the measurement points being analyzed, T To determine the number of time periods analyzed, For the first i Batch of products s For the measuring points in the time period t Characteristic frequency phase difference, For the benchmark product s For the measuring points in the time period t The reference phase difference.

[0031] index The stability of the phase relationship between measuring points as the vibration wave propagates along the shaft system was evaluated. Poor alignment or misalignment of supports will disrupt this stable phase relationship.

[0032] Through dynamic testing and analysis, the following was identified: m The modal frequencies of each batch of products were measured, and the modal frequency dispersion was calculated. :

[0033] in, The modal order for analysis is usually taken as the first 3 to 6. For the first o The modal frequency vector has a dimension equal to the batch number. m , For the first o The batch-to-batch standard deviation of the first modal frequencies, For the first o The batch-to-batch mean of the first modal frequencies.

[0034] index This reflects the changes in the overall or local dynamic characteristics of the structure caused by inconsistent process parameters affecting connection stiffness, such as bolt preload and interference fit.

[0035] The characteristic sideband energy ratio of each batch of products is calculated using the following formula, focusing on the measuring points near the thrust bearing. To evaluate the consistency of spectral modulation characteristics:

[0036] in, P For the number of characteristic frequencies, Indicates the first i Batch of products p Characteristic frequencies (such as the resonant frequencies excited by bearing failure). Indicates the first i Energy at characteristic frequencies of batch products These are the two sides of the characteristic frequency. Energy within the bandwidth.

[0037] index The amplitude modulation effect caused by uneven or defective bearing clearance was quantified. The larger the clearance or the greater the unevenness, the deeper the modulation. The larger the value.

[0038] Will include m The vector representation of the sound power level measurement values ​​of the batch of products is as follows The sound power level dispersion is calculated according to the following formula. :

[0039] in, This represents the sample mean of the sound power level of the batch of products. This represents the sample standard deviation.

[0040] index It is the most direct manifestation of the overall consistency of sound radiation energy, and its dispersion comprehensively reflects the consistency of vibration transmission and sound radiation efficiency. Exceeding this standard means that there are systemic problems with the overall assembly consistency.

[0041] Calculate the correlation coefficient of the sound pressure spectrum shape in the characteristic frequency band. :

[0042] in, f b This represents the center frequency of 1 / 3 octave band. For the first i Batch products at frequency f b The sound pressure level at that location, For reference products at frequency f b The sound pressure level at that location, and The first i Batch products and benchmark products in B Average sound pressure level of each frequency band. (Indicator) The evaluation assesses the consistency of the noise spectrum shape. This metric effectively separates different types of assembly problems. The correlation coefficient in the low-frequency band... Related to rotor imbalance and shaft misalignment, the correlation coefficient in the low-frequency range is usually the same as that in the high-frequency range. This is strongly correlated with bearing clearance and local looseness, and these faults generate broadband or modulated noise.

[0043] By constructing vibration spectrum characteristic indicators including frequency band energy distribution entropy, characteristic frequency variation coefficient, and harmonic structure similarity, as well as acoustic spectrum consistency characteristic indicators such as sound power level dispersion and characteristic frequency band sound pressure spectrum correlation coefficient, a systematic multi-index evaluation system has been formed. This system enables precise quantification of assembly consistency from multiple dimensions such as energy distribution, spectrum similarity, and phase relationship, solving the problem of the lack of a systematic index system and quantitative evaluation standards in existing technologies.

[0044] Step 110: Construct a nine-dimensional feature vector for each batch of products based on the vibration spectrum characteristic index and the acoustic spectrum consistency characteristic index, and establish a statistical database for the batch of products based on the nine-dimensional feature vector.

[0045] Based on the above indicator values, a batch statistical database is established. For each batch of products, its nine-dimensional feature vector can be calculated:

[0046] Based on the vibration and acoustic performance indicators, the problem is located according to the following procedure: like rise, Increase, at the same time The value decreased; this is judged to be due to poor rotor imbalance and consistency. like rise, and A decrease in the value indicates insufficient alignment accuracy of the shaft system; like rise, A decrease in the value can be attributed to uneven clearance in the thrust bearing. like rise, The increase is judged to be due to inconsistent structural connection stiffness, such as bolt preload or shell connection. If multiple vibration and acoustic indicators are abnormal at the same time, it can be judged that the overall assembly quality of the product is inconsistent.

[0047] By integrating multi-dimensional vibration and acoustic characteristic indicators, a nine-dimensional feature vector is formed for each batch of products. Based on this, a statistical database of multiple batches of products is established, providing a data foundation for subsequent analysis of the numerical change relationship between indicators and the location of process deviation types. This enables digital and standardized management of assembly consistency and facilitates rapid comparison and analysis of performance differences between batches of products during batch production.

[0048] Step 112: Based on the numerical change relationship of each indicator in the statistical database, locate the assembly process deviation type of the underwater propulsion system, use a weighted summation method, and assign weight coefficients to the importance of each indicator of the nine-dimensional feature vector on the acoustic performance of the underwater propulsion system, and calculate the comprehensive assembly consistency score of each batch of products.

[0049] The overall consistency score for each batch of products is calculated using a weighted summation method:

[0050] Among them, the weighting coefficient w 1, w 2,…, w 9. The impact of each indicator on the final acoustic performance of the system will be allocated according to its importance. This impact can be quantified through simulation or experimentation.

[0051] Based on the numerical changes of various indicators in the statistical database, key process deviations such as rotor imbalance, insufficient shaft alignment, and uneven thrust bearing clearance can be accurately identified. A clear mapping relationship between spectral characteristics and specific assembly process parameters has been established, solving the problems of insufficient diagnostic capability and inability to locate specific process parameter deviations in traditional methods. At the same time, by calculating the comprehensive score of assembly consistency through weighted summation, an intuitive and quantitative evaluation of assembly quality is achieved. Moreover, this method can be implemented based on process data, without excessive reliance on high-precision machining and post-production inspection, thus reducing control costs and meeting the needs of rapid, economical, and precise quality control for high-quality mass production of underwater equipment.

[0052] The aforementioned multi-dimensional quantitative evaluation method for the assembly consistency of underwater propulsion systems firstly involves building a test platform, setting up multiple vibration measurement points inside the propulsion system and its hull, and arranging acoustic sensors around the platform to collect vibration and sound pressure signals from multiple batches of products. This achieves multi-dimensional data acquisition from internal vibration excitation to external sound radiation, providing a foundation for a comprehensive evaluation of assembly consistency. Secondly, Fourier transforms are performed on the signals to extract key characteristic frequencies such as rotational frequency and its harmonics, propeller blade passing frequency and its harmonics, bearing characteristic frequencies, and structural modal frequencies. Vibration and acoustic spectral characteristic indicators are calculated, a nine-dimensional feature vector is constructed, and a statistical database is established, forming a systematic multi-indicator evaluation system that overcomes the shortcomings of existing technologies with their single evaluation dimensions. Furthermore, based on the numerical changes of various indicators in the statistical database, key process deviations such as rotor dynamic balance, shaft alignment, bearing clearance, and bolt preload can be accurately located. A clear mapping relationship between spectral characteristics and specific assembly process parameters is established, solving the problem of insufficient diagnostic capabilities in existing technologies. Finally, a weighted summation method was used to calculate the overall score of assembly consistency, which achieved accurate quantification of assembly consistency. Moreover, this method is based on process data collection and analysis, and does not rely excessively on high-precision processing and post-production inspection, thus reducing control costs. It provides an effective technical means to ensure the consistency of acoustic stealth performance of underwater equipment and meets the needs of high-quality mass production.

[0053] In one embodiment, the types of assembly process deviations include poor rotor imbalance consistency, insufficient shaft alignment accuracy, uneven thrust bearing clearance, inconsistent structural connection stiffness, and inconsistent overall assembly quality.

[0054] In one embodiment, the vibration spectrum characteristic indicators include the entropy value of the frequency band energy distribution, the coefficient of variation of the characteristic frequency, the harmonic structure similarity, the phase correlation coefficient of the characteristic frequency, the modal frequency dispersion, and the energy ratio of the characteristic sidebands. Based on the key characteristic frequencies, the vibration spectrum characteristic indicators are calculated, including: The vibration spectrum is divided based on a 1 / 3 octave band. B The frequency band, calculate the first i Batch of products bThe proportion of vibration energy in each frequency band is determined, and then the entropy of the frequency band energy distribution is calculated based on the proportion of vibration energy. ; in, B The number of frequency bands can be divided based on 1 / 3 octave bands. For the first i Batch of products b Vibrational energy of each frequency band Indicates the first b Energy percentage of each frequency band; from m Characteristic frequencies were extracted from the vibration spectrum of each batch of products. f The amplitude at a given point is calculated, along with the sample mean and standard deviation. Then, the coefficient of variation of the characteristic frequency is calculated based on the sample mean and standard deviation. ; in, for m Batch product amplitude A f The sample mean, This represents the sample standard deviation.

[0055] In one embodiment, the harmonic order is determined, and the th harmonic is extracted. i Harmonic differences between batch products and benchmark products f k The amplitude at point 1 is calculated. i The mean amplitudes of each harmonic order in the batch product and the benchmark product are used to calculate the harmonic structure similarity based on the amplitudes of each harmonic order and the mean amplitude. ; in, K The total number of harmonic orders. Indicates the first i Batch Product Feature Frequency k First harmonic f k The amplitude, Indicates the benchmark product number k The amplitude of the first harmonic wave, For the first i The mean amplitude of each harmonic of the batch of products. This represents the average amplitude of each harmonic of the benchmark product. Will n The vibration measuring points are combined in pairs to form S For each measurement point, determine the number of time periods for analysis. T Extract the first i The test points of the batch product and the reference product at various time periods tThe characteristic frequency phase difference is then used to calculate the characteristic frequency phase correlation coefficient. ; in, S For the logarithm of the measurement points being analyzed, T To determine the number of time periods analyzed, For the first i Batch of products s For the measuring points in the time period t Characteristic frequency phase difference, For the benchmark product s For the measuring points in the time period t The reference phase difference.

[0056] In one embodiment, identification is achieved through dynamic testing. m Modal frequencies of each batch of products were analyzed to determine the modal order for analysis. Extract the first o The first modal frequency vector is used to calculate the second modal frequency vector. o The batch-to-batch mean and standard deviation of the modal frequencies are calculated, and then the modal frequency dispersion is calculated based on the batch-to-batch mean and standard deviation. ; in, The modal order being analyzed, For the first o The modal frequency vector has a dimension equal to the batch number. m , For the first o The batch-to-batch standard deviation of the first modal frequencies, For the first o The batch-to-batch mean of the first modal frequencies.

[0057] In one embodiment, the number of characteristic frequencies analyzed is determined. P Extract the first i The energy at each characteristic frequency of the batch of products and the sum of the energy within the bandwidth on both sides of the characteristic frequency are then used to calculate the energy ratio of the characteristic sideband. ; in, P For the number of characteristic frequencies, Indicates the first i Energy at characteristic frequencies of batch products These are the two sides of the characteristic frequency. Energy within the bandwidth This indicates the rotor's fundamental frequency.

[0058] In one embodiment, the acoustic spectrum consistency characteristic index includes sound power level dispersion and sound pressure spectral shape correlation coefficient in characteristic frequency bands. The sound pressure spectral data is processed to calculate the acoustic spectrum consistency characteristic index, including: The sound power level dispersion was calculated based on the sample mean and sample standard deviation of the sound power level of the batch of products. ; in, This represents the sample mean of the sound power level of the batch of products. This represents the sample standard deviation.

[0059] In one embodiment, the center frequency is determined based on 1 / 3 octave band. f b Extract the first i Batch products and reference products at center frequency f b The sound pressure level at point 1 is calculated. i Batch products and benchmark products B The average sound pressure level of each frequency band is used as the basis for calculating the correlation coefficient of the sound pressure spectral shape of the characteristic frequency band based on the sound pressure level and the average sound pressure level. ; in, f b This represents the center frequency of 1 / 3 octave band. For the first i Batch products at frequency f b The sound pressure level at that location, For reference products at frequency f b The sound pressure level at that location, and The first i Batch products and benchmark products in B The average sound pressure level of each frequency band k Indicates the harmonic order. K This indicates the total number of harmonic orders.

[0060] In one embodiment, a nine-dimensional feature vector for each batch of products is constructed based on the vibration spectrum characteristic index and the acoustic spectrum consistency characteristic index. ; in, For sound power level dispersion, For the first i Characteristic sideband energy ratio of batch products For the first i Entropy values ​​of frequency band energy distribution of batch products For the first iThe correlation coefficient of low-frequency bands of batch products For the first i Correlation coefficients of high-frequency bands in batches of products The characteristic frequency variation coefficient, For the first i Harmonic structure similarity of batch products For the first i The characteristic frequency phase correlation coefficient of a batch of products This represents the modal frequency dispersion.

[0061] In one embodiment, a weighted summation method is used, combining the weighting coefficients of the importance of each index of the nine-dimensional feature vector to the acoustic performance of the underwater propulsion system, to calculate the comprehensive assembly consistency score for each batch of products, including: A weighted summation method is adopted, and weighting coefficients are assigned based on the importance of each index of the nine-dimensional eigenvector to the acoustic performance of the underwater propulsion system. to Calculate the overall assembly consistency score for each batch of products. ; in, For sound power level dispersion, For the first i Characteristic sideband energy ratio of batch products For the first i Entropy values ​​of frequency band energy distribution of batch products For the first i The correlation coefficient of low-frequency bands of batch products For the first i Correlation coefficients of high-frequency bands in batches of products The characteristic frequency variation coefficient, For the first i Harmonic structure similarity of batch products For the first i The characteristic frequency phase correlation coefficient of a batch of products This represents the maximum value of the sound power level dispersion. This represents the maximum value of the frequency band energy distribution entropy. This represents the minimum value of the correlation coefficient in the low-frequency band. This represents the minimum value of the correlation coefficient in the high-frequency band. This represents the maximum value of the characteristic frequency variation coefficient. This represents the minimum value of the harmonic structure similarity. This represents the minimum value of the phase correlation coefficient at the characteristic frequency. This represents the maximum value of the modal frequency dispersion. The maximum value of the characteristic sideband energy ratio. This represents the modal frequency dispersion.

[0062] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0063] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0064] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A multi-dimensional quantitative evaluation method for the assembly consistency of an underwater propulsion system, characterized in that, The method includes: An underwater propulsion system test platform was built, and multiple vibration measurement points were set up inside the propulsion system and the shell. Acoustic sensors were arranged around the test platform to collect vibration signals and sound pressure signals from multiple batches of the same type of underwater propulsion system. Each acquired vibration signal and sound pressure signal was subjected to Fourier transform to obtain the corresponding vibration spectrum data and sound pressure spectrum data; Key characteristic frequencies are extracted from the vibration spectrum data, including rotational frequency and its harmonics, propeller blade passing frequency and its harmonics, bearing characteristic frequencies, and structural modal frequencies. Based on the key characteristic frequencies, vibration spectrum characteristic indicators are calculated; the sound pressure spectrum data is processed to calculate acoustic spectrum consistency characteristic indicators; the vibration spectrum characteristic indicators include frequency band energy distribution entropy, characteristic frequency variation coefficient, harmonic structure similarity, characteristic frequency phase correlation coefficient, modal frequency dispersion, and characteristic sideband energy ratio; the acoustic spectrum consistency characteristic indicators include sound power level dispersion and characteristic frequency band sound pressure spectral shape correlation coefficient. The acoustic spectrum consistency characteristic indicators are calculated by processing the sound pressure spectrum data, including: The sound power level dispersion was calculated based on the sample mean and sample standard deviation of the sound power level of the batch of products. in, This represents the sample mean of the sound power level of the batch of products. The standard deviation is the sample standard deviation. A nine-dimensional feature vector is constructed for each batch of products based on the vibration spectrum characteristic index and the acoustic spectrum consistency characteristic index. A statistical database of the batch of products is established based on the nine-dimensional feature vector. The nine-dimensional feature vector includes sound power level dispersion, characteristic sideband energy ratio, frequency band energy distribution entropy value, correlation coefficient of low frequency band, correlation coefficient of high frequency band, characteristic frequency variation coefficient, harmonic structure similarity, characteristic frequency phase correlation coefficient, and modal frequency dispersion. Based on the numerical variation relationship of each indicator in the statistical database, the assembly process deviation type of the underwater propulsion system is located. Using a weighted summation method, and combining the weight coefficients of the influence of each indicator of the nine-dimensional feature vector on the acoustic performance of the underwater propulsion system, the overall assembly consistency score of each batch of products is calculated.

2. The method according to claim 1, characterized in that, The types of assembly process deviations include poor rotor imbalance consistency, insufficient shaft alignment accuracy, uneven thrust bearing clearance, inconsistent structural connection stiffness, and inconsistent overall assembly quality.

3. The method according to claim 1, characterized in that, Based on the aforementioned key characteristic frequencies, vibration spectrum characteristic indices are calculated, including: The vibration spectrum is divided based on a 1 / 3 octave band. B The frequency band, calculate the first i Batch of products b The proportion of vibration energy in each frequency band is determined, and then the entropy value of the frequency band energy distribution is calculated based on the proportion of vibration energy. in, B The number of frequency bands can be divided based on 1 / 3 octave bands. For the first i Batch of products b Vibrational energy of each frequency band Indicates the first b Energy percentage of each frequency band; from m Characteristic frequencies were extracted from the vibration spectrum of each batch of products. f The amplitude at a given point is calculated, along with the sample mean and standard deviation. Then, the coefficient of variation of the characteristic frequency is calculated based on the sample mean and standard deviation. in, for m Batch product amplitude A f The sample mean, This represents the sample standard deviation.

4. The method according to claim 3, characterized in that, The method further includes: Determine the harmonic order and extract the first harmonic. i Harmonic differences between batch products and benchmark products f k The amplitude at point 1 is calculated. i The mean amplitudes of each harmonic order in the batch product and the benchmark product are used to calculate the harmonic structure similarity based on the amplitudes of each harmonic order and the mean amplitude. in, K The total number of harmonic orders. Indicates the first i Batch Product Feature Frequency k First harmonic f k The amplitude, Indicates the benchmark product number k The amplitude of the first harmonic wave, For the first i The mean amplitude of each harmonic of the batch of products. This represents the average amplitude of each harmonic of the benchmark product. Will n The vibration measuring points are combined in pairs to form S For each measurement point, determine the number of time periods for analysis. T Extract the first i The test points of the batch product and the reference product at various time periods t The characteristic frequency phase difference is then used to calculate the characteristic frequency phase correlation coefficient. in, S For the logarithm of the measurement points being analyzed, T To determine the number of time periods analyzed, For the first i Batch of products s For the measuring points in the time period t Characteristic frequency phase difference, For the benchmark product s For the measuring points in the time period t The reference phase difference.

5. The method according to claim 3, characterized in that, The method further includes: Identification through dynamic testing m Modal frequencies of each batch of products were analyzed to determine the modal order for analysis. Extract the first o The first modal frequency vector is used to calculate the second modal frequency vector. o The batch-to-batch mean and standard deviation of the modal frequencies are calculated, and then the modal frequency dispersion is calculated based on the batch-to-batch mean and standard deviation. in, The modal order being analyzed, For the first o The modal frequency vector has a dimension equal to the batch number. m , For the first o The batch-to-batch standard deviation of the first modal frequencies, For the first o The batch-to-batch mean of the first modal frequencies.

6. The method according to claim 3, characterized in that, The method further includes: Determine the number of characteristic frequencies for analysis P Extract the first i The energy at each characteristic frequency of the batch of products and the sum of the energy within the bandwidth on both sides of the characteristic frequency are then used to calculate the characteristic sideband energy ratio. in, P For the number of characteristic frequencies, Indicates the first i Energy at characteristic frequencies of batch products These are the two sides of the characteristic frequency. Energy within the bandwidth This indicates the rotor's fundamental frequency.

7. The method according to claim 1, characterized in that, The method further includes: The center frequency is determined based on a 1 / 3 octave band. fb Extract the first i Batch products and reference products at center frequency fb The sound pressure level at point 1 is calculated. i Batch products and benchmark products B The average sound pressure level of each frequency band is used as the basis for calculating the correlation coefficient of the sound pressure spectral shape of the characteristic frequency band based on the sound pressure level and the average sound pressure level. in, f b This represents the center frequency of 1 / 3 octave band. For the first i Batch products at frequency f b The sound pressure level at that location, For reference products at frequency f b The sound pressure level at that location, and The first i Batch products and benchmark products in B The average sound pressure level of each frequency band k Indicates the harmonic order. K This indicates the total number of harmonic orders.

8. The method according to claim 1, characterized in that, Based on the vibration spectrum characteristic index and the acoustic spectrum consistency characteristic index, a nine-dimensional feature vector is constructed for each batch of products. in, The sound power level dispersion, For the first i Characteristic sideband energy ratio of batch products For the first i Entropy values ​​of frequency band energy distribution of batch products For the first i The correlation coefficient of low-frequency bands of batch products For the first i Correlation coefficients of high-frequency bands in batches of products The characteristic frequency variation coefficient, For the first i Harmonic structure similarity of batch products For the first i The characteristic frequency phase correlation coefficient of a batch of products This represents the modal frequency dispersion.

9. The method according to claim 1, characterized in that, A weighted summation method was used, combining the weighting coefficients based on the importance of each index in the nine-dimensional feature vector to the acoustic performance of the underwater propulsion system, to calculate the comprehensive assembly consistency score for each batch of products, including: A weighted summation method is adopted, and weighting coefficients are assigned based on the importance of each index of the nine-dimensional eigenvector to the acoustic performance of the underwater propulsion system. to Calculate the overall assembly consistency score for each batch of products. in, The sound power level dispersion, For the first i Characteristic sideband energy ratio of batch products For the first i Entropy values ​​of frequency band energy distribution of batch products For the first i The correlation coefficient of low-frequency bands of batch products For the first i Correlation coefficients of high-frequency bands in batches of products The characteristic frequency variation coefficient, For the first i Harmonic structure similarity of batch products For the first i The characteristic frequency phase correlation coefficient of a batch of products This represents the maximum value of the sound power level dispersion. This represents the maximum value of the frequency band energy distribution entropy. This represents the minimum value of the correlation coefficient in the low-frequency band. This represents the minimum value of the correlation coefficient in the high-frequency band. This represents the maximum value of the characteristic frequency variation coefficient. This represents the minimum value of the harmonic structure similarity. This represents the minimum value of the phase correlation coefficient at the characteristic frequency. This represents the maximum value of the modal frequency dispersion. The maximum value of the characteristic sideband energy ratio. This represents the modal frequency dispersion.

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