Acoustic temperature sensor and temperature measurement method

Abstract

An acoustic temperature sensor and a temperature measurement method, comprising a microphone, an acoustic wave guide, a power supply and a signal processing module. The microphone is used to detect acoustic waves; the first port of the acoustic wave guide is blocked by the microphone and constitutes an acoustic wave Fabry-Perot resonator with the second port, the first reflecting surface of the acoustic wave Fabry-Perot resonator is the sound receiving surface of the microphone, and the second reflecting surface is the interface between the inside and outside air of the second port; the background noise of the measured environment is transmitted into the acoustic wave guide from the second port, is modulated by the acoustic wave Fabry-Perot resonator, and the modulated background noise signal is converted into an electric signal by the microphone and then output; the power supply and the signal processing module are used to power the microphone and process the electric signal output by the microphone to obtain the temperature of the measured environment. The present application has the advantages of simple sensor structure, convenient use, wide temperature measurement range, low power consumption, fast response, high precision, and is not affected by air pressure.

Description

Technical Field

[0001] This invention relates to the fields of environmental noise detection technology and acoustic temperature measurement technology, and in particular to an acoustic temperature sensor and temperature measurement method based on an acoustic Fabry-Perot resonator. Background Technology

[0002] Acoustic thermometry is a non-contact, indirect temperature measurement technique based on the temperature dependence of sound velocity. It obtains the ambient gas temperature by measuring the speed of sound and offers advantages such as fast response, high accuracy, robustness, and a wide temperature measurement range. It is suitable for measuring temperatures in complex and harsh environments, such as melting furnaces, plasma chambers, and nuclear reactors. According to literature reports, existing acoustic thermometry techniques mainly include two types: one measures the time required for a sound pulse emitted from a sound source to reach a sound detector at a given distance, then calculates the speed of sound and uses the temperature dependence of the speed of sound to determine the ambient temperature; the other places the sound source and sound detector in an acoustic resonator to detect the resonant frequency, then uses the correlation between the resonant frequency and the speed of sound to determine the speed of sound, and thus obtains the ambient temperature. Both methods require a known sound source, consume a lot of power, have complex measurement devices, demanding signal control conditions, and are difficult to operate, making them inconvenient for temperature measurement anytime and anywhere. Summary of the Invention

[0003] In view of this, the main objective of the present invention is to provide an acoustic temperature sensor and a temperature measurement method, so as to at least partially solve at least one of the aforementioned technical problems.

[0004] According to one aspect of the present invention, an acoustic temperature sensor is provided, comprising: a microphone for detecting sound waves, the microphone having a sound-sensing surface; an acoustic waveguide having a first port and a second port with flush port surfaces, the first port being blocked by the microphone and forming an acoustic Fabry-Perot resonator with the second port, the first reflecting surface of the acoustic Fabry-Perot resonator being the sound-sensing surface of the microphone, and the second reflecting surface being the interface between the inner and outer air of the second port; wherein background noise of the measured environment is transmitted from the second port into the acoustic waveguide and modulated by the acoustic Fabry-Perot resonator, the modulated background noise signal being converted into an electrical signal by the microphone and output; and a power supply and signal processing module electrically connected to the microphone for supplying power to the microphone and processing the electrical signal output by the microphone to obtain the temperature of the measured environment.

[0005] According to another aspect of the present invention, a method for measuring temperature using an acoustic temperature sensor as described above is provided, comprising the following steps: Step A: placing an acoustic Fabry-Perot resonator in the environment under test, and powering the acoustic Fabry-Perot resonator by a power supply and signal processing module; Step B: continuously probing the background noise of the environment under test multiple times using the acoustic Fabry-Perot resonator, and outputting multiple background noise time-domain spectra; Step C: processing the multiple background noise time-domain spectra using the power supply and signal processing module to obtain a background noise spectrum, wherein the processing includes spectrum conversion and spectrum superposition; Step D: determining the frequencies and orders corresponding to multiple resonance peaks from the background noise spectrum using the power supply and signal processing module; Step E: solving for the temperature of the environment under test based on the linear regression slope between the frequencies and orders of the multiple resonance peaks.

[0006] As can be seen from the above technical solutions, the acoustic temperature sensor and temperature measurement method of the present invention have at least one or a part of the following beneficial effects compared with the prior art:

[0007] (1) This invention utilizes the characteristic that the power spectral density distribution of the background noise of the measured environment is relatively uniform in a wide frequency range, and based on the resonance phenomenon generated by the background noise in the acoustic Fabry-Perot resonator, the relationship between the resonant frequency and the temperature can be determined. Thus, temperature measurement can be achieved without knowing the sound source. It has a simple structure, low power consumption, fast response, low cost, and high sensitivity.

[0008] (2) The acoustic temperature sensor of the present invention can measure temperature based solely on the acoustic waveguide and the microphone sealed in the acoustic waveguide. It has the advantages of small size, light weight, easy portability and convenient operation, and can measure temperature anytime and anywhere.

[0009] (3) Compared with existing temperature measurement methods that require calibration before use, such as mercury thermometers that require temperature and scale calibration, the acoustic Fabry-Perot resonator of this invention does not require this calibration process. That is, it does not require the prior preparation of frequency-temperature standard curves or slope-temperature standard curves to achieve temperature measurement with high accuracy.

[0010] (4) The acoustic Fabry-Perot resonator of the present invention has multiple functions. It can not only measure temperature, but also monitor noise and measure sound speed. It is not affected by air pressure and is suitable for temperature measurement in harsh environments such as near space. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of the acoustic temperature sensor based on a Fabry-Perot resonator according to the first embodiment of the present invention.

[0012] Figure 2This is a flowchart of a method for measuring temperature using an acoustic temperature sensor based on an acoustic Fabry-Perot resonator, according to the first embodiment of the present invention.

[0013] Figure 3 The simulation results of the first four resonant frequencies of the second embodiment of the present invention as a function of temperature, based on the acoustic Fabry-Perot resonator structure of the first embodiment.

[0014] Figure 4 The third embodiment of the present invention uses an acoustic Fabry-Perot resonator prepared in the laboratory to measure the ambient background noise spectrum;

[0015] Figure 5 This is a schematic diagram of the apparatus used in the fourth embodiment of the present invention to test the changes of the resonant frequencies of a laboratory-prepared acoustic Fabry-Perot resonator with temperature in a temperature-controlled chamber environment.

[0016] Figure 6 The fourth embodiment of the present invention shows the curves of the changes in the resonant frequencies of each order of the acoustic Fabry-Perot resonator, the test temperature, and the test error as a function of ambient temperature, measured in a temperature-controlled chamber environment.

[0017] Figure 7 This is a curve showing the change of resonant frequency and test temperature under different ambient temperatures, measured in a temperature-controlled chamber environment according to the fourth embodiment of the present invention.

[0018] Figure 8 The fifth embodiment of the present invention shows the background noise spectrum and the linear variation curve of the resonant frequency with order, measured in a laboratory-prepared acoustic Fabry-Perot resonator in a fully anechoic chamber.

[0019] In the above figures, the meanings of the reference numerals are as follows:

[0020] 1-Microphone; 2-Audio waveguide;

[0021] 10 - Acoustic Fabry-Perot resonator; 20 - Preamplifier;

[0022] 30 - Reference microphone; 40 - Temperature control box;

[0023] 50 - Data Acquisition Card. Detailed Implementation

[0024] This invention discloses an acoustic temperature sensor and a temperature measurement method. The acoustic temperature sensor includes a microphone, an acoustic waveguide, and a power supply and signal processing module. The microphone and acoustic waveguide constitute an acoustic Fabry-Perot resonator. This resonator modulates and detects background noise in the measured environment. The power supply and signal processing module processes the measured modulated background noise signal, and the temperature of the measured environment can be measured based on the correspondence between the resonant peak frequency and temperature in the spectrum of the processed background noise signal. This invention's temperature sensor has a simple structure, is easy to use, is non-contact, has a wide temperature measurement range, low power consumption, fast response, high accuracy, and is unaffected by air pressure, making it suitable for temperature measurement in harsh environments such as near-space.

[0025] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be noted that similar or identical parts are referred to by the same reference numerals in the drawings or description. Implementations not shown or described in the drawings are forms known to those skilled in the art. Furthermore, although this document provides examples of parameters containing specific values, it should be understood that the parameters need not be exactly equal to the corresponding values, but can approximate the corresponding values ​​within acceptable error tolerances or design constraints. Directional terms mentioned in the embodiments, such as "up," "down," "front," "back," "left," and "right," are only for reference to the directions in the drawings. Therefore, the directional terms used are for illustrative purposes and not for limiting the scope of protection of this invention.

[0026] First Embodiment

[0027] In a first exemplary embodiment of the present invention, an acoustic temperature sensor based on an acoustic Fabry-Perot resonator is provided. Figure 1 This is a schematic diagram of the structure of a temperature sensor based on an acoustic Fabry-Perot resonator according to the first embodiment of the present invention. Figure 1As shown, the acoustic temperature sensor includes: a microphone 1, an acoustic waveguide 2, and a power supply and signal processing module (not shown in the figure). The microphone 1 is used to detect sound waves and has an acoustic sensing surface. The acoustic waveguide 2 has a first port and a second port with flush port surfaces. The first port is blocked by the microphone 1 and forms an acoustic Fabry-Perot resonator with the second port. The first reflecting surface of the acoustic Fabry-Perot resonator is the acoustic sensing surface of the microphone 1, and the second reflecting surface is the interface between the inner and outer air of the second port. The background noise of the measured environment is transmitted into the acoustic waveguide 2 through the second port and modulated by the acoustic Fabry-Perot resonator. The modulated background noise signal is converted into an electrical signal by the microphone 1 and output. The power supply and signal processing module is electrically connected to the microphone 1 and is used to supply power to the microphone 1 and process the electrical signal output by the microphone 1 to obtain the temperature of the measured environment.

[0028] It should be noted beforehand that the background noise in this invention can be understood as white noise or background noise in the surrounding environment when no specific sound source is present, and the power spectral density of which is relatively uniformly distributed over a wide frequency range.

[0029] The principle behind the acoustic temperature sensor based on the acoustic Fabry-Perot resonator of this invention for temperature measurement is as follows:

[0030] The diaphragm (i.e., the acoustic surface) of microphone 1 forms a reflective surface with a reflectivity of R1 in the acoustic Fabry-Perot resonator, while the other end of the acoustic waveguide 2 is connected to the outside air to accelerate heat exchange. Although the acoustic waveguide 2 is connected to the outside air, due to the acoustic impedance mismatch between the inner diameter of the duct and the near-infinite space outside, the other end of the acoustic waveguide 2 can be equivalent to another air reflective surface with a reflectivity of R2 in the acoustic Fabry-Perot resonator. Sound waves entering the acoustic waveguide from the outside are reflected when they reach the diaphragm of microphone 1. The reflected sound waves return to the outlet and are reflected again by the air reflective surface, with an additional phase shift of π. Thus, the sound waves repeatedly reflect and superimpose within the acoustic Fabry-Perot resonator, forming interference. The phase difference between adjacent reflected waves can be expressed as: Where L is the length of the acoustic waveguide between the two reflecting surfaces, and k is the propagation constant of the sound wave inside the waveguide, which can be expressed as k = 2πf / c, where f is the sound wave frequency and c is the speed of sound. Considering that sound wave propagation inside the acoustic waveguide will cause transmission loss, the propagation constant k is expressed as: k = 2πf / c - jα, where α is the loss factor, which is proportional to the square of the sound wave frequency. Therefore, when a sound wave with a sound pressure amplitude of P0 is incident inside the acoustic waveguide, the sound pressure experienced by the microphone's sensing surface can be expressed as:

[0031]

[0032] The sound pressure amplitude of formula (I) can be expressed as:

[0033]

[0034] From formula (II), it can be seen that when the incident sound frequency satisfies the relationship shown in formula (III):

[0035]

[0036] When the sound pressure amplitude is at its extreme value, the Fabry-Perot resonator is in a resonant state. At this time, f m This is the frequency corresponding to the m-th resonant peak, also known as the m-th resonant frequency. It can be understood that the first resonant peak is the first resonant peak appearing in the background noise spectrum from low to high frequencies, and so on.

[0037] Furthermore, according to fundamental acoustic theory, the relationship between sound speed and temperature satisfies the equation shown in formula (IV):

[0038]

[0039] Combining equations (III) and (IV), we can obtain the relationship between the resonant frequency of the acoustic Fabry-Perot resonator and temperature:

[0040]

[0041] Where B is a constant, and for airborne sound, B = 20.05. Therefore, using the relationship between the resonant frequency and temperature, and based on the sound wave spectrum measured by placing the Fabry-Perot resonator in the environment under test, the temperature of the environment can be measured.

[0042] Furthermore, from the above formula (1), it can be seen that at a given temperature, the resonant frequency f of the acoustic Fabry-Perot resonator is... m The slope of the peak is linearly related to the order m of the corresponding resonance peak, and can be expressed as follows:

[0043]

[0044] As can be seen from formula (2), the slope SL is proportional to the square root of the temperature T. Therefore, during the test, the resonant frequencies of different order resonance peaks are first determined, and then the temperature to be measured is obtained by using the linear regression slope between the resonant frequency and the resonant order. It should be noted that the temperature T in the above formula is the Kelvin temperature.

[0045] The following sections will provide a detailed description of each component of the acoustic temperature sensor based on the acoustic Fabry-Perot resonator in this embodiment.

[0046] like Figure 1As shown, microphone 1 is a half-inch electret condenser microphone (ECM), but it is not limited to this. In other embodiments, microphone 1 can also be a piezoelectric microphone, electromagnetic microphone, fiber optic microphone, grating microphone, or MEMS microphone. In this embodiment, the lower limit of the microphone's frequency response is no greater than 50Hz, thereby satisfying the measurement of environmental background noise in the low-frequency range.

[0047] In this embodiment, the acoustic waveguide 2 is a stainless steel cylindrical acoustic waveguide with a length L = 0.38m and an inner diameter d = 25mm, but it is not limited to this. In other embodiments, the acoustic waveguide 2 can be of other sizes; the acoustic waveguide 2 can be made of other materials such as metal, ceramic, hardwood, glass, or PVC. The shape of the acoustic waveguide 2 is also not limited. Figure 1 The straight shape shown can also be curved or a combination of straight and curved shapes. The specific shape of the acoustic waveguide 2 does not affect the resonant interference effect generated by the acoustic wave in the acoustic Fabry-Perot resonator.

[0048] In this embodiment, the acoustic waveguide 2 has a circular cross-sectional shape to match the microphone 1, so that the microphone 1 is blocked at the first port of the acoustic waveguide 2. The two are tightly assembled to form a sealed structure, thereby avoiding acoustic crosstalk and improving measurement accuracy. However, it is not limited to this. In other embodiments, the cross-sectional shape of the acoustic waveguide 2 can also be other regular or irregular shapes such as squares or triangles, as long as its cross-sectional shape is compatible with the microphone 1. Furthermore, in other embodiments, the microphone 1 is a MEMS microphone, and the acoustic waveguide 2 is a miniature acoustic waveguide compatible with the MEMS microphone. The acoustic temperature sensor constructed in this way can become a highly integrated on-chip acoustic thermometer.

[0049] In this embodiment, the axes at the first and second ports of the acoustic waveguide 2 are perpendicular to the first and second reflecting surfaces of the acoustic Fabry-Perot resonator, thereby facilitating the formation of an acoustic Fabry-Perot resonator at the microphone 1 at the first port and the second port.

[0050] In this embodiment, the quality factor of the resonant peak of the acoustic Fabry-Perot resonator is not less than 2, which is more conducive to accurately determining the resonant frequency, thereby facilitating more accurate temperature measurement. Here, the quality factor (Q) = resonant frequency / bandwidth.

[0051] In this embodiment, the signal-to-noise ratio of the output signal of the acoustic Fabry-Perot resonator is not less than 5. It can be understood that a higher signal-to-noise ratio is beneficial for accurately determining the position and frequency of the resonance peak.

[0052] In this embodiment, the power supply and signal processing module can be implemented using hardware comprising several different components and a suitably programmed computer. The various component embodiments can be implemented in hardware, or as conventional software modules running on one or more processors, or a combination thereof. For example, it may include a preamplifier for amplifying the output signal of an acoustic Fabry-Perot resonator; a data acquisition card for acquiring and converting the analog output signal of the preamplifier; and a computer with LabVIEW software for processing the output signal of the data acquisition card, etc. Those skilled in the art should understand that existing programming methods or code can be used for specific programming, only adjusting the controlled objects and parameters; this invention does not involve program improvements.

[0053] In a first exemplary embodiment of the present invention, a method for measuring temperature using the acoustic temperature sensor based on the acoustic Fabry-Perot resonator described above is also provided. Figure 2 This is a flowchart illustrating a method for measuring temperature using an acoustic temperature sensor based on a Fabry-Perot resonator, according to the first embodiment of the present invention. Figure 2 As shown, the method includes the following steps:

[0054] Step A: Place the acoustic Fabry-Perot resonator in the environment under test, and power it with the power supply and signal processing module.

[0055] Step B: Use an acoustic Fabry-Perot resonator to continuously detect the background noise in the environment under test multiple times, and output multiple background noise time-domain spectra.

[0056] Optionally, in step B, the background noise in the tested environment is continuously detected at least three times using an acoustic Fabry-Perot resonator to improve the signal-to-noise ratio of the subsequently obtained background noise spectrum. The number of detections in this step is the number of times the subsequent background noise spectrum is superimposed.

[0057] Step C: Use the power supply and signal processing module to process multiple background noise time-domain spectra to obtain the background noise spectrum. The processing includes spectrum conversion and spectrum superposition.

[0058] Optionally, in step C, the spectrum conversion can be achieved, for example, by using Fourier transform to convert the time-domain spectrum to the frequency spectrum. By performing spectrum conversion on the time-domain spectra of multiple background noises, multiple background noise spectra are obtained; then, the multiple background noise spectra are superimposed to obtain a background noise spectrum with a high signal-to-noise ratio. The signal-to-noise ratio of the superimposed background noise spectrum is preferably not less than 20.

[0059] In this embodiment, the method may further include: using a reference microphone to detect background noise in the tested environment to obtain a reference background noise time-domain spectrum; and using a power supply and signal processing module to perform spectral conversion on the reference background noise time-domain spectrum to obtain a reference background noise spectrum. Optionally, the reference microphone may be the microphone itself extracted from the acoustic Fabry-Perot resonator. In this case, the reference microphone detection step and the acoustic Fabry-Perot resonator detection step (i.e., step B) are performed at different times. The reference microphone may also be set up separately. In the case of separate setting, the aforementioned two detection steps can be performed synchronously to improve the accuracy and reliability of the differential operation results.

[0060] Based on this, step C further includes: performing a difference operation between the obtained background noise spectrum and the reference background noise spectrum to obtain the background noise spectrum after the difference operation. Subsequent processing steps are then performed based on this background noise spectrum after the difference operation.

[0061] Step D: Use the power supply and signal processing module to determine the frequencies and orders corresponding to multiple resonant peaks from the background noise spectrum;

[0062] Step E: The temperature of the measured environment is determined based on the linear regression slope between the frequencies and orders of multiple resonance peaks. In this step E, formula (2) from above can also be used to determine the temperature based on the resonant frequency f. m The temperature T of the measured environment is determined by the slope SL of the linear curve corresponding to the order m of the resonance peak.

[0063]

[0064] It is understood that the specific implementation of step E may include: sub-step E1: plotting a linear curve in the background noise spectrum between the frequencies and orders corresponding to multiple resonant peaks; sub-step E2: using formula (2) to solve for the temperature of the measured environment based on the slope of the linear curve. As can be seen from the following embodiments, this temperature determination method can accurately measure the temperature of the measured environment and has good robustness.

[0065] In other embodiments, instead of using the slope to solve for the temperature in step E, step F can be used: directly using formula (1) above to determine the frequency f of the resonance peak. m Determine the temperature T of the environment being measured, but the accuracy is not as good as the solution method in step E.

[0066]

[0067] Among them, f mLet be the frequency corresponding to the m-th resonant peak, L be the length of the acoustic waveguide between the first and second reflecting surfaces, and B be a constant, which is 20.05 for airborne sound.

[0068] Optionally, if the background noise spectrum contains multiple resonance peaks, a specific implementation of step F may include:

[0069] Sub-step F1: Substitute the frequency corresponding to each resonance peak in the background noise spectrum into formula (1) to obtain the corresponding temperature value; Sub-step F2: Take the average of the multiple temperature values ​​obtained as the temperature of the measured environment.

[0070] Optionally, if the background noise spectrum contains multiple resonant peaks, another specific implementation of step F may include:

[0071] Sub-step F1': Select multiple resonance peaks with similar quality factors and signal-to-noise ratios from the background noise spectrum; Sub-step F2': Substitute the frequency corresponding to each selected resonance peak into formula (1) to obtain the corresponding temperature value; Sub-step F3': Take the average of the multiple temperature values ​​obtained as the temperature of the measured environment.

[0072] This concludes the description of the acoustic temperature sensor and temperature measurement method based on the acoustic Fabry-Perot resonator in the first embodiment of the present invention.

[0073] Second Embodiment

[0074] In a second exemplary embodiment of the present invention, the acoustic temperature sensor based on the acoustic Fabry-Perot resonator in the first embodiment is used to conduct a simulation test in a temperature-changing environment to verify that the response characteristics of the acoustic temperature sensor to temperature changes in the environment meet the measurement requirements.

[0075] Figure 3 This is a simulation result of the first four resonant frequencies of the acoustic Fabry-Perot resonator structure in the second embodiment of the present invention as a function of temperature. Figure (a) shows the numerical simulation based on the above formula (1). It can be observed that within the temperature range of -50℃ to 750℃, the first four resonant peaks exhibit a monotonic nonlinear frequency shift with increasing temperature. At the same temperature, the higher the order of the resonant frequency, the higher the sensitivity. For the same resonant peak, the rate of change of the resonant frequency with temperature gradually decreases with increasing temperature. Therefore, it can be predicted that this system is more suitable for temperature measurement in the low-temperature range.

[0076] In many cases, the nonlinearity of a system complicates backend signal processing, which is detrimental to temperature sensing. Therefore, many sensing applications require systems to have linear response characteristics. Any curve can be approximated by countless straight lines. Therefore, when the selected temperature measurement range is small, the system's operating curve can be approximated as linear.

[0077] Therefore, this embodiment further simulates the response of the acoustic temperature sensor within a temperature range of -20℃ to 60℃. For example... Figure 3 As shown in Figure (b), the first four resonant frequencies maintain a good linear relationship with temperature. The R² of the linear fitting lines is greater than 0.999, indicating that the system possesses high linearity and can be considered a linear system where the resonant frequencies maintain a linear relationship with temperature. Based on the slope of the fitted lines, the theoretical temperature sensitivities of the first four resonant frequencies can be calculated as S1 = 0.39 Hz / ℃, S2 = 1.18 Hz / ℃, S3 = 1.97 Hz / ℃, and S4 = 2.75 Hz / ℃. Therefore, theoretically, without considering the signal-to-noise ratio and the difficulty of picking up the resonant peak, the higher the resonant frequency order, the higher the temperature sensitivity.

[0078] Third Embodiment

[0079] In a third exemplary embodiment of the present invention, an acoustic Fabry-Perot resonator was fabricated in the laboratory based on the structure described in the first embodiment. The fabricated acoustic Fabry-Perot resonator was used to measure the ambient background noise spectrum, and the effect of the signal processing method was evaluated by testing the frequency response characteristics of the Fabry-Perot resonator. Figure 4 The third embodiment of the present invention uses an acoustic Fabry-Perot resonator prepared in the laboratory to measure the ambient background noise spectrum.

[0080] First, measurements were performed using the B&K acoustic calibration system (including a loudspeaker (Visaton FR 9.15), a reference microphone (B&K, 4193), B&K PULSE Labshop software, and B&K LAN-3160 hardware). The B&K acoustic calibration system and the acoustic Fabry-Perot resonator were placed in the same test environment. The spectrum measured using the acoustic Fabry-Perot resonator under excitation from the loudspeaker is shown below. Figure 4 As shown in (a), the first-order resonant frequency, i.e., the fundamental frequency f1 = 226 Hz, is close to the theoretical value of 225.6 Hz calculated by formula (III). The higher-order resonant frequencies are all odd multiples of the fundamental frequency, which is basically consistent with the theory. These values ​​can be used as a reference for evaluating the effectiveness of subsequent signal processing methods.

[0081] Then, the speaker was turned off, and the time-domain signal from the microphone mounted at the end of the acoustic waveguide was acquired using a data acquisition card (SB 4431, National Instruments, United States). The power spectrum was calculated, and the results are as follows: Figure 4 As shown in Figure (b), the system has a sampling frequency and number of sampling points of 50k, and a power spectral resolution of 1Hz. The unprocessed power spectrum will produce a spectrum similar to... Figure 4 The multiple resonance peaks shown in Figure (a) are due to the Fabry-Perot resonator amplifying background noise in the environment near the system's resonant frequency. This method can avoid sound source excitation, reducing system power consumption and complexity. It also allows for simultaneous detection of multiple peaks, improving system efficiency and real-time performance. However, it's important to note that due to the weak ambient background noise, the average deviation between the resonant frequency of the obtained background noise power spectrum and the corresponding order resonant frequency shown in Figure (a) is 7 Hz. Furthermore, the higher the resonant frequency, the greater the deviation. This means that the system can only operate with low precision at low-order resonant frequencies with a low quality factor (Q) under these conditions. Clearly, this situation cannot meet the needs of most applications.

[0082] This problem is improved through a two-step signal processing approach. First, random noise at high frequencies is suppressed by summing the signals. After processing, the accuracy of picking up the system's higher-order resonant frequencies is significantly improved. Figure 4 Figure (c) shows the background noise power spectrum for superposition times of 10, 30, and 60. The average deviation of the last three resonant frequencies decreased from 10 Hz to 6 Hz. This is because superposition greatly enhances the signal at the resonant peaks, while the superposition of random noise at other frequencies tends to be constant. Taking the fourth resonant frequency f4 as an example, its signal-to-noise ratio (SNR) improved by 1.08 times. However, due to the significant non-random noise at low frequencies, the power spectrum itself exhibits a strong trend term. Therefore, superposition does not significantly improve the SNR of low-order resonant frequencies and may even worsen it. Furthermore, if there is strong non-random noise in the external environment, multiple resonant peaks at non-resonant frequencies will appear on the power spectrum, which can also lead to misjudgments by the system.

[0083] To address this issue, another reference microphone can be used to collect the background noise signal, obtaining a reference background noise power spectrum. Then, the superimposed background noise power spectrum is differentially analyzed with the reference background noise power spectrum to suppress low-frequency non-random noise and strong random noise. The results are as follows: Figure 4 As shown in Figure (d), it can be observed that after the two-step processing, a relatively sharp resonance peak can be observed in the power spectrum, and the resonant frequencies of the system are... Figure 4The average deviation of the results shown in Figure (a) is only 3 Hz. Compared with the original signal, the signal-to-noise ratio of f4 is improved by 2.3 times. This result confirms the feasibility and accuracy of the proposed signal processing method. It is worth noting that increasing the number of superpositions does not significantly improve the system performance, but rather increases the system's computation time and reduces its real-time performance. Therefore, the number of superpositions was chosen to be 10, and the time for one measurement was 10 seconds (sampling rate and number of sampling points are the same), balancing the high signal-to-noise ratio of the resonance peak and the real-time performance of the system.

[0084] It should be noted that, based on the technical characteristics of the proposed method, compared to traditional acoustic resonance temperature measurement methods, this method can simultaneously use multiple resonance peaks as reference quantities for temperature sensing without significantly affecting the system's real-time performance. This multi-peak operating mode gives the system higher temperature measurement accuracy and robustness.

[0085] Fourth embodiment

[0086] In a fourth exemplary embodiment of the present invention, an acoustic Fabry-Perot resonator of the structure described in the first embodiment was prepared in a laboratory and placed in a temperature-controlled chamber environment to measure the changes in each resonant frequency with temperature, in order to verify that the acoustic temperature sensor's response characteristics to temperature changes in the measured environment can meet the measurement requirements in practical applications.

[0087] Figure 5 This is a schematic diagram of the apparatus used in the fourth embodiment of the present invention to test the changes in the resonant frequencies of a laboratory-prepared acoustic Fabry-Perot resonator with temperature in a temperature-controlled chamber environment. Figure 5As shown, during signal acquisition, a reference microphone 30 (AWA14423, Hangzhou Aihua Instruments, China) and an acoustic Fabry-Perot resonator 10 are placed in the temperature cavity of a temperature-controlled chamber 40 (JJ-36L, Dongguan Jingte Instruments, China). The temperature inside the temperature cavity is controlled by the heating and cooling devices built into the temperature control chamber, and the temperature is calibrated by a thermometer built into the temperature cavity. The analog sound signal sensed by the microphone diaphragm is converted into a digital signal by a preamplifier and a data acquisition card (USB 4431, National Instruments, United States). This signal is processed by a computer-controlled LabVIEW (National Instruments, United States). The signal processing process specifically includes: first, performing a Fourier transform (FFT) on the time-domain signals from the dual channels to calculate the power spectrum of the background noise; then, summing the signals to filter out random noise; then, performing a differential operation on the two superimposed signals to filter additive noise; finally, peak detection is performed on the processed signal to obtain the resonant peak frequencies of each order.

[0088] It should be noted that the proposed real-time acquisition of the background noise spectrum via a reference microphone is a technique for ease of application. It targets complex, time-varying, and unpredictable noise fields in real-world working environments. Using this technique can resist most external noise, significantly improving the system's robustness. However, if the background noise in the application environment is relatively uniform and stable, in other embodiments, the background noise spectrum can be estimated in advance using a microphone, and then noise filtering and signal-to-noise ratio improvement can be achieved through signal processing techniques such as spectral subtraction. This approach can further reduce the system's complexity and cost.

[0089] Figure 6 The figures show curves of the changes in resonant frequencies, test temperature, and test error of the acoustic Fabry-Perot resonator as a function of ambient temperature, measured in a temperature-controlled chamber environment according to the fourth embodiment of the present invention. Figure 6 As shown, taking the second-order resonance peak as an example, it can be seen from... Figure 6 As shown in Figure (a-1), the resonant frequency exhibits a significant monotonic frequency shift as the ambient temperature increases from -20℃ to 60℃. The quality factor Q remains essentially consistent across different temperatures. The relationship between the second to seventh order resonant frequencies and temperature is shown below. Figure 6 As shown in the columns (a-1) to (f-1), the resonant frequencies of each order increase nearly linearly with increasing temperature. The R-value of the linearly fitted line... 2The values ​​are all above 0.98, indicating that the system possesses high linearity. The sensitivities for the second to seventh resonant frequencies, calculated from the slopes of the fitted lines, are S2 = 1.174 Hz / ℃, S3 = 1.805 Hz / ℃, S4 = 2.652 Hz / ℃, S5 = 3.366 Hz / ℃, S6 = 4.256 Hz / ℃, and S7 = 4.950 Hz / ℃. These results are consistent with... Figure 3 The maximum deviation of the theoretical sensitivity shown is 8%. The possible reason for this deviation is the uneven temperature inside the cavity, and the temperature measured by the built-in thermometer in the cavity deviates from the actual temperature at the waveguide. The average Q values ​​of the first four resonant peaks in the temperature range of -20℃ to 60℃ are calculated as Q1 = 4.47, Q2 = 9.34, Q3 = 13.11, and Q4 = 26.04, respectively. Therefore, if only one resonant peak is selected as a reference for temperature sensing, a higher-order resonant peak is a better choice because it has a higher Q value and higher sensitivity, which means that the resonant peak is sharper and the resonant frequency is easier to obtain accurately. However, if factors such as the signal-to-noise ratio of the noise signal are taken into account, the signal-to-noise ratio of higher-order resonant frequencies may decrease, so it is still more preferable to choose a lower-order resonant peak, such as the 1st to 4th order. Furthermore, when the test temperature is directly solved using formula (1), the relationship between the second to seventh order test temperatures and the ambient temperature is as follows. Figure 6 As shown in columns (a-2) to (f-2), it can be observed that the test temperature and the ambient temperature are roughly linearly related. However, the deviations of the resonance peaks of different orders relative to the ambient temperature vary, and the underlying pattern cannot be determined. Further analysis is needed... Figure 6 As shown in columns (a-3) to (f-3), the MAE error of the test temperature obtained from the third-order resonance peak relative to the ambient temperature is as high as 20℃.

[0090] To further improve testing accuracy and robustness, the linear relationship between resonant frequency and resonant peak order can be used to more accurately measure temperature. Figure 7 This is a curve showing the change of resonant frequency and test temperature under different ambient temperatures, measured in a temperature-controlled chamber environment according to the fourth embodiment of the present invention. Figure 7 As shown in Figure (a), under different ambient temperatures ranging from -20°C to 60°C, it was found that the resonant frequency exhibits a good linear relationship with the resonant peak order. The inset in Figure (a) shows SL. 2 The curve showing the change with temperature T indicates that the R² of the fitted straight line reaches 0.999, suggesting that SL... 2 It exhibits good linearity with T, indicating that it can be based on the slope. This is used to calculate the ambient temperature T. Figure 7As shown in Figure (b), the upper fitted line is the curve of the test temperature versus ambient temperature obtained by solving the slope SL obtained by the change of multiple resonance peaks with order, and the lower fitted line is the curve of the test temperature versus ambient temperature obtained by solving the resonant frequency of the first resonant peak according to formula (1). Obviously, the linearity of the upper fitted line is better. According to the inset in Figure (b), the error MAE of the test temperature obtained by using a single resonance peak in the upper curve relative to the ambient temperature is as high as 14℃, while the error of the test temperature obtained by using the fitting slope SL of multiple resonant frequencies with order in the lower curve relative to the ambient temperature is within 2℃, which significantly improves the test accuracy.

[0091] In summary, during actual measurement, the accuracy of temperature measurement can be improved by averaging the temperature values ​​calculated for each resonance peak in the background noise spectrum, or by averaging the temperature values ​​calculated for each resonance peak with similar quality factor and signal-to-noise ratio. However, a more preferred method is to calculate the temperature value by fitting the slope of the frequency of multiple resonance peaks with the order, which provides higher accuracy than the aforementioned methods.

[0092] Fifth embodiment

[0093] In a fifth exemplary embodiment of the present invention, the acoustic Fabry-Perot resonator of the structure described in the first embodiment, prepared in the laboratory, is placed in a fully anechoic chamber with a size of 6.4m × 4.7m × 4.2m and a background noise of less than 6dBA to measure the background noise spectrum.

[0094] Figure 8 This is the background noise spectrum measured in a fully anechoic chamber using a laboratory-prepared acoustic Fabry-Perot resonator, according to the fifth embodiment of the present invention. With no sound applied, a single microphone was placed in the central region of the anechoic chamber to estimate the ambient background noise; the results are as follows. Figure 8 As shown in Figure (a), a relatively flat spectrum can be observed, with the sound pressure level of the background noise basically below 0 dB. Next, the acoustic Fabry-Perot resonator was placed near the reference microphone for background noise measurement and signal processing. The resulting power spectrum is shown below. Figure 8 As shown in Figure (b), compared to the results obtained in the third embodiment, the signal-to-noise ratio of the system operating in the anechoic chamber is reduced, but the first seven resonance peaks are still clearly visible. This indicates that the system can still function normally even in a relatively quiet environment. Figure 8Figure (c) shows the linear variation curves of each resonant frequency with the resonant peak order in Figure (b). The linearity of the fit reaches 0.999, and the slope is 0.451. Combined with formula (2), the ambient temperature is calculated to be 18.41℃, which is slightly lower than the 20.5℃ measured by a commercially available electronic thermometer (Testo 635-2, Testo SE&Co.KGaA, Germany). This shows that even if the ambient temperature is very quiet, the method of the present invention can still be used to determine its temperature.

[0095] The five embodiments of the present invention have now been described in detail with reference to the accompanying drawings. Based on the above description, those skilled in the art should have a clear understanding of the acoustic temperature sensor and temperature measurement method of the present invention.

[0096] In summary, the acoustic temperature sensor and temperature measurement method of this invention can achieve temperature measurement by measuring ambient background noise without the need for active sound source excitation. It boasts advantages such as simple structure and operation, high sensitivity, low power consumption, and low cost. Furthermore, by employing a MEMS acoustic sensor and a miniature acoustic waveguide, the acoustic temperature sensor of this invention can be further developed into a highly integrated on-chip acoustic thermometer.

[0097] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An acoustic temperature sensor, characterized in that, The acoustic temperature sensor includes: A microphone for detecting sound waves, the microphone having a sound-sensing surface; The acoustic waveguide has a first port and a second port with flush port surfaces. After the first port is blocked by the microphone, it forms an acoustic Fabry-Perot resonator with the second port. The first reflecting surface of the acoustic Fabry-Perot resonator is the acoustic sensing surface of the microphone, and the second reflecting surface is the interface between the inner and outer air of the second port. The background noise of the measured environment is transmitted from the second port into the acoustic waveguide and modulated by the acoustic Fabry-Perot resonator. The modulated background noise signal is converted into an electrical signal by the microphone and then output. The power supply and signal processing module is electrically connected to the microphone and is used to supply power to the microphone and process the electrical signal output by the microphone to obtain the temperature of the measured environment.

2. The acoustic temperature sensor according to claim 1, characterized in that, The acoustic waveguide is one of the following: metal tube, ceramic tube, hardwood tube, glass tube, or PVC tube.

3. The acoustic temperature sensor according to claim 1, characterized in that, The acoustic waveguide can be straight, curved, or a combination of straight and curved shapes.

4. The acoustic temperature sensor according to claim 1 or 3, characterized in that, The axes at the first and second ports of the acoustic waveguide are perpendicular to the first and second reflecting surfaces of the acoustic Fabry-Perot resonator.

5. The acoustic temperature sensor according to claim 1, characterized in that, The microphone is one of the following: electret condenser microphone, piezoelectric microphone, electromagnetic microphone, fiber optic microphone, grating microphone, or MEMS microphone.

6. The acoustic temperature sensor according to claim 1, characterized in that, The lower limit of the frequency response of the microphone is no greater than 50 Hz.

7. A method for measuring temperature using an acoustic temperature sensor as described in any one of claims 1 to 6, characterized in that, Includes the following steps: Step A: Place the acoustic Fabry-Perot resonator in the environment under test. The acoustic Fabry-Perot resonator is composed of a first port of an acoustic waveguide blocked by a microphone and a second port of the acoustic waveguide. The microphone is powered by a power supply and signal processing module. Step B: Use the acoustic Fabry-Perot resonator to continuously detect the background noise of the environment under test multiple times, and output multiple background noise time-domain spectra; Step C: The power supply and signal processing module is used to process the time-domain spectra of the background noise to obtain the background noise spectrum, wherein the processing includes spectrum conversion and spectrum superposition. Step D: Use the power supply and signal processing module to determine the frequencies and orders corresponding to multiple resonant peaks from the background noise spectrum; Step E: Determine the temperature of the measured environment based on the linear regression slope between the frequencies and orders of the multiple resonant peaks.

8. The method according to claim 7, characterized in that, Step E specifically includes: Sub-step E1: Plot the linear curves between the frequencies and orders corresponding to the multiple resonant peaks; Sub-step E2: Calculate the temperature of the measured environment using the following formula based on the slope of the linear curve: Wherein, SL is the slope of the linear curve, T is the Kelvin temperature of the measured environment, B is a constant, for airborne sound B=20.05, and L is the length of the acoustic waveguide between the first and second reflecting surfaces.

9. The method according to claim 7, characterized in that, The method further includes: The background noise of the measured environment is detected using a reference microphone to obtain the time-domain spectrum of the reference background noise; and The power supply and signal processing module is used to perform spectral conversion on the time-domain spectrum of the reference background noise to obtain the reference background noise spectrum. Step C further includes: performing a difference operation between the background noise spectrum and the reference background noise spectrum to obtain the background noise spectrum after the difference operation.

10. The method according to claim 7, characterized in that, The background noise of the measured environment is detected continuously for no less than three times using the acoustic Fabry-Perot resonator.

Images