Metal part temperature measurement method and system based on ultrasonic signal

Abstract

The invention provides a metal part temperature measuring method and system based on an ultrasonic signal, and the method comprises the steps: generating a sweep frequency electric signal through an ultrasonic transmitting unit, and transmitting the sweep frequency electric signal to the surface of a to-be-measured metal part through a power amplifier; the ultrasonic transduction receiver receives the reflected sound wave signal and then carries out signal amplification and digital processing to obtain a discrete time sequence; inputting the discrete time sequence into a pre-established temperature prediction model to calculate related parameters; inputting the obtained parameter data into a pre-established and trained metal part temperature calculation model for temperature calculation; and the human-computer interface displays the temperature value of the measured metal part in real time. According to the invention, through non-contact temperature measurement, the technical problems of easy damage to a to-be-measured metal part, response lag and incapability of measuring the temperature of the to-be-measured metal part in the existing contact temperature measurement technology are solved; compared with an existing non-contact infrared temperature measurement method, the influence of the surface state on the measurement result can be effectively reduced, and the temperature measurement precision is greatly improved.

Description

Technical Field

[0001] This invention belongs to the field of temperature detection technology for metal parts, specifically relating to a method and system for measuring the temperature of metal parts based on ultrasonic signals. The method calculates the temperature of the metal parts by taking advantage of the different propagation speeds of ultrasonic signals in metal materials at different temperatures. Background Technology

[0002] The temperature of metal parts, including commonly used processing and production equipment, hot-processed parts, and machined products, affects their dimensional accuracy and thermal deformation. Therefore, temperature monitoring of metal parts is a key technology in aerospace, energy, and high-end manufacturing. Existing common temperature measurement methods are divided into contact and non-contact methods. Among these, the most common contact method is the thermocouple method, which offers high accuracy but suffers from drawbacks such as damaging installation, response lag, and inability to measure temperature. The most common non-contact temperature measurement method is infrared thermography, but its accuracy is greatly affected by surface conditions. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for measuring the temperature of metal parts based on ultrasonic signals, which solves the technical problems of existing contact temperature measurement technology that easily damages the metal parts under test, has a slow response, and cannot measure the temperature. Compared with existing non-contact infrared temperature measurement methods, it can effectively reduce the influence of surface condition on the measurement results and greatly improve the accuracy of temperature measurement.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions.

[0005] On one hand, the present invention provides a temperature measurement system for metal parts based on ultrasonic signals, comprising: a waveform generator, a power amplifier, an ultrasonic transducer, a low-noise preamplifier, and a data acquisition card; the power amplifier is connected to the output of the waveform generator and is used to amplify the electrical signal voltage generated by the waveform generator to drive the ultrasonic transducer; the transmitting end of the ultrasonic transducer is connected to the output of the power amplifier; the input of the low-noise preamplifier is connected to the receiving end of the ultrasonic transducer and is used to amplify the reflected echo signal; the metal part to be tested is equidistantly arranged between the transmitting end and the receiving end of the ultrasonic transducer for detection; the data acquisition card is connected to the output of the low-noise preamplifier and is used to convert the preamplified echo signal into a discrete digital signal.

[0006] The aforementioned temperature measurement system for metal parts based on ultrasonic signals further includes an embedded processor and a human-machine interface;

[0007] The embedded processor includes a signal processing module and a data storage module. The input terminal of the signal processing module receives the digital signal, and the output terminal of the signal processing module is connected to the input terminal of the data storage module. The signal processing module is used to store and execute signal processing and temperature calculation algorithms, and the data storage module is used to store raw waveform data.

[0008] The human-machine interface includes a display screen and a touch screen, which are electrically connected to a signal processing module for parameter setting, status monitoring, and result display.

[0009] The aforementioned temperature measurement system for metal parts based on ultrasonic signals also includes a precision mechanical adjustment frame and a laser positioner. The precision mechanical adjustment frame is connected to the ultrasonic transducer and is used to adjust the position and angle of the ultrasonic transducer relative to the metal part to be measured. The laser positioner is used to assist the transmitting end of the ultrasonic transducer in aligning with the measurement point of the metal part to be measured.

[0010] Secondly, the present invention provides a method for measuring the temperature of metal parts based on ultrasonic signals, comprising:

[0011] S101: Ultrasonic signal generation and transmission: The ultrasonic transducer and laser positioner are aligned with the metal part under test using a precision mechanical adjustment frame, and the distance between the ultrasonic transducer and the metal part under test is measured. The waveform generator produces an electrical signal. And it is emitted to the surface of the metal part to be tested through the transmitting end of the power amplifier and ultrasonic transducer;

[0012] S102: Reflected signal reception and processing: After receiving the reflected echo signal, the ultrasonic transducer amplifies the signal and performs digital processing to obtain a discrete time series. ;

[0013] S103: Parameter calculation of the temperature prediction model: Calculating the discrete time series... The temperature prediction model, pre-established in the signal processing module, is input and combined with the distance between the ultrasonic transducer and the metal part under test. The relevant parameters are calculated to obtain the reference speed of sound. Sound attenuation coefficient Nonlinear acoustic parameters and flight time difference ;

[0014] S104: Establish a temperature calculation model for metal parts;

[0015] S105: Output of temperature calculation results: The obtained reference sound velocity Sound attenuation coefficient Nonlinear acoustic parameters and flight time difference The temperature of the metal part is input into a pre-established and trained temperature calculation model for processing, and the temperature value of the metal part to be measured is obtained. The temperature value of the metal part under test is displayed in real time through the human-machine interface. .

[0016] In the aforementioned method for measuring the temperature of metal parts based on ultrasonic signals, the receiving end of the ultrasonic transducer receives the reflected echo signal; the echo signal includes: a first reflection signal and a second reflection signal, which are generated by the reflection of the sound wave on the surface of the metal part to be tested and by the sound wave passing through the metal part to be tested and being emitted on its reverse side, respectively.

[0017] The aforementioned method for measuring the temperature of metal parts based on ultrasonic signals, wherein the discrete time series... The temperature prediction model, pre-established in the signal processing module, is input and combined with the distance between the ultrasonic transducer and the metal part under test. Calculate relevant parameters; these parameters include: reference speed of sound. Sound attenuation coefficient Nonlinear acoustic parameters and flight time difference ;

[0018] The reference speed of sound The calculations include:

[0019] For discrete time series Perform a short-time Fourier transform and calculate based on the distance between the ultrasonic transducer and the metal part under test. The experimental dispersion curve is obtained by calculating the group velocity of each frequency component, and its expression is:

[0020] ,

[0021] in, For the first Arrival time of each frequency component wave packet For the first Group velocity of each frequency component

[0022] By fitting the experimental dispersion curve to the waveguide model, a reference sound velocity that is only related to the material properties and thickness of the metal part under test is obtained. ;

[0023] The sound attenuation coefficient The calculations include:

[0024] For the transmitted swept frequency band electrical signal and the received and processed discrete time series Perform a fast Fourier transform to obtain and ;

[0025] in, It is the transmitted frequency sweep electrical signal Amplitude spectrum in the frequency domain;

[0026] This indicates the received and digitized echo signal. The amplitude spectrum in the frequency domain represents the amplitude at each frequency point. The above refers to the actual strength of the received signal.

[0027] Based on the transmitted and received signals and The sound attenuation coefficient was calculated. The expression is as follows:

[0028] ;

[0029] Among them, the sound attenuation coefficient The image is a curve that varies with frequency. The average sound attenuation coefficient is obtained by averaging the values ​​over the entire frequency band.

[0030] The nonlinear acoustic parameters The calculations include:

[0031] The received and processed discrete time series Perform a Fourier transform to measure the amplitude of the original frequency components. and second harmonic frequency amplitude ;

[0032] The nonlinear acoustic parameters Approximately The second harmonic frequency This indicates the frequency of the signal generated by the nonlinear response;

[0033] The flight time difference The acquisition includes: calculating the sound waves of the first and second reflections, calculating the time interval between them, and obtaining the time difference of flight. .

[0034] The aforementioned method for measuring the temperature of metal parts based on ultrasonic signals uses a Lamb wave dispersion model to describe the propagation of ultrasonic guided waves in a plate-like structure, expressed as follows: ,in, For theoretically calculated wave speed, For frequency, For the thickness of the metal part to be measured, For the material density of the metal part to be tested, For longitudinal wave velocity, The transverse wave velocity;

[0035] The experimental dispersion curve is fitted to the waveguide model, including:

[0036] Step 1: Based on the experimental dispersion curve, obtain the... Different frequency points Experimental values ​​of group velocity obtained from the following measurements ;in, , The frequency extracted from the time-frequency analysis results of the received signal The arrival time of the wave packet corresponding to the component;

[0037] Step 2: Using the longitudinal wave velocity of the metal part to be tested and transverse wave velocity As the variable to be inverted, its thickness and material density As a known constant, the theoretical group velocity calculation model is obtained by numerically solving the Lamb wave dispersion equation: ;

[0038] Step 3: Construct the target error function

[0039] ;

[0040] Step 4: Iteratively adjust using a nonlinear optimization algorithm. and The optimal P-wave velocity is obtained by minimizing the target error function E when E is less than a preset threshold or when the iteration converges. and transverse wave velocity ;

[0041] Step 5: Select the optimal and Substituting the theoretical phase velocity corresponding to the lowest measurement frequency point, the reference sound velocity, independent of frequency, is calculated. .

[0042] The aforementioned method for measuring the temperature of metal parts based on ultrasonic signals, specifically includes the following steps in the pre-established and trained metal part temperature calculation model:

[0043] Establish a temperature calculation model for metal parts, specifically including:

[0044] Changing the temperature of the metal parts of the sample under test at different temperatures The following repeats the contents of S101 to S103 of the method, wherein the temperature Thermocouple method was used to measure different temperatures. The parameter eigenvectors ;

[0045] in, express Reference speed of sound at temperature express The average sound attenuation coefficient at temperature, express Nonlinear acoustic parameters at temperature express Time difference of flight under temperature, Indicates the number of times the temperature of the metal part of the test sample has been changed;

[0046] Training a temperature calculation model for metal parts, specifically including:

[0047] by Using the input data, a feedforward neural network model is constructed with 4 nodes in the input layer, 10 nodes in the hidden layer, and 1 node in the output layer. As the four nodes of the input layer, As one node in the output layer, the weight coefficients of the temperature calculation model for the metal part under test are obtained through training; by changing the temperature, the reference sound velocity at the corresponding temperature is input. Average sound attenuation coefficient Nonlinear acoustic parameters and flight time difference The weight coefficients are adjusted. If the output prediction error exceeds the preset threshold, the hidden layer nodes are added and the training is repeated until the output accuracy meets the requirements. Then the training is stopped, and the final trained metal part temperature calculation model is obtained.

[0048] The beneficial effects of this invention are as follows: This invention solves the technical problems of existing contact temperature measurement technologies, such as easy damage to the metal parts being measured, response lag, and inability to measure temperature, by providing a method and system for measuring the temperature of metal parts based on ultrasonic signals. Compared with existing non-contact infrared temperature measurement methods, it can effectively reduce the influence of surface condition on the measurement results and greatly improve the accuracy of temperature measurement. Furthermore, by introducing the sound attenuation coefficient and the nonlinearity of sound wave signal transmission, this invention further improves the accuracy of temperature measurement results.

[0049] This invention utilizes the ability of ultrasonic signals to penetrate media, enabling non-contact measurement of rotating or moving parts. It extracts four acoustic parameters (reference sound velocity, average sound attenuation coefficient, nonlinear acoustic parameter, and time-of-flight difference) as feature vectors for temperature to construct a multi-parameter fusion temperature calculation model. Even if a certain acoustic property of the material deviates from its normal state due to microscopic changes, other parameters can compensate and correct for this, enhancing the robustness of the measurement. This invention can establish corresponding "temperature calculation models" for metal parts of different materials and thicknesses. The system can be extended to various types of metal parts without changing the core hardware; only the corresponding software model needs to be trained. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of a metal part temperature measurement system based on ultrasonic signals according to Embodiment 1 of the present invention;

[0051] Figure 2 This is a schematic diagram of a method for measuring the temperature of metal parts based on ultrasonic signals in Embodiment 2 of the present invention. Detailed Implementation

[0052] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0053] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0054] Example 1:

[0055] like Figure 1The embodiment shown provides a temperature measurement system for metal parts based on ultrasonic signals, including: a waveform generator, a power amplifier, an ultrasonic transducer (wideband), a low-noise preamplifier, and a data acquisition card; the power amplifier is connected to the output of the waveform generator and is used to amplify the electrical signal voltage generated by the waveform generator to drive the ultrasonic transducer; the transmitting end of the ultrasonic transducer is connected to the output of the power amplifier; the input of the low-noise preamplifier is connected to the receiving end of the ultrasonic transducer and is used to amplify the reflected echo signal; the data acquisition card is connected to the output of the low-noise preamplifier and is used to convert the preamplified echo signal into a discrete digital signal; the metal part under test is disposed between the transmitting end and the receiving end of the ultrasonic transducer, and is equidistant from both the transmitting end and the receiving end.

[0056] The aforementioned temperature measurement system for metal parts based on ultrasonic signals also includes an embedded processor and a human-machine interface.

[0057] The embedded processor includes a signal processing module and a data storage module. The signal processing module is connected to the data storage module. The signal processing module is used to store and execute signal processing and temperature calculation algorithms, and the data storage module is used to store raw waveform data.

[0058] The human-machine interface includes a display screen and a touch screen, which are electrically connected to a signal processing module for parameter setting, status monitoring, and result display.

[0059] The aforementioned temperature measurement system for metal parts based on ultrasonic signals also includes: a precision mechanical adjustment frame and a laser positioner. The precision mechanical adjustment frame is connected to the ultrasonic transducer and is used to adjust the position and angle of the ultrasonic transducer relative to the metal part to be measured. The laser positioner is used to assist in aligning the measurement point.

[0060] Example 2:

[0061] like Figure 2 The embodiment shown provides a method for measuring the temperature of metal parts based on ultrasonic signals, including:

[0062] S101: Ultrasonic signal generation and transmission: The ultrasonic transducer and laser positioner are aligned with the metal part under test using a precision mechanical adjustment frame, and the distance between the ultrasonic transducer and the metal part under test is measured. The waveform generator produces an electrical signal. And it is emitted to the surface of the metal part to be tested through the transmitting end of the power amplifier and ultrasonic transducer;

[0063] S102: Reflected signal reception and processing: After receiving the reflected echo signal, the ultrasonic transducer amplifies the signal and performs digital processing to obtain a discrete time series. ;

[0064] S103: Parameter calculation of the temperature prediction model: Calculating the discrete time series... The temperature prediction model, pre-established in the signal processing module, is input and combined with the distance between the ultrasonic transducer and the metal part under test. The relevant parameters are calculated to obtain the reference speed of sound. Sound attenuation coefficient Nonlinear acoustic parameters and flight time difference ;

[0065] S104: Establish a temperature calculation model for metal parts;

[0066] S105: Output of temperature calculation results: The obtained reference sound velocity Sound attenuation coefficient Nonlinear acoustic parameters and flight time difference The temperature of the metal part is input into a pre-established and trained temperature calculation model for processing, and the temperature value of the metal part to be measured is obtained. The temperature value of the metal part under test is displayed in real time through the human-machine interface. .

[0067] In the aforementioned method for measuring the temperature of metal parts based on ultrasonic signals, the receiving end of the ultrasonic transducer receives the reflected echo signal; the echo signal includes: a first reflection signal and a second reflection signal, which are generated by the reflection of the sound wave on the surface of the metal part to be tested and by the sound wave passing through the metal part to be tested and being emitted on its reverse side, respectively.

[0068] The aforementioned method for measuring the temperature of metal parts based on ultrasonic signals, wherein the discrete time series... The temperature prediction model, pre-established in the signal processing module, is input and combined with the distance between the ultrasonic transducer and the metal part under test. Calculate relevant parameters; these parameters include: reference speed of sound. Sound attenuation coefficient Nonlinear acoustic parameters and flight time difference ;

[0069] The reference speed of sound The calculations include:

[0070] For discrete time series Perform a short-time Fourier transform and calculate based on the distance between the ultrasonic transducer and the metal part under test. The experimental dispersion curve is obtained by calculating the group velocity of each frequency component, and its expression is:

[0071] ,

[0072] in, For the first Arrival time of each frequency component wave packet For the first Group velocity of each frequency component

[0073] By fitting the experimental dispersion curve to the waveguide model, a reference sound velocity that is only related to the properties and thickness of the metal material under test is obtained. ;

[0074] The sound attenuation coefficient The calculations include:

[0075] For the transmitted swept frequency band electrical signal and the received and processed discrete time series Perform a fast Fourier transform to obtain and ;

[0076] in, It is the transmitted frequency sweep electrical signal Amplitude spectrum in the frequency domain;

[0077] This indicates the received and digitized echo signal. The amplitude spectrum in the frequency domain represents the amplitude at each frequency point. The above refers to the actual strength of the received signal.

[0078] The sound attenuation coefficient The calculation methods include:

[0079] Based on the transmitted and received signals and The sound attenuation coefficient was calculated. The expression is as follows:

[0080] ;

[0081] Among them, the sound attenuation coefficient The image is a curve that varies with frequency. The average sound attenuation coefficient is obtained by averaging the values ​​over the entire frequency band.

[0082] The nonlinear acoustic parameters The calculations include:

[0083] The received and processed discrete time series Perform a Fourier transform to measure the amplitude of the original frequency components. and second harmonic frequency amplitude ;

[0084] The nonlinear acoustic parameters It can be approximated as The second harmonic frequency This ratio represents the frequency of the signal generated by the nonlinear response. As a relative indicator reflecting the nonlinearity of the material, this ratio is closely related to temperature changes.

[0085] The flight time difference The acquisition includes: calculating the sound waves of the first and second reflections, calculating the time interval between them, and obtaining the time difference of flight. .

[0086] The aforementioned method for measuring the temperature of metal parts based on ultrasonic signals uses a Lamb wave dispersion model to describe the propagation of ultrasonic guided waves in a plate-like structure, expressed as follows: ,in, For theoretically calculated wave speed, For frequency, For the thickness of the metal part to be measured, For the material density of the metal part to be tested, For longitudinal wave velocity, The transverse wave velocity;

[0087] The experimental dispersion curve is fitted to the waveguide model, including:

[0088] Step 1: Based on the experimental dispersion curve, obtain the... Different frequency points Experimental values ​​of group velocity obtained from the following measurements ;in, , The frequency extracted from the time-frequency analysis results of the received signal The arrival time of the wave packet corresponding to the component.

[0089] Step 2: Using the longitudinal wave velocity of the metal part to be tested and transverse wave velocity As the variable to be inverted, its thickness and material density As a known constant, the theoretical group velocity calculation model is obtained by numerically solving the Lamb wave dispersion equation: .

[0090] Step 3: Construct the target error function

[0091] .

[0092] Step 4: Iteratively adjust using a nonlinear optimization algorithm. and The optimal P-wave velocity is obtained by minimizing the target error function E when E is less than a preset threshold or when the iteration converges. and transverse wave velocity .

[0093] Step 5: Select the optimal and Substituting the theoretical phase velocity corresponding to the lowest measurement frequency point, the reference sound velocity, independent of frequency, is calculated. .

[0094] The aforementioned method for measuring the temperature of metal parts based on ultrasonic signals, specifically includes the following steps in the pre-established and trained metal part temperature calculation model:

[0095] Establish a temperature calculation model for metal parts, specifically including:

[0096] Changing the temperature of the metal part under test, at different temperatures The following repeats the contents of S101 to S103 of the method, wherein the temperature Thermocouple method was used to measure different temperatures. The parameter eigenvectors ;

[0097] in, express Reference speed of sound at temperature express The average sound attenuation coefficient at temperature, express Nonlinear acoustic parameters at temperature express Time difference of flight under temperature, Indicates the number of times the temperature of the metal part being tested has been changed;

[0098] Training a temperature calculation model for metal parts, specifically including:

[0099] by Using the input data, a feedforward neural network model is constructed with 4 nodes in the input layer, 10 nodes in the hidden layer, and 1 node in the output layer. As the four nodes of the input layer, As one node in the output layer, the weight coefficients of the metal part temperature calculation model are obtained through training; by changing the temperature, the reference sound velocity at the corresponding temperature is input. Average sound attenuation coefficient Nonlinear acoustic parameters and flight time difference The weight coefficients are adjusted. If the output prediction error exceeds the preset threshold, the hidden layer nodes are added and the training is repeated until the output accuracy meets the requirements. Then the training is stopped, and the final trained metal part temperature calculation model is obtained.

[0100] Example 3: This example uses an aero-engine turbine blade as the test object, achieving non-contact measurement of its surface temperature on a ground test bench. Since the turbine blade is rotating, traditional contact temperature measurement methods are difficult to apply. The specific implementation steps are as follows:

[0101] Step 1: Assisted positioning and distance measurement

[0102] The position and angle of the ultrasonic transducer are adjusted using the precision mechanical adjustment frame of the auxiliary positioning unit, and the laser positioner is activated to precisely align its light spot with the middle of the blade under test. A laser rangefinder is then used to accurately measure the distance L=100mm between the ultrasonic transducer's emitting surface and the surface of the blade being tested.

[0103] Step 2: Frequency sweep signal transmission

[0104] The arbitrary waveform generator in the ultrasonic transmitting unit generates a swept frequency electrical signal. Its center frequency is 5MHz, bandwidth is 2-8MHz, and duration is Tp=10μs. After being amplified by a power amplifier, the signal drives the ultrasonic transducer to emit ultrasonic waves onto the surface of the turbine blade.

[0105] Step 3: Echo signal reception and digitization

[0106] An ultrasonic transducer receiver receives acoustic wave signals reflected from the blades. As described in the invention, the signal includes:

[0107] The first reflected signal is generated by the direct reflection of sound waves on the blade surface.

[0108] The second reflected signal is generated by sound waves penetrating the blade surface, propagating inside it, and reflecting back after being reflected off the blade's reverse side.

[0109] The received weak analog echo signal is first amplified by a low-noise preamplifier (gain set to 40 dB), and then digitized by a data acquisition card at a sampling rate of 200 MS / s to obtain a discrete-time series. .

[0110] Step 4: Calculation of multimodal acoustic characteristic parameters

[0111] Discrete time series The temperature prediction model, which can be a multimodal fusion LSTM model, is input into the signal processing and control unit. The following four key parameters are then calculated:

[0112] 4.1 Reference speed of sound Calculation

[0113] (1) Obtain experimental values ​​of group velocity

[0114] For the received discrete time series Perform a short-time Fourier transform to obtain the time spectrum R(f, t) of the signal. Select M=61 frequency points uniformly within the sweep bandwidth (2-8 MHz). For each frequency point Extract the arrival time of the wave packet energy of this frequency component from the time spectrum. Given that the total sound path L = 100 mm, according to the formula... The experimental group velocity values ​​corresponding to 61 frequency points were calculated, thus forming the experimental dispersion curve. .

[0115] (2) Establish a theoretical group velocity model

[0116] The longitudinal wave velocity CL and transverse wave velocity CT of the turbine blade (made of nickel-based superalloy) are used as the variables to be inverted. The blade thickness h = 2 mm and the material density are known. Theoretical group velocity calculation model It is obtained by numerically solving the Lamb wave dispersion equation.

[0117] (3) Construct and optimize the objective function

[0118] Construct the target error function:

[0119] ;

[0120] The Levenberg-Marquardt nonlinear least squares optimization algorithm was employed, using typical values ​​at room temperature (CL≈6000 m / s, CT≈3200 m / s) as initial values. CL and CT were iteratively adjusted to minimize the objective function E. A convergence threshold of 1×10⁻⁻⁻⁶ was set. 6 After the optimization process converges, the optimal solution is obtained: , .

[0121] (4) Calculate the reference speed of sound

[0122] The optimal parameters obtained through optimization and Substituting into the Lamb wave dispersion equation, the calculation is performed at the lowest measurement frequency. The theoretical phase velocity at this frequency is calculated. In the low-frequency range, the phase velocity of the zero-order symmetric mode tends to stabilize. The calculated theoretical phase velocity at this frequency is 5200 m / s, and this value is used as a frequency-independent reference sound velocity. This is used for constructing subsequent temperature feature vectors.

[0123] 4.2 Sound Attenuation Coefficient Calculation

[0124] The transmitted sweep frequency electrical signals were respectively... and received discrete time series Perform a fast Fourier transform to obtain their amplitude spectra. and .

[0125] The sound attenuation coefficient is calculated according to the formula given in the invention:

[0126] ;

[0127] Sound attenuation coefficient It is a curve that varies with frequency. The average value is calculated over the entire swept frequency band (2-8 MHz), and the average acoustic attenuation coefficient a = 85 dB / m is obtained for temperature calculation.

[0128] 4.3 Nonlinear acoustic parameters Calculation

[0129] For the received discrete time series Perform high-precision Fourier transform to measure the fundamental frequency in the spectrum. amplitude and second harmonic frequency amplitude .

[0130] According to the invention, the nonlinear acoustic parameters are approximately: .

[0131] Substituting the measured values, we get: .

[0132] 4.4 Flight Time Difference Acquisition

[0133] In time domain signal In the process, the first reflected signal (from the blade surface) and the second reflected signal (from the underside of the blade) were identified.

[0134] The time difference of flight is obtained by calculating the interval between the arrival times of the two main echo packets. .

[0135] Thus, the acoustic feature vector in the current state has been constructed. .

[0136] Step 5: Neural Network Temperature Calculation

[0137] The feature vector obtained in step 4 Input into a pre-built and trained temperature calculation model for metal parts.

[0138] Model Establishment and Training: This model was obtained through calibration on blade specimens of the same material in the laboratory. Temperature was precisely measured using thermocouples in a controlled high-temperature furnace. And at this temperature, repeat steps 1 to 4 to collect a large amount of data. This constitutes the training dataset.

[0139] Model Structure: As described in the invention, the model consists of an input layer with 4 nodes (corresponding to...) ), 10 nodes in the hidden layer, and 1 node in the output layer (corresponding to temperature). (A feedforward neural network.)

[0140] The trained model is embedded into the embedded processor. The system calls the model to... The calculations are performed, and the final output is an estimated value of the current turbine blade temperature. .

[0141] Step 6: Temperature Display

[0142] The human-machine interface (such as the monitoring screen of an industrial computer) displays the measured turbine blade temperature value of 925°C in real time. This data can also be transmitted to a host computer via the system interface for recording or for closed-loop control.

[0143] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A temperature measurement system for metal parts based on ultrasonic signals, characterized in that, include: The system includes a waveform generator, a power amplifier, an ultrasonic transducer, a low-noise preamplifier, and a data acquisition card. The power amplifier is connected to the output of the waveform generator and is used to amplify the electrical signal voltage generated by the waveform generator to drive the ultrasonic transducer. The transmitting end of the ultrasonic transducer is connected to the output end of the power amplifier; the input end of the low-noise preamplifier is connected to the receiving end of the ultrasonic transducer to amplify the reflected echo signal; the metal part under test is arranged at equal distances between the transmitting end and the receiving end of the ultrasonic transducer; the data acquisition card is connected to the output end of the low-noise preamplifier to convert the preamplified echo signal into a discrete digital signal.

2. The temperature measurement system for metal parts based on ultrasonic signals according to claim 1, characterized in that, The system also includes an embedded processor and a human-machine interface; The embedded processor includes a signal processing module and a data storage module. The input terminal of the signal processing module receives the digital signal, and the output terminal of the signal processing module is connected to the input terminal of the data storage module. The signal processing module is used to store and execute signal processing and temperature calculation algorithms, and the data storage module is used to store raw waveform data. The human-machine interface includes a display screen and a touch screen, which are electrically connected to a signal processing module for parameter setting, status monitoring, and result display.

3. The temperature measurement system for metal parts based on ultrasonic signals according to claim 1, characterized in that, It also includes a precision mechanical adjustment frame and a laser positioner. The precision mechanical adjustment frame is connected to the ultrasonic transducer and is used to adjust the position and angle of the ultrasonic transducer relative to the metal part to be tested. The laser positioner is used to assist the transmitting end of the ultrasonic transducer in aligning with the measurement point of the metal part to be tested.

4. A method for measuring the temperature of metal parts based on ultrasonic signals, characterized in that, include: Step 1: Align the ultrasonic transducer and laser positioner with the metal part to be tested using a precision mechanical adjustment frame, and measure the distance between the ultrasonic transducer and the metal part to be tested. ; Step 2: The waveform generator produces an electrical signal. And it is emitted to the surface of the metal part to be tested through the transmitting end of the power amplifier and ultrasonic transducer; Step 3: After receiving the reflected echo signal, the ultrasonic transducer amplifies the signal and performs digital processing to obtain a discrete-time sequence. ; Step 4: Convert the discrete time series The temperature prediction model, pre-established in the signal processing module, is input and combined with the distance between the ultrasonic transducer and the metal part under test. The relevant parameters are calculated to obtain the reference speed of sound. Sound attenuation coefficient Nonlinear acoustic parameters and flight time difference ; Step 5: Obtain the reference speed of sound Sound attenuation coefficient Nonlinear acoustic parameters and flight time difference The temperature of the metal part is input into a pre-established and trained temperature calculation model for processing, and the temperature value of the metal part to be measured is obtained. The temperature value of the metal part under test is displayed in real time through the human-machine interface. .

5. The method for measuring the temperature of metal parts based on ultrasonic signals according to claim 4, characterized in that, The receiving end of the ultrasonic transducer receives the reflected echo signal; the echo signal includes: a first reflection signal and a second reflection signal, which are generated by the reflection of the sound wave on the surface of the metal part under test and the sound wave passing through the metal part under test and being emitted on its reverse side, respectively.

6. The method for measuring the temperature of metal parts based on ultrasonic signals according to claim 4, characterized in that, Discrete time series The temperature prediction model, pre-established in the signal processing module, is input and combined with the distance between the ultrasonic transducer and the metal part under test. The calculation of relevant parameters includes: The parameters include: reference speed of sound Sound attenuation coefficient Nonlinear acoustic parameters and flight time difference ; The reference speed of sound The calculations include: For discrete time series Perform a short-time Fourier transform and calculate based on the distance between the ultrasonic transducer and the metal part under test. The experimental dispersion curve is obtained by calculating the group velocity of each frequency component, and its expression is: ， in, For the first Arrival time of each frequency component wave packet For the first Group velocity of each frequency component By fitting the experimental dispersion curve to the waveguide model, a reference sound velocity that is only related to the material properties and thickness of the metal part under test is obtained. ; The sound attenuation coefficient The calculations include: For the transmitted swept frequency band electrical signal and the received and processed discrete time series Perform a fast Fourier transform to obtain and ; in, It is the transmitted frequency sweep electrical signal Amplitude spectrum in the frequency domain; This indicates the received and digitized echo signal. The amplitude spectrum in the frequency domain represents the amplitude at each frequency point. The above refers to the actual strength of the received signal. Based on the transmitted and received signals and The sound attenuation coefficient was calculated. The expression is as follows: ； Among them, the sound attenuation coefficient The image is a curve that varies with frequency. The average sound attenuation coefficient is obtained by averaging the values ​​over the entire frequency band. The nonlinear acoustic parameters The calculations include: The received and processed discrete time series Perform a Fourier transform to measure the amplitude of the original frequency components. and second harmonic frequency amplitude ; The nonlinear acoustic parameters Approximately The second harmonic frequency This indicates the frequency of the signal generated by the nonlinear response; The flight time difference The acquisition includes: calculating the sound waves of the first and second reflections, calculating the time interval between them, and obtaining the time difference of flight. .

7. The method for measuring the temperature of metal parts based on ultrasonic signals according to claim 6, characterized in that, The waveguide model described is a Lamb wave dispersion model that describes the propagation of ultrasonic guided waves in a plate-like structure, expressed as follows: ,in, For theoretically calculated wave speed, For frequency, For the thickness of the metal part to be measured, For the material density of the metal part to be tested, For longitudinal wave velocity, The transverse wave velocity; The experimental dispersion curve is fitted to the waveguide model, including: Step 1: Based on the experimental dispersion curve, obtain the... Different frequency points Experimental values ​​of group velocity obtained from the following measurements ;in, , The frequency extracted from the time-frequency analysis results of the received signal The arrival time of the wave packet corresponding to the component; Step 2: Using the longitudinal wave velocity of the metal part to be tested and transverse wave velocity As the variable to be inverted, its thickness and material density As a known constant, the theoretical group velocity calculation model is obtained by numerically solving the Lamb wave dispersion equation: ; Step 3: Construct the target error function ； Step 4: Iteratively adjust using a nonlinear optimization algorithm. and The optimal P-wave velocity is obtained by minimizing the target error function E when E is less than a preset threshold or when the iteration converges. and transverse wave velocity ; Step 5: Select the optimal and Substituting the theoretical phase velocity corresponding to the lowest measurement frequency point, the reference sound velocity, independent of frequency, is calculated. .

8. The method for measuring the temperature of metal parts based on ultrasonic signals according to claim 4, characterized in that, The pre-established and trained temperature calculation model for metal parts specifically includes the following steps. Establish a temperature calculation model for metal parts, specifically including: Changing the temperature of the metal parts of the sample under test at different temperatures The following repeats steps 1 to 3 of the method, where the temperature... Thermocouple method was used to measure different temperatures. The parameter eigenvectors ; in, express Reference speed of sound at temperature express The average sound attenuation coefficient at temperature, express Nonlinear acoustic parameters at temperature express Time difference of flight under temperature, Indicates the number of times the temperature of the metal part of the test sample has been changed; Training a temperature calculation model for metal parts, specifically including: by Using the input data, a feedforward neural network model is constructed with 4 nodes in the input layer, 10 nodes in the hidden layer, and 1 node in the output layer. As the four nodes of the input layer, As one node in the output layer, the weight coefficients of the temperature calculation model for the metal part under test are obtained through training; by changing the temperature, the reference sound velocity at the corresponding temperature is input. Average sound attenuation coefficient Nonlinear acoustic parameters and flight time difference The weight coefficients are adjusted. If the output prediction error exceeds the preset threshold, the hidden layer nodes are added and the training is repeated until the output accuracy meets the requirements. Then the training is stopped, and the final trained metal part temperature calculation model is obtained.

Images