WO2021169229A1 - 一种基于回波信号特征参数的换能器一致性评价方法 - Google Patents

一种基于回波信号特征参数的换能器一致性评价方法 Download PDF

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WO2021169229A1
WO2021169229A1 PCT/CN2020/112989 CN2020112989W WO2021169229A1 WO 2021169229 A1 WO2021169229 A1 WO 2021169229A1 CN 2020112989 W CN2020112989 W CN 2020112989W WO 2021169229 A1 WO2021169229 A1 WO 2021169229A1
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echo signal
characteristic parameters
echo
transducer
consistency
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PCT/CN2020/112989
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French (fr)
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章圣意
赵伟国
林新霞
黄象克
林恒
卜勤超
林景殿
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浙江苍南仪表集团股份有限公司
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Priority to KR1020217041123A priority Critical patent/KR102721638B1/ko
Publication of WO2021169229A1 publication Critical patent/WO2021169229A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
    • G01F25/00—Testing or calibration of apparatus for measuring volume, volume flow or liquid level or for metering by volume
    • G01F25/10—Testing or calibration of apparatus for measuring volume, volume flow or liquid level or for metering by volume of flowmeters
    • G01F25/15—Testing or calibration of apparatus for measuring volume, volume flow or liquid level or for metering by volume of flowmeters specially adapted for gas meters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
    • G01F1/00—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow
    • G01F1/66—Measuring the volume flow or mass flow of fluid or fluent solid material wherein the fluid passes through a meter in a continuous flow by measuring frequency, phase shift or propagation time of electromagnetic or other waves, e.g. using ultrasonic flowmeters
    • G01F1/667—Arrangements of transducers for ultrasonic flowmeters; Circuits for operating ultrasonic flowmeters
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling

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  • the invention belongs to the field of ultrasonic transducer detection, and relates to a method for evaluating the consistency of a transducer based on characteristic parameters of echo signals.
  • the gas ultrasonic flowmeter As a non-contact instrument, the gas ultrasonic flowmeter has the characteristics of high precision, low pressure loss, wear resistance and wide range ratio. It is used for flow measurement in natural gas, petrochemical and civil aviation fields. As a sensor that realizes acoustic-electric conversion, the gas ultrasonic transducer is an important sensing component in the gas ultrasonic flowmeter, and it plays an important role in the performance of the gas ultrasonic flowmeter.
  • the consistency evaluation of gas ultrasonic transducers generally focuses on its steady-state performance, and the performance characteristics of the transducer are not studied under actual working conditions.
  • the dynamic performance of the transducer describes the transmission characteristics of the transducer in the working state, that is, under the excitation of the pulse signal, which is mainly manifested in the sensitivity and waveform consistency of the transducer.
  • the analysis of the dynamic performance of the transducer is to analyze the characteristics of the echo signal of the transducer under the condition of simulating the actual working state.
  • the dynamic analysis method of the transducer has a high sampling frequency of the echo signal, the calculation amount required to analyze the overall waveform similarity of the echo using the correlation method is relatively large, which is not suitable for practical applications.
  • the present invention proposes a method for evaluating the consistency of a transducer based on the characteristic parameters of the echo signal.
  • the main steps are as follows:
  • Step 1 Determine the echo signal simulation model, and the echo signal mathematical model can generally choose the Gaussian model or the mixed exponential model according to the actual echo signal.
  • the Gaussian model expression is:
  • the mixed index model expression is:
  • the echo characteristic parameter vector ⁇ [ ⁇ T m ⁇ f c ⁇ ]; m is an integer that determines the concentration of the echo signal energy, and its value range is generally [1,3]; T determines the echo The existence time of the signal; the initial phase is generally 0.
  • Step 2 Initialize the fish school parameters, import the echo signal data and perform normalization processing, including the initial artificial fish parameters, the number of artificial fish, the upper and lower limits of the parameters, the step length, the maximum number of iterations, and the value of the objective function.
  • Step 3 Set up the initial fish school randomly according to the initial parameters, and set the initial artificial fish parameters according to the general characteristics of the echo signal of the selected gas ultrasonic transducer.
  • Step 4 Calculate the objective function of all artificial fish in the initial fish school and get the optimal value and put it on the bulletin board.
  • the present invention adopts the least square method to establish the objective function.
  • x(i) is the collected echo data
  • N is the number of discrete echo signals collected
  • ⁇ (k) represents the echo parameter vector at the kth iteration
  • s( ⁇ (k) ) is the The mathematical model echo signal at k iterations
  • f( ⁇ (k) ) represents the value of the objective function at the k iteration.
  • Step 5 According to the current environment and the optimal value in the artificial fish school, select the behavior that the artificial fish school will perform, including foraging, rear-end collision, and group gathering.
  • Foraging behavior is to assume that the current artificial fish state is X k , randomly select a state X visual within its field of view, and determine the forward direction of the artificial fish by comparing the objective function solutions Y k and Y visual in the two states. Assuming that the minimum value is sought, when Y visual ⁇ Y k , X visual moves forward in the direction. When the number of trials reaches the maximum value N try , one step forwards randomly in a certain direction. Its execution mode can be expressed by the formula:
  • Rand() represents a random number from 0 to 1
  • S represents the step length of the artificial fish.
  • Swarming behavior assumes that the current artificial fish state is X k , explore the number of companions in the current field of view N f and the companion’s center position X c , when the artificial fish state is better at the central position and the number of artificial fish is not crowded, the artificial fish will be directed Move forward in the center position; otherwise perform foraging behavior.
  • the grouping behavior can be expressed as:
  • AF_Prey represents the foraging behavior
  • ⁇ is the crowding factor
  • the center position X c can be obtained from the position of the fish school in the field of view:
  • the rear-end behavior is to search for the optimal value Y min in the artificial fish within the field of view, and to determine the next step of execution by comparing the current optimal value Y k of the artificial fish X k with the optimal value Y min in the companion.
  • Y min ⁇ Y k
  • the artificial fish will move forward in the direction of the best companion. Otherwise, the foraging behavior will be performed.
  • the rear-end collision behavior can be expressed by the formula:
  • Step 6 Update your own status and compare to get the best value and replace it into the bulletin board.
  • Step 7 Determine the population size of the artificial fish school, if it is equal to N, proceed to step 8; otherwise, proceed to step 5.
  • Step 8 Determine whether the current number of iterations meets the maximum number of iterations or whether the optimal value meets the termination condition, that is, f( ⁇ (k) ) ⁇ 0.3V 2 , and if both are not satisfied, perform step 5. If any one of the conditions is met, the loop is ended, and the optimal solution of the current echo signal in the bulletin board is taken.
  • Step 9 Through the similarity comparison of the Euclidean distance of the characteristic parameters between the transducers, the consistency evaluation of the transducers is realized.
  • the characteristic parameters of the echo signal based on the Gaussian model ⁇ [ ⁇ ⁇ ⁇ f c ⁇ ] vary greatly in magnitude and dimension of each component. Therefore, it is necessary to normalize the characteristic parameters before evaluating the consistency of the transducer according to the characteristic parameters of the echo signal.
  • the expression is
  • Step 10 For the normalized echo signal characteristic parameters of each transducer, the present invention uses the Euclidean distance method to compare the difference in the numerical characteristics of the transducers, thereby evaluating the degree of consistency between the transducers .
  • the formula for solving the Euclidean distance is
  • ⁇ 1 , ⁇ 2 are the characteristic parameter vectors of different transducers
  • ⁇ 1i , ⁇ 2i are the components of the characteristic parameter vector of the corresponding transducers
  • n is the number of components of the characteristic parameter vector.
  • the present invention realizes consistency evaluation according to the difference of echo signal characteristic parameters in different environments, and can analyze the dynamic performance difference between the transducers more comprehensively, intuitively and efficiently.
  • the invention has low sampling requirements for the echo signal, reduces the computational complexity, and efficiently and intuitively realizes the consistent evaluation of the dynamic performance of the transducer.
  • Figure 1 shows the echo signal of a gas ultrasonic transducer at room temperature and pressure
  • Figure 2 is a flowchart of a method for evaluating the consistency of a transducer based on characteristic parameters of echo signals
  • Figure 3 shows the simulated echo signal of Gaussian model
  • Figure 4 shows the simulated echo signal of the mixed exponential model.
  • the echo signal of a certain model of 200kHz gas ultrasonic transducer is selected, and its echo sampling signal under normal temperature and pressure is shown in FIG. 1.
  • Figure 2 shows the flow chart of the method for evaluating the consistency of the transducer based on the characteristic parameters of the echo signal. The steps of the method are as follows:
  • Step 1 Establish a simulation signal model and compare the Gaussian model and the mixed exponential model.
  • Fig. 3 shows the simulated echo signal of the Gaussian model
  • Fig. 4 shows the simulated echo signal of the mixed exponential model.
  • the rising edge part is very close to the actual echo signal, and the falling edge part is quite different from the actual echo signal.
  • the characteristics of the rising edge of the echo signal determine the measurement of the echo signal's arrival time and have nothing to do with the falling edge of the echo signal. Therefore, by extracting the characteristic parameters of the Gaussian model of the echo signal, the characteristics of the above-mentioned edge part of the echo signal can be characterized and the consistency evaluation of the transducer applied to the gas ultrasonic flowmeter can be studied.
  • the optimal estimation of the simulated signal is achieved through artificial fish schools and the comparison through the objective function shows that the echo signal based on the Gaussian model is closer to the actual echo signal, so in this embodiment, the Gaussian model is selected for the optimal estimation of the echo signal .
  • Step 2 The artificial fish school algorithm realizes the optimal estimation of the mathematical model of the echo signal.
  • the artificial fish swarm algorithm has better global optimization capabilities and can avoid falling into local optimal solutions; it has low sensitivity to initial parameter settings and a large allowable range. Due to the large differences in the parameters of the sample transducers and the large number of local extremes, it is more appropriate to use artificial fish schools to achieve the consistency evaluation of the gas ultrasonic transducer.
  • the performance of the gas ultrasonic transducer selected according to the example of the present invention determines the initial parameters of the artificial fish school, including the initial artificial fish parameters, the number of artificial fish, the upper and lower limits of the parameters, the step length, the maximum number of iterations, and the value of the objective function.
  • the actual peak-to-peak voltage range of the echo signal is [2V, 3V]. According to the actual simulation analysis, it can be known that when the bandwidth factor exceeds the range of [2.5 ⁇ 10 3 (Hz) 2 , 1 ⁇ 10 3 (Hz) 2 ], it is based on The simulated echo signal of the Gaussian model obviously deviates from the actual echo signal.
  • the resonance frequency range of the gas ultrasonic transducer selected in this embodiment is [194kHz, 208kHz], so the estimated range can be set to [190kHz, 212kHz].
  • the echo signal arrival time ⁇ is affected by the environment and the sound path and has a large fluctuation range, so its range is [5 ⁇ 10 -6 s, 2 ⁇ 10 -2 s].
  • the fish school size is 10, the maximum number of iterations is 300, and the objective function value is 0.3V 2 .
  • Step 4 Calculate the objective function of all artificial fish in the initial fish school and get the optimal value and put it on the bulletin board, and introduce the objective function based on the least square method.
  • the formula is:
  • x(i) is the collected echo data
  • N is the number of discrete echo signals collected
  • ⁇ (k) represents the echo parameter vector at the kth iteration
  • s( ⁇ (k) ) is the The Gaussian model echo signal at k iterations
  • f( ⁇ (k) ) represents the value of the objective function at the k iteration.
  • Step 5 According to the current environment and the optimal value in the artificial fish school, select the behavior that the artificial fish school will perform, including foraging, rear-end collision, and group gathering.
  • the foraging behavior randomly selects a state according to the current artificial fish state, and determines the forward direction of the artificial fish through the objective function.
  • the expression is:
  • the swarming behavior assumes that the current artificial fish state is to explore the number of companions N f in the current field of view and the center position of the companion. When the state of the artificial fish in the central position is better and the number of artificial fish is not crowded, the artificial fish will move forward to the center position.
  • the tail-chasing behavior is to search for the optimal value Y min in the artificial fish within the field of view, and compare the current artificial fish's optimal value Y k to determine the next step of execution.
  • Y min ⁇ Y k
  • the artificial fish will move forward in the direction of the best companion, otherwise it will perform foraging behavior.
  • Step 6 Update your own status and compare to get the best value and replace it into the bulletin board.
  • Step 7 Determine the population size of the artificial fish school, if it is equal to N, go to step 7 or go to step 4.
  • Step 8 Determine whether the current number of iterations meets the maximum number of iterations or whether the optimal value meets the termination conditions. If both are not satisfied, then proceed to step 4. If any one of the conditions is met, the loop will end, and the bulletin board will be the current echo signal The optimal solution.
  • Step 9 Realize the consistency evaluation of the transducers through the comparison of the Euclidean distance of the characteristic parameters between the transducers.
  • the characteristic parameters of the echo signal based on the Gaussian model ⁇ [ ⁇ ⁇ ⁇ f c ⁇ ] vary greatly in magnitude and have different dimensions. Therefore, it is necessary to normalize the characteristic parameters before evaluating the consistency of the transducer according to the characteristic parameters of the echo signal.
  • the expression is
  • Step 10 For the normalized echo signal characteristic parameters of each transducer, the present invention uses the Euclidean distance method to compare the difference in the numerical characteristics of the transducers, thereby evaluating the degree of consistency between the transducers .
  • the formula for solving the Euclidean distance is
  • ⁇ 1 and ⁇ 2 are the characteristic parameter vectors of different transducers; ⁇ 1i and ⁇ 2i are the components of the characteristic parameter vector of the corresponding transducer; n is the number of components of the characteristic parameter vector.

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Abstract

一种基于回波信号特征参数的换能器一致性评价方法，根据气体超声换能器在不同环境下的回波信号以仿真数学模型提取其回波信号特征参数，为保证特征参数准确性采用人工鱼群算法实现回波信号特征参数的最优估计。然后，计算不同换能器回波信号总体特征参数之间的欧氏距离，通过比较欧氏距离评价换能器之间的一致性差异；根据不同环境下回波信号特征参数的差异实现一致性评价，可更为全面、直观、高效地分析换能器之间动态性能差异。

Description

一种基于回波信号特征参数的换能器一致性评价方法 技术领域
本发明属于超声波换能器检测领域，涉及一种基于回波信号特征参数的换能器一致性评价方法。
背景技术
气体超声流量计作为一种非接触式仪表，具有高精度、低压损、耐磨和宽量程比等特点，应用于天然气、石油化工和民用航空等领域的流量计量。气体超声换能器作为一种实现声电转换的传感器，是气体超声流量计中重要的传感部件，对气体超声流量计性能有着重要作用。
目前对于气体超声换能器的一致性评价一般针对其稳态性能进行研究，并没有在换能器实际工作状态下研究其性能特性。换能器的动态性能描述的是换能器在工作状态也就是在脉冲信号激励情况下的传输特性，主要表现为换能器的灵敏度与波形一致性。对换能器动态性能的分析则是在模拟实际工作状态的情况下分析换能器的回波信号特性。然而，由于换能器的动态分析方法对回波信号采样频率较高，采用相关法分析回波整体波形相似性所需计算量较大不适用于实际应用领域。
发明内容
本发明针对当前技术的不足，提出了基于回波信号特征参数的换能器一致性评价方法，主要步骤如下：
步骤一：回波信号仿真模型确定，回波信号数学模型根据实际回波信号一般可选择高斯模型或者混合指数模型。高斯模型表达式为：
Figure PCTCN2020112989-appb-000001
其中：s(θ；t)为换能器的仿真回波信号；t为时间；θ为超声波 回波信号的特征参数向量，即θ＝[β α τ f c φ]；β为回波信号的峰值电压，由于超声波在气体介质中衰减严重，因此原始的回波信号幅值一般较小；α为带宽因子，既反映了回波信号的带宽范围，又反映了回波信号在时域中的持续时间；τ为回波信号到达时间；f c为回波信号中心频率，与换能器本身的中心频率以及传输路径的频率特性有关；φ为回波信号初相位，一般取0。
混合指数模型表达式为：
Figure PCTCN2020112989-appb-000002
其中：回波特征参数向量θ＝[β T m τ f c φ]；m为整数决定了回波信号能量的集中程度，其取值范围一般为[1,3]；T则决定了回波信号的存在时间；初相位一般取0。
步骤二：对鱼群参数初始化，导入回波信号数据并进行归一化处理，其中包括初始人工鱼参数、人工鱼数量、参数上下限、步长、最大迭代次数、目标函数值。
步骤三：根据初始参数随机建立初始鱼群，按照选用的气体超声换能器回波信号的一般特性设置初始人工鱼参数。
步骤四：计算初始鱼群中所有人工鱼的目标函数并得到最优值放入公告牌。为了实现回波特征参数的最优估计需要确定鱼群的目标函数作为优化指标，本发明采用最小二乘法建立目标函数
Figure PCTCN2020112989-appb-000003
式中：x(i)为采集的回波数据，N为采集的离散回波信号数量，θ (k)表示在第k次迭代时的回波参数向量，s(θ (k))为第k次迭代时的数学模型回波信号，f(θ (k))表示在第k次迭代时的目标函数值。
步骤五：根据当前环境以及人工鱼群中最优值选择人工鱼群将要执行的行为，包括觅食、追尾、群聚。
觅食行为是假设当前人工鱼状态为X k，在其视野范围内随机选择一个状态X visual，通过比较两种状态下的目标函数解Y k和Y visual确定人工鱼的前进放向。假设求极小值，则当Y visual<Y k时则X visual向方向 前进一步。当试探次数达到最大值N try后随机向某一方向前进一步。其执行模式可由公式表示:
Figure PCTCN2020112989-appb-000004
其中Rand()表示0到1的随机数，S表示人工鱼的步长。
群聚行为假设当前人工鱼状态为X k，探索当前视野范围内的同伴数量N f和同伴的中心位置X c，当在中心位置人工鱼状态较优且人工鱼数量不拥挤时则人工鱼向中心位置前进一步；否则执行觅食行为。群聚行为可用公式表示为：
Figure PCTCN2020112989-appb-000005
其中：AF_Prey表示觅食行为，δ为拥挤度因子，中心位置X c可由视野内鱼群位置求得：
Figure PCTCN2020112989-appb-000006
追尾行为则是通过搜索视野范围内的人工鱼中的最优值Y min，通过比较当前人工鱼X k的寻优值Y k与同伴中的最优值Y min可确定下一步的执行方式。当Y min<Y k时，则人工鱼向最优同伴的方向前进一步，否则执行觅食行为，追尾行为可用公式表示为：
Figure PCTCN2020112989-appb-000007
步骤六：更新自身状态并比较得到最优值替换进入公告牌中。
步骤七：判断人工鱼群种群大小，若等于N则执行步骤八否则执行步骤五。
步骤八：判断当前迭代数是否满足最大迭代次数或者判断最优 值是否满足终止条件即f(θ (k))<0.3V 2，两者全部不满足则执行步骤五。任意满足一种条件则结束循环，取公告牌中为当前回波信号的最优解。
步骤九：通过换能器之间特征参数的欧氏距离的相似性比较实现换能器的一致性评价。基于高斯模型的回波信号特征参数θ＝[β α τ f c φ]各个分量的量级差距极大且量纲也各不相同。因此在根据回波信号特征参数进行换能器一致性评价之前需要对特征参数进行归一化处理，其表达式为
Figure PCTCN2020112989-appb-000008
式中x′ i为归一化后的特征参数分量为无量纲；x i为特征参数分量，i＝1,…,N，N表示参与一致性评价的换能器数量；x min为被测换能器中某一特征参数的最小值；x max为被测换能器中某一特征参数的最大值。
步骤十：对于归一化处理后的各个换能器回波信号特征参数，本发明采用欧氏距离法进行换能器数值特征的差异的比较，以此评价换能器之间的一致性程度。欧氏距离的求解公式为
Figure PCTCN2020112989-appb-000009
式中θ 1,θ 2分别为不同换能器的特征参数向量，θ 1i,θ 2i分别为对应换能器的特征参数向量的分量，n为特征参数向量的分量个数。
本发明的有益效果：本发明根据不同环境下回波信号特征参数的差异实现一致性评价，可更为全面、直观、高效地分析换能器之间动态性能差异。本发明对回波信号的采样要求较低且降低了计算复杂度，高效直观地实现换能器动态性能的一致性评价。
附图说明
图1为气体超声换能器常温常压下回波信号；
图2为基于回波信号特征参数的换能器一致性评价方法流程图；
图3为高斯模型仿真回波信号；
图4为混合指数模型仿真回波信号。
具体实施方式
以下结合附图对本发明进行进一步的详细描述。
本实施例选择某一型号200kHz气体超声换能器的回波信号，其在常温常压下的回波采样信号如图1所示。如图2基于回波信号特征参数的换能器一致性评价方法流程图，本方法步骤如下所示：
步骤一：通过对高斯模型和混合指数模型建立仿真信号模型并进行比较，如图3为高斯模型仿真回波信号，图4为混合指数模型仿真回波信号。
基于高斯模型的回波信号仿真模型其上升沿部分与实际回波信号极为接近，下降沿部分则与实际回波信号有着较大差异。但是基于流量计的渡越时间测量原理可知，回波信号上升沿部分的特征决定回波信号到达时间的测量而与回波信号下降沿无关。因此通过提取关于回波信号高斯模型的特征参数可以表征回波信号上述沿部分特征并研究应用于气体超声流量计的换能器一致性评价。
通过人工鱼群实现仿真信号的最优估计并通过目标函数进行比较可知基于高斯模型的回波信号更接近于实际的回波信号，因此本实施例中选用高斯模型进行回波信号的最优估计。
步骤二：人工鱼群算法实现回波信号数学模型的最优估计。人工鱼群算法具有较好的全局寻优能力，能避免陷入局部最优解；对初始参数设定的敏感性较低，允许范围较大。由于样本换能器的参数差异较大且局部极值较多，采用人工鱼群实现气体超声换能器一致性评价较为合适。
对鱼群参数初始化并导入回波信号数据。根据本发明实例选用的气体超声换能器性能确定人工鱼群各项初始参数包括初始人工鱼参数、人工鱼数量、参数上下限、步长、最大迭代次数、目标函数值。实际回波信号峰峰值电压范围为[2V,3V]，根据实际的仿真分析可知，当带宽因子超过[2.5×10 3(Hz) 2,1×10 3(Hz) 2]这一范围时基于高斯模型的仿真回波信号明显偏离实际的回波信号。本实施例所选用 的气体超声波换能器谐振频率范围为[194kHz,208kHz]，因此可设置其估计范围为[190kHz,212kHz]。回波信号到达时间τ受到环境以及声程影响波动范围较大因此其范围为[5×10 -6s,2×10 -2s]。鱼群大小为10，最大迭代次数为300，目标函数值为0.3V 2。人工鱼的感知范围V＝[0.5V 2.5×10 3(Hz) 2 5×10 -5s 10kHz]，人工鱼移动的最大步长S＝[1V 1×10 2(Hz) 2 1×10 -5s 1kHz]
步骤三：根据初始参数随机建立初始鱼群，按照气体超声换能器回波信号的一般特性设置初始人工鱼参数为X＝[1V 2.5×10 3(Hz) 21×10 -5s 200kHz]；
步骤四：计算初始鱼群中所有人工鱼的目标函数并得到最优值放入公告牌，引入基于最小二乘法的目标函数其公式为：
Figure PCTCN2020112989-appb-000010
式中：x(i)为采集的回波数据，N为采集的离散回波信号数量，θ (k)表示在第k次迭代时的回波参数向量，s(θ (k))为第k次迭代时的高斯模型回波信号，f(θ (k))表示在第k次迭代时的目标函数值。
步骤五：根据当前环境以及人工鱼群中最优值选择人工鱼群将要执行的行为，包括觅食、追尾、群聚。觅食行为根据当前人工鱼状态随机选择一个状态，通过目标函数确定人工鱼的前进方向，其表达式为：
Figure PCTCN2020112989-appb-000011
群聚行为假设当前人工鱼状态为，探索当前视野范围内的同伴数量N f和同伴的中心位置，当在中心位置人工鱼状态较优且人工鱼数量不拥挤时则人工鱼向中心位置前进一步
Figure PCTCN2020112989-appb-000012
追尾行为则是通过搜索视野范围内的人工鱼中的最优值Y min，并比较当前人工鱼的寻优值Y k确定下一步的执行方式。当Y min<Y k时，则人工鱼向最优同伴的方向前进一步，否则执行觅食行为。
步骤六：更新自身状态并比较得到最优值替换进入公告牌中。
步骤七：判断人工鱼群种群大小，若等于N则执行步骤七否则执行步骤四。
步骤八：判断当前迭代数是否满足最大迭代次数或者判断最优值是否满足终止条件，两者全部不满足则执行步骤四，任意满足一种条件则结束循环，取公告牌中为当前回波信号的最优解。
步骤九：通过换能器之间特征参数的欧氏距离的相似性比较实现换能器的一致性评价。基于高斯模型的回波信号特征参数θ＝[β α τ f c φ]各个分量的量级差距极大且量纲也各不相同。因此在根据回波信号特征参数进行换能器一致性评价之前需要对特征参数进行归一化处理，其表达式为
Figure PCTCN2020112989-appb-000013
式中x′ i为归一化后的特征参数分量为无量纲；x i为特征参数分量，i＝1,…,N，N表示参与一致性评价的换能器数量；x min为被测换能器中某一特征参数的最小值；x max为被测换能器中某一特征参数的最大值。
步骤十：对于归一化处理后的各个换能器回波信号特征参数，本发明采用欧氏距离法进行换能器数值特征的差异的比较，以此评价换能器之间的一致性程度。欧氏距离的求解公式为
Figure PCTCN2020112989-appb-000014
式中θ 1,θ 2分别为不同换能器的特征参数向量；θ 1i,θ 2i分别为对 应换能器的特征参数向量的分量；n为特征参数向量的分量个数，当只考虑换能器在常温常压下的性能则n＝5。
考虑换能器在不同环境下的回波信号的变化，因此需要对换能器不同环境下的回波信号进行特征参数提取以及一致性评价。本实施例选用的气体超声换能器选择-20℃、40℃、70℃三个温度点以及101kPa、300kPa、500kPa三个压力点分别采集回波信号。则回波信号总体特征参数向量组Θ＝[θ 1 θ 2 θ 3 θ 4 θ 5 θ 6]。

Claims (6)

  1. 一种基于回波信号特征参数的换能器一致性评价方法，其特征在于：通过仿真数学模型实现回波信号的特征参数提取，利用人工鱼群算法实现回波信号特征参数的最优估计；计算不同换能器之间回波信号特征参数的欧氏距离，以此评价换能器动态性能的一致性。
  2. 根据权利要求1所述的一种基于回波信号特征参数的换能器一致性评价方法，其特征在于：所选用的回波信号仿真数学模型选用高斯模型或者混合指数模型，两种模型都接近实际回波波形；通过将两种模型与实际回波波形比较可以选择得到更适合更近似的回波信号仿真模型。
  3. 根据权利要求2所述的一种基于回波信号特征参数的换能器一致性评价方法，其特征在于：两种仿真数学模型的下降沿部分与实际回波信号有所差异，而实际气体超声流量计通过判断回波信号上升沿部分特征确定信号到达时间，因此只考虑回波信号上升沿部分与仿真数学模型的相似度。
  4. 根据权利要求1所述的一种基于回波信号特征参数的换能器一致性评价方法，其特征在于：所述的人工鱼群算法中的目标函数基于最小二乘法，其表达式为：
    Figure PCTCN2020112989-appb-100001
    式中：x(i)为采集的回波数据，N为采集的离散回波信号数量，θ (k)表示在第k次迭代时的回波参数向量，s(θ (k))为第k次迭代时的数学模型回波信号，f(θ (k))表示在第k次迭代时的目标函数值。
  5. 根据权利要求1所述的一种基于回波信号特征参数的换能器一致性评价方法，其特征在于：对于归一化处理后的各个换能器回波信号特征参数，采用欧氏距离法进行换能器数值特征的差异的比较，以此评价换能器之间的一致性程度。
  6. 根据权利要求5所述的一种基于回波信号特征参数的换能器一致性评价方法，其特征在于：气体超声换能器回波信号随环境变化也会产 生差异，为了实现换能器之间的性能一致性评价，考虑超声波换能器在不同温度与不同压力情况下的回波信号特征参数，根据气体超声换能器在不同温度压力下的回波信号的特征参数构建其回波信号总体特征参数向量组。
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114509136A (zh) * 2022-04-21 2022-05-17 青岛鼎信通讯股份有限公司 一种超声波水表换能器一致性测试方法及测试系统

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113688486B (zh) * 2021-07-29 2024-05-28 国网吉林省电力有限公司四平供电公司 基于回波模型参数估计的变压器套管内引线超声检测方法

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1442674A (zh) * 2003-04-09 2003-09-17 李艾华 一种小管径超声波流量测量装置及方法
JP2011247624A (ja) * 2010-05-24 2011-12-08 Furuno Electric Co Ltd 水中探知装置及び水中探知方法
CN103278810A (zh) * 2013-05-10 2013-09-04 南京航空航天大学 基于空间相似度的水下目标尺度特性提取方法
CN103679762A (zh) * 2013-12-13 2014-03-26 江苏大学 一种基于稀疏数据的超声信号重建方法
CN105259540A (zh) * 2015-11-26 2016-01-20 西安电子科技大学 一种多站雷达抗有源欺骗式干扰的优化方法
CN106198763A (zh) * 2016-08-31 2016-12-07 南通友联数码技术开发有限公司 超声探伤仪性能测试装置
CN106768110A (zh) * 2017-03-08 2017-05-31 中国计量大学 一种低成本超声波换能器信号快速测量方法及电路
CN206583498U (zh) * 2017-03-08 2017-10-24 中国计量大学 一种超声波换能器信号快速测量电路
CN109816087A (zh) * 2018-12-28 2019-05-28 天津大学 基于人工鱼群与蛙群混合算法的粗糙集属性约简方法

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2078277C (en) * 1992-09-15 1999-09-14 Luc Piche Ultrasonic characterization of polymer melts under processing conditions
CN103778327A (zh) * 2014-01-09 2014-05-07 宁波职业技术学院 一种曲线拟合方法
ES2594808B1 (es) * 2016-02-04 2017-10-05 Universidad De Granada Procedimiento de obtención de datos sobre la elasticidad de materiales empleando ondas de torsión
CN106271036B (zh) * 2016-08-12 2018-09-14 广州市精源电子设备有限公司 超声波金属焊接质量评估方法、装置和超声波金属焊接机
CN109407052B (zh) * 2018-12-18 2022-09-13 江苏中海达海洋信息技术有限公司 一种线性阵换能器通道一致性校准方法
CN111220942B (zh) * 2019-12-10 2023-01-03 哈尔滨工程大学 一种接收换能器阵列幅相一致性近场校准方法
CN111458415B (zh) * 2020-04-13 2023-07-18 润电能源科学技术有限公司 一种超声相控阵换能器与待测工件耦合状态的检测方法

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1442674A (zh) * 2003-04-09 2003-09-17 李艾华 一种小管径超声波流量测量装置及方法
JP2011247624A (ja) * 2010-05-24 2011-12-08 Furuno Electric Co Ltd 水中探知装置及び水中探知方法
CN103278810A (zh) * 2013-05-10 2013-09-04 南京航空航天大学 基于空间相似度的水下目标尺度特性提取方法
CN103679762A (zh) * 2013-12-13 2014-03-26 江苏大学 一种基于稀疏数据的超声信号重建方法
CN105259540A (zh) * 2015-11-26 2016-01-20 西安电子科技大学 一种多站雷达抗有源欺骗式干扰的优化方法
CN106198763A (zh) * 2016-08-31 2016-12-07 南通友联数码技术开发有限公司 超声探伤仪性能测试装置
CN106768110A (zh) * 2017-03-08 2017-05-31 中国计量大学 一种低成本超声波换能器信号快速测量方法及电路
CN206583498U (zh) * 2017-03-08 2017-10-24 中国计量大学 一种超声波换能器信号快速测量电路
CN109816087A (zh) * 2018-12-28 2019-05-28 天津大学 基于人工鱼群与蛙群混合算法的粗糙集属性约简方法

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114509136A (zh) * 2022-04-21 2022-05-17 青岛鼎信通讯股份有限公司 一种超声波水表换能器一致性测试方法及测试系统

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