Method and device for detecting thickness of accumulated ice, and aircraft

Abstract

The invention discloses an ice accumulation thickness detection method and device and an aircraft, and the method comprises the steps: obtaining a sampling signal when no ice is accumulated in a to-be-detected ice accumulation region through a transmitter-receiver pair which is disposed in the to-be-detected ice accumulation region and of which a signal transmission path passes through the to-be-detected ice accumulation region, and setting the sampling signal as a first baseline signal; subsequently calculating a first signal difference coefficient between the newly acquired sampled signal and the first baseline signal after acquiring the sampled signal by the transmitter-receiver pair; and based on the first signal difference coefficient and an ice accumulation thickness calculation model constructed in advance based on a coupling relationship between the signal difference coefficient and the ice accumulation thickness, determining the ice accumulation thickness of the ice accumulation area to be measured.

Description

Technical Field

[0001] The present application relates to the technical field of icing detection, and more particularly to a method and device for detecting icing thickness and an aircraft. Background Art

[0002] The aircraft anti-icing technology can generally be divided into three steps: prevention, detection, and removal. Existing detection methods using ultrasonic guided wave technology rely on establishing a database of a large number of ultrasonic guided wave signal data to distinguish the ice layer state. Therefore, most of these methods can only be applied to the corresponding structures and cannot be extended to other structures. Based on the above analysis, although ultrasonic guided waves have strong application potential in the field of icing detection and have been verified in some previous studies. However, in current research, the determination of the inversion parameters of icing information is generally based on statistical methods, and a database of a large number of ultrasonic guided wave signal data is established to distinguish the ice layer state. Therefore, most of these methods can only be applied to the corresponding structures and specific environments and cannot be extended to other application objects. In addition, these methods can only distinguish specific ice layer states defined in the model and cannot quantitatively measure the size parameters of the ice layer, such as length and thickness. Summary of the Invention

[0003] The present application is proposed to solve the above problems. According to the first aspect of the present application, a method for detecting icing thickness is provided. The detection method includes: obtaining a sampling signal when there is no ice on the to-be-detected icing area through a transmitter-receiver pair disposed in the to-be-detected icing area and with a signal transmission path passing through the to-be-detected icing area, and setting it as the first baseline signal; subsequently, after obtaining the sampling signal once through the transmitter-receiver pair, calculating a first signal difference coefficient between the latest obtained sampling signal and the first baseline signal; and determining the icing thickness of the to-be-detected icing area based on the first signal difference coefficient and an icing thickness calculation model pre-constructed based on the coupling relationship between the signal difference coefficient and the icing thickness.

[0004] According to the method and device for detecting icing thickness and the aircraft provided by the embodiments of the present application, by obtaining a sampling signal when there is no ice on the to-be-detected icing area and setting it as the first baseline signal; subsequently, after obtaining a sampling signal once, calculating a first signal difference coefficient between the latest obtained sampling signal and the first baseline signal; and determining the icing thickness of the to-be-detected icing area based on the first signal difference coefficient and an icing thickness calculation model pre-constructed based on the coupling relationship between the signal difference coefficient and the icing thickness, the measurement of the thickness size of the ice is realized, so that the change of the icing thickness can be detected in real time. Brief Description of the Drawings

[0005] Figure 1Schematic flowchart of the ice thickness detection method shown in an embodiment of the present application;

[0006] Figure 2 Top view setting schematic diagram of the transmitter-receiver pair shown in an embodiment of the present application;

[0007] Figures 3 to 5 Schematic flowchart of the ice thickness detection method shown in another different embodiment of the present application;

[0008] Figures 6 to 9 Schematic flowchart of the construction method of the ice thickness calculation model shown in different embodiments of the present application;

[0009] Figure 10 Comparison schematic diagram of different effective signal interception ranges shown in an embodiment of the present application;

[0010] Figure 11 Relationship comparison schematic diagram of different sub-ice thickness calculation models and the total ice thickness calculation model shown in an embodiment of the present application;

[0011] Figure 12 Guided wave dispersion curves corresponding to different ice layers shown in an embodiment of the present application;

[0012] Figure 13 Schematic diagram of the overall signal transmission scheme of the experimental system shown in an embodiment of the present application;

[0013] Figure 14 Overall structure schematic diagram of the experimental system shown in an embodiment of the present application;

[0014] Figure 15 Three-dimensional schematic diagram of the overall structure for experiments shown in an embodiment of the present application;

[0015] Figure 16 Hardware connection schematic diagram of the ultrasonic guided wave ice detection prototype shown in an embodiment of the present application;

[0016] Figure 17 Software working flowchart of the ultrasonic guided wave ice detection prototype shown in an embodiment of the present application;

[0017] Figure 18 Software working flowchart of the ultrasonic guided wave ice detection prototype shown in another embodiment of the present application;

[0018] Figure 19 Simulation schematic diagram of the experimental prototype shown in an embodiment of the present application;

[0019] Figure 20 Actual photo of the experimental prototype shown in an embodiment of the present application;

[0020] Figure 21 Schematic diagram of comparison between sampling signal and baseline signal shown in an embodiment of the present application;

[0021] Figure 22 Schematic diagram of comparison between MATLAB calculation results and prototype (Vivado) calculation results shown in an embodiment of the present application;

[0022] Figure 23 Schematic diagram of comparison between MATLAB calculation results and prototype (Vivado) calculation results shown in another embodiment of the present application;

[0023] Figure 24 Schematic diagram of single - experiment results shown in an embodiment of the present application;

[0024] Figure 25 Schematic diagram of change in SDC value without changing the baseline signal shown in an embodiment of the present application;

[0025] Figure 26 Schematic diagram of change in SDC value when the baseline signal and the effective signal intercept range are changed in real time shown in an embodiment of the present application;

[0026] Figure 27 Schematic diagram of relationship between cumulative SDC, spray time and ice layer thickness shown in an embodiment of the present application;

[0027] Figure 28 Schematic diagram of fitting of multiple - group experimental results of ice layer thickness shown in an embodiment of the present application. Detailed implementation manners

[0028] To thoroughly understand the present application, detailed structures will be presented in the following description to illustrate the technical solutions proposed by the present application. The optional embodiments of the present application are described in detail below. However, in addition to these detailed descriptions, the present application may have other implementation manners. The following describes some implementation manners of the present application in conjunction with the accompanying drawings. Without conflict, the following embodiments and the features in the embodiments may be combined with each other.

[0029] Embodiment 1

[0030] First, the application scenario of the ice - thickness detection method shown in the examples of the present application is introduced. This ice - thickness detection method is applied in the process of ice - thickness detection.

[0031] Exemplarily, referring to Figure 1 and Figure 2 , the embodiments of the present application provide an ice - thickness detection method, which mainly includes the following steps:

[0032] In S101, a sampling signal when there is no ice accumulation in the ice accumulation area to be measured is obtained through a transmitter-receiver pair disposed in the ice accumulation area to be measured and with a signal transmission path passing through the ice accumulation area to be measured, and it is set as the first baseline signal;

[0033] In S102, after obtaining a sampling signal through the transmitter-receiver pair subsequently, a first signal difference coefficient between the latest obtained sampling signal and the first baseline signal is calculated;

[0034] In S103, based on the first signal difference coefficient and an ice thickness calculation model pre-constructed based on the coupling relationship between the signal difference coefficient and the ice thickness, the ice thickness of the ice accumulation area to be measured is determined.

[0035] In the above solution, by obtaining a sampling signal when there is no ice accumulation in the ice accumulation area to be measured and setting it as the first baseline signal; after obtaining a sampling signal subsequently, calculating a first signal difference coefficient between the latest obtained sampling signal and the first baseline signal; and based on the first signal difference coefficient and an ice thickness calculation model pre-constructed based on the coupling relationship between the signal difference coefficient and the ice thickness, determining the ice thickness of the ice accumulation area to be measured, the measurement of the thickness dimension of the ice accumulation is realized, so that the change of the ice thickness can be detected in real time. The following introduces each of the above steps in detail with reference to the accompanying drawings.

[0036] First, refer to Figure 1 and Figure 2, a transmitter-receiver pair is set in the icing area to be measured, and the signal transmission path passes through the icing area to be measured. A sampling signal when there is no ice in the icing area to be measured is obtained and set as the first baseline signal. It should be noted that regarding the setting of the icing area to be measured, it can be an area on an application object such as but not limited to the skin of an aircraft. Regarding the setting method of the transmitter-receiver pair, it includes a transmitter and a receiver set in pairs. Both the transmitter and the receiver are set on the icing area to be measured and are spaced apart from each other, so that the excitation signal generated by the transmitter can pass through the icing area to be measured and then be received by the receiver, thereby obtaining the original sampling signal. Exemplarily, a control device can also be set as the master control unit. The control device is communicatively connected to the transmitter to control the transmitter to emit an excitation signal. And the control device is communicatively connected to the receiver to control the receiver to receive the excitation signal. Exemplarily, the excitation signal emitted by the transmitter can be an ultrasonic guided wave, that is, an ultrasonic guided wave is used as a tool for ice detection. Exemplarily, the transmitter can continuously output an excitation pulse signal periodically, the signal waveform can be a sine signal of 1 cycle, and the pulse interval can be 30 ms. Exemplarily, the transmitter and the receiver can include a first piezoelectric transducer for emitting an excitation signal and a second piezoelectric transducer for receiving the original sampling signal. The first piezoelectric transducer and the second piezoelectric transducer can realize the mutual conversion between the electrical signal and the ultrasonic guided wave signal, thereby realizing the emission of the excitation signal and the reception of the original sampling signal. Exemplarily, the transmitter can further include: an excitation signal generation source and a first signal amplifier connected between the first piezoelectric transducer and the excitation signal generation source. The first signal amplifier is used to amplify the excitation signal generated by the excitation signal generation source and then send it to the first piezoelectric transducer, thereby realizing the emission of the excitation signal. Exemplarily, the receiver can further include: a second signal amplifier connected to the second piezoelectric transducer. The second signal amplifier is used to amplify the original sampling signal output by the second piezoelectric transducer. Exemplarily, various types of filters can also be set between the second signal amplifier and the control device to filter the received original sampling signal. Exemplarily, the control device can also intercept the original sampling signal based on the effective signal interception range to obtain the sampling signal. Exemplarily, the control device can further include a storage medium for storing the obtained sampling signal. Exemplarily, the control device can also have the function of calculating the signal difference coefficient between the sampling signal and the set baseline signal, that is, the control device has the functions of calculating the signal characteristics and comparing the signal differences. When there is no ice in the icing area to be measured, at least one sampling signal is obtained through the transmitter-receiver pair, and the sampling signal at this time is set as the first baseline signal. For example, after the icing area to be measured has just completed deicing, a sampling signal can be obtained, and the sampling signal obtained at this time is used as the first baseline signal.

[0037] Next, refer to Figure 1 After obtaining a sampling signal through the transmitter-receiver pair once, calculate the first signal difference coefficient between the latest obtained sampling signal and the first baseline signal.

[0038] Next, refer to Figure 1 Based on the first signal difference coefficient and the ice thickness calculation model pre-constructed based on the coupling relationship between the signal difference coefficient and the ice thickness, determine the ice thickness of the ice accumulation area to be measured. It should be noted that the ice thickness calculation model pre-constructed based on the coupling relationship between the signal difference coefficient and the ice thickness can adopt any ice thickness calculation model constructed through simulation or experiment based on the coupling relationship between the signal difference coefficient and the ice thickness. The description of the third part of the following embodiment can be referred to for the experimental construction method. When determining the ice thickness of the ice accumulation area to be measured based on the first signal difference coefficient and the ice thickness calculation model, different methods can be adopted according to different types of ice thickness calculation models. Some methods are introduced in the following embodiments. Exemplarily, the ice thickness calculation model can include the following first sub-ice thickness calculation model:

[0039] d ice = SDC1 × ξ1

[0040] where, d ice represents the ice thickness; SDC1 represents the first signal difference coefficient; ξ1 represents the first thickness conversion coefficient. At this time, based on the first signal difference coefficient and the ice thickness calculation model pre-constructed based on the coupling relationship between the signal difference coefficient and the ice thickness, determining the ice thickness of the ice accumulation area to be measured can include: substituting the first signal difference coefficient into the above first sub-ice thickness calculation model of the ice thickness calculation model, and calculating to obtain the ice thickness of the ice accumulation area to be measured. The reason for adopting the above method in this embodiment is that the present application has found that when the first signal difference coefficient is not greater than the first set value, there is a good linear correspondence relationship between the first signal difference coefficient and the ice thickness, and the ice thickness can be accurately detected based on this at this time.

[0041] Exemplarily, refer to Figure 3, the detection method may further include the following steps: starting from when there is no ice accumulation in the ice accumulation area to be measured, periodically obtaining sampling signals through the transmitter-receiver pair; when the latest calculated first signal difference coefficient is not greater than the first set value, substituting the first signal difference coefficient into the first sub-ice accumulation thickness calculation model to calculate the ice accumulation thickness of the ice accumulation area to be measured; when the latest calculated first signal difference coefficient is greater than the first set value, setting the latest obtained sampling signal as the second baseline signal; after each subsequent acquisition of a sampling signal, calculating the second signal difference coefficient between the latest obtained sampling signal and the second baseline signal; and determining the ice accumulation thickness of the ice accumulation area to be measured based on the second signal difference coefficient and the ice accumulation thickness calculation model.

[0042] In the above embodiment, starting from when there is no ice accumulation in the ice accumulation area to be measured, that is, periodically obtaining sampling signals through the transmitter-receiver pair, the specific method may be: immediately starting or starting after a period of time the process of periodically obtaining sampling signals after obtaining the first baseline signal. For example, when the ice accumulation area to be measured is located on the skin surface of an aircraft such as but not limited to an aircraft, the process of periodically obtaining sampling signals may be started at the moment of takeoff of the aircraft or after taking off to a certain altitude. After each acquisition of a sampling signal, calculate the first signal difference coefficient between the latest obtained sampling signal and the first baseline signal. In this embodiment, different ice accumulation thickness detection methods are also determined according to the relationship between the first signal difference coefficient and the first set value. The following is a specific description.

[0043] Reference Figure 3 , when the latest calculated first signal difference coefficient is not greater than the first set value, substituting the first signal difference coefficient into the first sub-ice accumulation thickness calculation model to calculate the ice accumulation thickness of the ice accumulation area to be measured. Reference Figure 3, and when the latest calculated first signal difference coefficient is greater than the first set value, the latest acquired sampling signal is set as the second baseline signal, and after each subsequent acquisition of the sampling signal, the second signal difference coefficient between the latest acquired sampling signal and the second baseline signal is calculated. Then, based on the second signal difference coefficient and the ice accumulation thickness calculation model, the ice accumulation thickness of the ice accumulation area to be measured is determined. The reason why this embodiment adopts the above method is that the present application has found that: when the first signal difference coefficient is not greater than the first set value, the first signal difference coefficient and the ice accumulation thickness meet the linear correspondence relationship well, and the ice accumulation thickness can be accurately detected by this. After the first signal difference coefficient is greater than the first set value, the first signal difference coefficient and the ice accumulation thickness no longer meet the linear correspondence relationship, or the fitting matching degree of the linear fitting model is not high. The ice accumulation thickness is no longer detected based on the first signal difference coefficient, but the ice accumulation thickness is detected based on the second signal difference coefficient calculated based on the reset second baseline signal, thereby improving the detection accuracy of the ice accumulation thickness.

[0044] Regarding the method of determining the ice thickness of the ice accretion area to be measured based on the second signal difference coefficient and the ice accretion thickness calculation model, different methods can be adopted according to different types of ice accretion thickness calculation models. Some methods are introduced as follows. For example, the ice accretion thickness calculation model may include the following second sub-ice accretion thickness calculation model:

[0045] d ice =(SDC2+α2)×ξ2

[0046] Wherein, SDC2 represents the second signal difference coefficient; α2 represents the second accumulation coefficient; ξ2 represents the second thickness conversion coefficient. At this time, based on the second signal difference coefficient and the ice accretion thickness calculation model, determining the ice accretion thickness of the ice accretion area to be measured may include: substituting the second signal difference coefficient into the second sub-ice accretion thickness calculation model of the ice accretion thickness calculation model, and calculating the ice accretion thickness of the ice accretion area to be measured. In the above embodiment, the present application found that: after resetting the second baseline signal, the second signal difference coefficient calculated based on the reset second baseline signal and the ice accretion thickness again meet the linear correspondence relationship. Based on this, the present embodiment adopts the second sub-ice accretion thickness calculation model as a linearly fitted model as the calculation model for calculating the ice accretion thickness based on the second signal difference coefficient, so that the ice accretion thickness can be accurately detected, thereby improving the accuracy of detection. It should be noted that the ice accretion thickness calculation model is not limited to the first sub-ice accretion thickness calculation model and the second sub-ice accretion thickness calculation model shown above. In addition, it can also include other models or adopt other models.

[0047] Exemplarily, the ice accumulation thickness calculation model may further include a first sub-ice accumulation thickness calculation model to an Nth sub-ice accumulation thickness calculation model, where N can be any positive integer not less than 2, such as 2, 3, 4, 6, 8, etc. Among them, the ith sub-ice accumulation thickness calculation model in the ice accumulation thickness calculation model may include the following formula:

[0048]

[0049] Among them, i is any positive integer not less than 1, and i also represents the number of times of the set baseline signal; SDC i represents the ith signal difference coefficient; α j represents the jth cumulative coefficient preset in advance; ξ i represents the ith thickness conversion coefficient.

[0050] Exemplarily, the (i + 1)th sub-ice accumulation thickness calculation model in the ice accumulation thickness calculation model may include the following formula:

[0051]

[0052] Among them, SDC i+1 represents the (i + 1)th signal difference coefficient; ξ i+1 represents the (i + 1)th thickness conversion coefficient.

[0053] In this embodiment, referring to Figure 4 , this detection method may further include:

[0054] Starting from when there is no ice accumulation in the to-be-detected ice accumulation area, periodically obtain sampling signals through the transmitter-receiver pair; the specific execution method can refer to the description of the same part above;

[0055] After each acquisition of a sampling signal, calculate the ith signal difference coefficient between the latest acquired sampling signal and the currently set ith baseline signal;

[0056] Subsequently, based on the relationship between the ith signal difference coefficient and the ith set value, different processing methods are adopted, which are specifically described as follows.

[0057] When the ith signal difference coefficient is greater than the ith set value, set the latest acquired sampling signal as the (i + 1)th baseline signal;

[0058] For the sampling signal acquired after resetting the latest baseline signal, calculate the (i + 1)th signal difference coefficient based on the newly set (i + 1)th baseline signal;

[0059] Substitute the (i + 1)th signal difference coefficient into the above (i + 1)th sub-ice accumulation thickness calculation model of the ice accumulation thickness calculation model to calculate the ice accumulation thickness of the to-be-detected ice accumulation area.

[0060] In the above embodiments, the present application discovers that: after resetting the (i + 1)-th baseline signal, the (i + 1)-th signal difference coefficient calculated based on the reset (i + 1)-th baseline signal and the ice accretion thickness again satisfy a linear correspondence. By virtue of this, in this embodiment, by using the above (i + 1)-th sub-ice accretion thickness calculation model that is linearly fitted as the calculation model for calculating the ice accretion thickness based on the (i + 1)-th signal difference coefficient, the ice accretion thickness can be accurately detected, improving the accuracy of detection.

[0061] Exemplarily, in this embodiment, referring to Figure 4 , the detection method may further include: when the i-th signal difference coefficient is not greater than the i-th set value, substituting the i-th signal difference coefficient into the above i-th sub-ice accretion thickness calculation model of the ice accretion thickness calculation model to calculate the ice accretion thickness of the to-be-detected ice accretion area.

[0062] The reason for adopting the above method in this embodiment is that the present application discovers that: when the i-th signal difference coefficient is not greater than the i-th set value, the i-th signal difference coefficient and the ice accretion thickness satisfy a good linear correspondence, and at this time, the ice accretion thickness can be accurately detected based on this. After the i-th signal difference coefficient is greater than the i-th set value, the i-th signal difference coefficient and the ice accretion thickness no longer satisfy the linear correspondence, or rather, the fitting matching degree of the linear fitting model is not high. Subsequently, the ice accretion thickness is not detected based on the i-th signal difference coefficient, but based on the (i + 1)-th signal difference coefficient calculated based on the reset (i + 1)-th baseline signal, thereby improving the detection accuracy of the ice accretion thickness. It should be noted that the ice accretion thickness calculation model is not limited to the above-described first sub-ice accretion thickness calculation model to the N-th sub-ice accretion thickness calculation model. In addition, the ice accretion thickness calculation model can also adopt other methods.

[0063] Exemplarily, the ice accretion thickness calculation model may further include the following total ice accretion thickness calculation model:

[0064] d ice =(SDC i +α×(i - 1))×ξ

[0065] where SDC i represents the i-th signal difference coefficient; α represents the cumulative coefficient; ξ represents the thickness conversion coefficient.

[0066] At this time, referring to Figure 5 , the detection method may further include:

[0067] starting from when there is no ice accretion in the to-be-detected ice accretion area, periodically obtaining sampling signals through the transmitter-receiver pair;

[0068] After obtaining a sampling signal each time, calculate the i-th signal difference coefficient between the latest obtained sampling signal and the currently set i-th baseline signal; where i is any positive integer not less than 1.

[0069] If the i-th signal difference coefficient is not greater than the i-th set value, keep the currently set baseline signal unchanged, and substitute the i-th signal difference coefficient into the above total ice thickness calculation model of the ice thickness calculation model to calculate the ice thickness of the ice accumulation area to be measured.

[0070] In the above embodiment, only by using the total ice thickness calculation model, it can be applicable to the thickness detection in each baseline signal stage, without replacing the detection model, thus simplifying the calculation process. Of course, if the i-th signal difference coefficient is greater than the i-th set value, the following processing method can also be adopted.

[0071] Exemplarily, referring to Figure 5 , the detection method may further include:

[0072] If the i-th signal difference coefficient is greater than the i-th set value, set the latest obtained sampling signal as the (i + 1)-th baseline signal;

[0073] For the sampling signal obtained after resetting the latest baseline signal, calculate the (i + 1)-th signal difference coefficient based on the newly set (i + 1)-th baseline signal;

[0074] When the (i + 1)-th signal difference coefficient is not greater than the (i + 1)-th set value, keep the currently set baseline signal unchanged, and substitute the (i + 1)-th signal difference coefficient into the following total ice thickness calculation model to calculate the ice thickness of the ice accumulation area to be measured:

[0075] d ice =(SDC i+1 +α×i)×ξ

[0076] Where SDC i+1 represents the (i + 1)-th signal difference coefficient.

[0077] In the above embodiment, when the i-th signal difference coefficient is greater than the i-th set value, only by updating some parameters in the total ice thickness calculation model, the updated total ice thickness calculation model can be applicable to the thickness detection in the (i + 1)-th baseline signal stage, without replacing the detection model, thus simplifying the calculation process.

[0078] Exemplarily, referring to Figure 10 , the detection method may further include:

[0079] Based on the amplitude and / or time distribution of the first baseline signal, set the effective signal intercept range when obtaining the sampling signal used to calculate the first signal difference coefficient;

[0080] While resetting the baseline signal, the effective signal intercept range for subsequent acquisition of the sampled signal is also reset based on the amplitude and / or time distribution of the reset baseline signal.

[0081] In the above embodiments, after each setting of the baseline signal, the corresponding effective signal intercept range is also set based on the reset baseline signal, so as to obtain a sampled signal that is more conducive to accurately calculating the ice accretion thickness, thereby improving the detection accuracy of the ice accretion thickness.

[0082] Exemplarily, referring to Figure 10 , the effective signal intercept range corresponding to the i-th baseline signal is: the start and end time range of the first wave packet range on the i-th baseline signal; where i is any positive integer not less than 1. That is, for the corresponding effective signal intercept range set for each baseline signal, it is the range formed between the start time point and the end time point of the first wave packet range on the baseline signal, so as to intercept the original sampled signal received by the receiver based on this effective signal intercept range to obtain the sampled signal. For example, referring to Figure 10 , the effective signal intercept ranges corresponding to the first baseline signal without ice, the baseline signal when the ice accretion thickness is equal to 0.5 mm, and the baseline signal when the ice accretion thickness is equal to 1 mm are respectively shown. It can be seen that there are differences in the start and end time ranges of the effective signal intercept ranges corresponding to different baseline signals. Of course, it should be noted that preprocessing such as but not limited to denoising and filtering can also be performed on the original sampled signal received by the receiver to prevent errors caused by crosstalk, etc. There are various ways to calculate the signal difference coefficient between the sampled signal and the currently set baseline signal, and some ways are introduced in the following embodiments. Exemplarily, calculating the signal difference coefficient between the sampled signal and the currently set baseline signal may include:

[0083] Using the following formula to calculate the signal difference coefficient between the sampled signal and the currently set baseline signal:

[0084]

[0085] where SDC represents the signal difference coefficient, and in combination with the current i-th baseline signal stage, it can be SDC i .

[0086] s0(t) represents the currently set baseline signal, and in combination with the current i-th baseline signal stage, it can be

[0087] s1(t) represents the sampled signal, and in combination with the current i-th baseline signal stage, it can be

[0088] Example Two

[0089] This embodiment provides a device for detecting ice accretion thickness. The detection device includes: a transmitter-receiver pair disposed in the ice accretion area to be measured and having a signal transmission path passing through the ice accretion area to be measured, for obtaining sampling signals; and a control device communicatively connected to the transmitter-receiver pair for performing any of the above ice accretion thickness detection methods. Regarding the setting manner of the control device, it can adopt various ways, and an example is introduced as follows. Exemplarily, the above control device may include: a storage medium and a processor. A computer program is stored on the storage medium and run by the processor. When the computer program is run by the processor, the processor performs any of the above ice accretion thickness detection methods. Among them, the processor may include processor devices with logical operation functions such as, but not limited to, single-chip microcomputers, MCUs (microcontroller units), etc. The storage medium may include, for example, a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0090] Example Three

[0091] This embodiment further provides a method for constructing an ice accretion thickness calculation model. The construction method can construct any of the sub-ice accretion thickness calculation models or the total ice accretion thickness calculation model shown in the above detection method, and is introduced in detail as follows.

[0092] Exemplarily, referring to Figure 2 and Figure 6 , an embodiment of the present application provides a method for constructing an ice accretion thickness calculation model. The construction method mainly includes the following steps: setting a transmitter-receiver pair in the ice accretion area to be measured and having a signal transmission path passing through the ice accretion area to be measured; adjusting the temperature of the ice accretion area to be measured to a set temperature range and continuously spraying liquid on the ice accretion area to be measured to form ice accretion with a gradually increasing thickness in the ice accretion area to be measured; while periodically obtaining sampling signals through the transmitter-receiver pair, periodically measuring the ice accretion thickness to obtain an ice accretion thickness change sample set; setting the sampling signal obtained when there is no ice accretion in the ice accretion area to be measured as the first baseline signal; after each acquisition of a sampling signal, calculating the first signal difference coefficient between the latest acquired sampling signal and the first baseline signal to obtain a first signal difference coefficient change sample set; constructing a first sub-ice accretion thickness calculation model based on the first signal difference coefficient change sample set and the ice accretion thickness change sample set.

[0093] In the above solution, a transmitter-receiver pair with a signal transmission path passing through the icing area to be measured is set in the icing area to be measured, and a liquid is continuously sprayed onto the icing area to be measured to form icing with a gradually increasing thickness in the icing area to be measured; during this process, while periodically obtaining sampling signals through the transmitter-receiver pair, the icing thickness is periodically measured to obtain an icing thickness change sample set; the sampling signal obtained when there is no icing in the icing area to be measured is set as the first baseline signal; after each sampling signal is obtained, the first signal difference coefficient between the latest obtained sampling signal and the first baseline signal is calculated to obtain a first signal difference coefficient change sample set; then, based on the first signal difference coefficient change sample set and the icing thickness change sample set, a first sub-icing thickness calculation model is constructed. Thus, in subsequent applications of the icing area to be measured, the icing thickness can be detected based on the first sub-icing thickness calculation model, so that the icing thickness detection method is not limited to the corresponding structure and specific environment and can be extended to various application objects. Moreover, the measurement of the thickness dimension of the icing can be realized, and thus the change of the icing thickness can be detected in real time. The following is a detailed introduction to the above-described construction methods with reference to the accompanying drawings.

[0094] First, refer to Figure 2 and Figure 6, a transmitter-receiver pair whose signal transmission path passes through the icing area to be measured is set in the icing area to be measured. The setting method can be various. Some methods are introduced as follows by way of example. Regarding the setting of the icing area to be measured, an area on an application object such as but not limited to the skin of an aircraft can be directly used as the icing area to be measured, or a plate structure for experiments can be set, and a certain area on the plate structure can be used as the icing area to be measured. Regarding the setting method of the transmitter-receiver pair, it includes a paired transmitter and receiver. Both the transmitter and the receiver are set on the icing area to be measured and are spaced apart from each other, so that the excitation signal generated by the transmitter can be received by the receiver after passing through the icing area to be measured, thereby obtaining the original sampling signal. Exemplarily, a control device can also be set as the master control unit. The control device is communicatively connected to the transmitter to control the transmitter to emit an excitation signal. And the control device is communicatively connected to the receiver to control the receiver to receive the excitation signal. Exemplarily, the excitation signal emitted by the transmitter can be an ultrasonic guided wave, that is, an ultrasonic guided wave is used as a tool for ice detection. Exemplarily, the transmitter can continuously output an excitation pulse signal periodically, the signal waveform can be a sine signal of 1 cycle, and the pulse interval can be 30 ms. Exemplarily, the transmitter and the receiver can include a first piezoelectric transducer for emitting an excitation signal and a second piezoelectric transducer for receiving the original sampling signal. The first piezoelectric transducer and the second piezoelectric transducer can realize the mutual conversion between the electrical signal and the ultrasonic guided wave signal, thereby realizing the emission of the excitation signal and the reception of the original sampling signal. Exemplarily, the transmitter can further include: an excitation signal generation source and a first signal amplifier connected between the first piezoelectric transducer and the excitation signal generation source. The first signal amplifier is used to amplify the excitation signal generated by the excitation signal generation source and then send it to the first piezoelectric transducer, thereby realizing the emission of the excitation signal. Exemplarily, the receiver can further include: a second signal amplifier connected to the second piezoelectric transducer. The second signal amplifier is used to amplify the original sampling signal output by the second piezoelectric transducer. Exemplarily, various types of filters can also be set between the second signal amplifier and the control device to filter the received original sampling signal. Exemplarily, the control device can also intercept the original sampling signal based on the effective signal interception range to obtain the sampling signal. Exemplarily, the control device can further include a storage medium for storing the obtained sampling signal. Exemplarily, the control device can also have the function of calculating the signal difference coefficient between the sampling signal and the set baseline signal, that is, the control device has the functions of calculating the signal characteristics and comparing the signal differences.

[0095] Next, refer to Figure 6, adjust the temperature of the ice accumulation area to be measured to the set temperature range, and continuously spray liquid on the ice accumulation area to form ice with a gradually increasing thickness. That is, place the ice accumulation area to be measured in a low-temperature environment, so that when continuously spraying liquid on the ice accumulation area to be measured, the liquid sprayed on the ice accumulation area to be measured can freeze, thereby forming ice with a gradually increasing thickness in the ice accumulation area to be measured. Exemplarily, the method of continuously spraying liquid on the ice accumulation area to be measured can be: using the method of spraying atomized liquid, which can not only be closer to scenarios such as clouds, but also be beneficial to uniformly thickening the ice thickness at various positions in the ice accumulation area to be measured, so as to be closer to the actual application scenario. Exemplarily, a vacuum pump and a spraying device can be set, so that the nozzle is connected to the vacuum pump and the spraying device, and the nozzle is located about 30 cm above the ice accumulation area to be measured and sprays water vertically downward, and adjust the gas-liquid mixing ratio in the liquid sprayed on the ice accumulation area to be measured to ensure that ice is quickly formed on the aluminum plate surface. Regarding the value of the set temperature range, it can be a single temperature value or a temperature range. Exemplarily, the maximum value of the set temperature range is less than 0 °C, so that the liquid on the ice accumulation area to be measured can freeze. Exemplarily, the environmental temperature where the ice accumulation area to be measured is located can be adjusted to the set temperature range by setting a temperature control device.

[0096] Next, refer to Figure 6 , during the process of continuously spraying liquid on the ice accumulation area to be measured, the following method is also used to obtain the ice thickness change sample set and the first signal difference coefficient change sample set, providing sample data for constructing the first sub-ice thickness calculation model. The acquisition method can be as follows. During the process of continuously spraying liquid on the ice accumulation area to be measured, while periodically obtaining sampling signals through the transmitter-receiver pair, the ice thickness is measured periodically to obtain the ice thickness change sample set. Specifically, the sampling signals can be periodically obtained through the transmitter-receiver pair, and each time a sampling signal is obtained, or within a short time after it, the current ice thickness is measured. It should be noted that the method of measuring the ice thickness in this step is to directly measure the relatively accurate ice thickness through methods such as but not limited to a scale, an ultrasonic detector, etc., rather than measuring the ice thickness based on the first sub-ice thickness calculation model that has not been constructed yet. It should be noted that the ice thickness measured when there is no ice in the ice accumulation area to be measured is 0. As the ice thickness increases, the ice thickness obtained in the ice thickness change sample set is a sequence that generally shows an increasing trend. Refer to Figure 6 , when there is no ice in the ice accumulation area to be measured, the sampling signal obtained through the transmitter-receiver pair can be set as the first baseline signal to be used as the reference parameter for calculating the first signal difference coefficient in the subsequent calculation.

[0097] Next, refer to Figure 6, during the process of continuously spraying liquid on the icing area to be measured, as the icing thickness increases, there will be differences in the sampling signals obtained each time. After obtaining a sampling signal each time, the first signal difference coefficient between the latest obtained sampling signal and the first baseline signal can be calculated to characterize the difference between the latest obtained sampling signal and the first baseline signal, so as to obtain a first signal difference coefficient change sample set.

[0098] Next, referring to Figure 6 , based on the first signal difference coefficient change sample set and the icing thickness change sample set, a first sub-icing thickness calculation model is constructed. The construction method can be implemented by means such as but not limited to fitting algorithms. The fitting model used in the fitting algorithm can be, for example, but not limited to, linear fitting once, non-linear fitting multiple times, etc. Exemplarily, based on the first signal difference coefficient change sample set and the icing thickness change sample set, constructing a first sub-icing thickness calculation model may include: based on the first signal difference coefficient change sample set and the icing thickness change sample set, constructing the following first sub-icing thickness calculation model:

[0099] d ice = SDC1 × ξ1

[0100] where, d ice represents the icing thickness; SDC1 represents the first signal difference coefficient; ξ1 represents the first thickness conversion coefficient obtained by fitting. It can be seen that the first sub-icing thickness calculation model shown above is a linear fitting model. The reason for adopting the linear fitting model as the first sub-icing thickness calculation model in this embodiment is that this application has discovered the rule that "the first signal difference coefficient calculated by the above method also increases linearly as the icing thickness increases". Based on this discovery, this embodiment innovatively proposes an icing thickness calculation model to achieve the accuracy of quantitative detection of icing thickness.

[0101] Exemplarily, referring to Figure 7 , the construction method may further include: as the icing thickness increases, when the first signal difference coefficient is greater than the first set value, setting the latest obtained sampling signal as the second baseline signal; after obtaining a sampling signal each time subsequently, calculating the second signal difference coefficient between the latest obtained sampling signal and the second baseline signal to obtain a second signal difference coefficient change sample set; based on the second signal difference coefficient change sample set and the icing thickness change sample set, constructing a second sub-icing thickness calculation model.

[0102] In the above embodiments, as the ice accretion thickness increases, when the first signal difference coefficient is greater than the first set value, the latest acquired sampling signal is set as the second baseline signal; and subsequently, the second signal difference coefficient between the latest acquired sampling signal and the second baseline signal is calculated to obtain a second signal difference coefficient change sample set. Since the above method is still used to periodically measure the ice accretion thickness during this process, the ice accretion thickness change sample set obtained also includes the ice accretion thickness corresponding to the sampling signal used when calculating the second signal difference coefficient. Subsequently, a second sub-ice accretion thickness calculation model can be constructed based on the second signal difference coefficient change sample set and the ice accretion thickness change sample set. The reason for adopting the above method is that the present application discovers that when the first signal difference coefficient calculated based on the previously set first baseline signal is relatively large, the relationship between the first signal difference coefficient and the change in ice accretion thickness no longer satisfies the linear fitting relationship. Based on the above discovery, the present application, when the first signal difference coefficient is greater than the first set value, resets a new baseline signal (specifically, sets the latest acquired sampling signal as the second baseline signal), and reconstructs the second sub-ice accretion thickness calculation model based on the newly acquired second signal difference coefficient change sample set and the ice accretion thickness change sample set. During subsequent application, when the first signal difference coefficient is not greater than the first set value, the ice accretion thickness can be detected based on the first sub-ice accretion thickness calculation model. When the first signal difference coefficient is greater than the first set value, the ice accretion thickness is detected based on the second sub-ice accretion thickness calculation model. The detailed method can be referred to the description of the embodiment part of the following ice accretion thickness detection method.

[0103] Regarding the method of constructing the second sub-ice accretion thickness calculation model based on the second signal difference coefficient change sample set and the ice accretion thickness change sample set, it can adopt methods such as but not limited to linear fitting, non-linear fitting, etc. Some methods are introduced exemplarily as follows. Exemplarily, constructing the second sub-ice accretion thickness calculation model based on the second signal difference coefficient change sample set and the ice accretion thickness change sample set may include: constructing the following second sub-ice accretion thickness calculation model based on the second signal difference coefficient change sample set and the ice accretion thickness change sample set:

[0104] d ice =(SDC2 + α2)×ξ2

[0105] Wherein, SDC2 represents the second signal difference coefficient; α2 represents a preset second cumulative coefficient; ξ2 represents a second thickness conversion coefficient obtained by fitting.

[0106] In the above embodiments, the present application discovers that: after resetting the second baseline signal, the sum of the calculated second signal difference coefficient and a pre-set second cumulative coefficient still satisfies a linear fitting relationship with the ice accretion thickness. Only compared with the linear first sub-ice accretion thickness calculation model, in the second sub-ice accretion thickness calculation model, it is necessary to multiply the sum of the second signal difference coefficient and the pre-set second cumulative coefficient by the second thickness conversion coefficient obtained by fitting. Subsequently, after the first signal difference coefficient is greater than the first set value, the above method can be used to reset the second baseline signal, calculate the second signal difference coefficient based on the latest acquired sampling signal and the second baseline signal, and calculate the ice accretion thickness based on the second sub-ice accretion thickness calculation model, so as to achieve the accuracy of quantitative detection of the ice accretion thickness. The detection method can refer to the description of the embodiment part of the following ice accretion thickness detection method.

[0107] Exemplarily, referring to Figure 8 , the construction method may further include: as the ice accretion thickness increases, when the i-th signal difference coefficient is greater than the i-th set value, setting the latest acquired sampling signal as the (i + 1)-th baseline signal; where i is any positive integer not less than 1; for the sampling signal acquired after resetting the latest baseline signal, calculating the (i + 1)-th signal difference coefficient based on the latest set (i + 1)-th baseline signal to obtain the (i + 1)-th signal difference coefficient change sample set; and constructing the (i + 1)-th sub-ice accretion thickness calculation model based on the (i + 1)-th signal difference coefficient change sample set and the ice accretion thickness change sample set.

[0108] Specifically, the stage of calculating the i-th signal difference coefficient based on the i-th baseline signal can be defined as the i-th baseline stage. Sequentially, the stage of calculating the (i + 1)-th signal difference coefficient based on the (i + 1)-th baseline signal is defined as the (i + 1)-th baseline stage. In the above construction process, the first baseline stage, the second baseline stage, etc. are experienced in sequence. The judgment condition for entering the next baseline stage is: whether the latest calculated i-th signal difference coefficient in this baseline stage is greater than the i-th set value. Specifically, when the latest calculated i-th signal difference coefficient in this baseline stage is greater than the i-th set value, set the latest acquired sampling signal as the (i + 1)-th baseline signal and enter the next baseline stage. Each baseline stage constructs the sub-ice accretion thickness calculation model of this baseline stage based on the signal difference coefficient change sample set and the ice accretion thickness change sample set obtained in this baseline stage.

[0109] In the above embodiment, the present application found that when the i-th signal difference coefficient newly calculated in the baseline stage is greater than the i-th set value, it is no longer applicable to the sub-ice accretion thickness calculation model fitted when the i-th signal difference coefficient is less than the i-th set value. Therefore, the baseline signal can be reset and a new sub-ice accretion thickness calculation model can be constructed. Thereby improving the accuracy of subsequent ice accretion thickness detection based on the ice accretion thickness calculation model. There are many ways to construct the i+1 sub-ice accretion thickness calculation model based on the i+1 signal difference coefficient change sample set and the ice accretion thickness change sample set. Some methods are introduced as follows. Exemplarily, constructing the i+1 sub-ice accretion thickness calculation model based on the i+1 signal difference coefficient change sample set and the ice accretion thickness change sample set can include: constructing the following i+1 sub-ice accretion thickness calculation model based on the i+1 signal difference coefficient change sample set and the ice accretion thickness change sample set:

[0110]

[0111] Among them, SDC i+1 represents the difference coefficient of the i+1th signal; α j represents the preset j-th cumulative coefficient; ξ i+1 In the above embodiments, the applicant found that after resetting the new baseline signal to enter the new baseline stage, the signal difference coefficient calculated based on the new baseline signal satisfies the linear fitting relationship again. With the advantage that linear fitting can simplify the fitting difficulty and improve the accuracy of fitting, this embodiment fits a new linearly fitted sub-ice accretion thickness calculation model again for the new baseline signal stage, thereby improving the accuracy of subsequent ice accretion thickness detection based on this.

[0112] Exemplary, reference Figure 9, as the ice accretion thickness increases, when i is equal to the preset N and the Nth signal difference coefficient is greater than the Nth set value, the continuous spraying of liquid into the area with ice accretion to be measured is stopped. That is, the number of times of resetting the baseline signal as described above is N times. After resetting the Nth baseline signal as the baseline signal, as the experiment progresses, when the Nth signal difference coefficient calculated based on the Nth baseline signal is greater than the Nth set value, the experiment is stopped. Exemplarily, the above N can be any value not less than 2 such as but not limited to 3, 5, 7, 9, etc. In the above embodiment, the constructed ice accretion thickness calculation model includes the first sub-ice accretion thickness calculation model to the Nth sub-ice accretion thickness calculation model, a total of N sub-ice accretion thickness calculation models. The thickness conversion coefficients and cumulative coefficients in different sub-ice accretion thickness calculation models can be the same or different. Thus, more accurate cumulative coefficients and thickness conversion coefficients can be set for different sub-ice accretion thickness calculation models, thereby improving the quantitative detection accuracy when detecting the ice accretion thickness based on different sub-ice accretion thickness calculation models. It should be noted that the N thickness conversion coefficients in the above N sub-ice accretion thickness calculation models can be equal or not equal. Specifically, among the first thickness conversion coefficient to the Nth thickness conversion coefficient, they are independent of each other and are all obtained by fitting the sample sets acquired based on their corresponding baseline signal stages, thereby improving the accuracy of their respective sub-ice accretion thickness calculation models and improving the accuracy of detecting the ice accretion thickness based on this. Similarly, it should be noted that among the above N sub-ice accretion thickness calculation models, except for the first sub-ice accretion thickness calculation model, the preset N - 1 cumulative coefficients in the other N - 1 sub-ice accretion thickness calculation models can be equal or not equal. Specifically, among the second cumulative coefficient to the Nth cumulative coefficient, they are independent of each other and are all obtained by fitting the sample sets acquired based on their corresponding baseline signal stages, thereby improving the accuracy of their respective sub-ice accretion thickness calculation models and improving the accuracy of detecting the ice accretion thickness based on this. Finally, it should be noted that the N set values (the first set value to the Nth set value) respectively set for the N baseline stages are independent of each other, can be equal or not equal. They are all set based on the sample sets acquired based on their corresponding baseline signal stages, thereby improving the accuracy of whether to switch to the next baseline signal stage.

[0113] Exemplarily, referring to Figure 9 , the construction method may further include: based on the first signal difference coefficient change sample set to the Nth signal difference coefficient change sample set, and the ice accretion thickness change sample set, constructing the following total ice accretion thickness calculation model:

[0114] d ice =(SDC i +α×(i - 1))×ξ

[0115] where SDC iDenote the i-th signal difference coefficient; α denotes the cumulative coefficient; ξ denotes the thickness conversion coefficient. That is, in the above embodiments, based on the first signal difference coefficient change sample set to the N-th signal difference coefficient change sample set, and the icing thickness change sample set, a total icing thickness calculation model applicable to each stage can be constructed, thereby simplifying the number and complexity of the icing thickness calculation models and making it applicable to each stage. It should be noted that the division method between the above-mentioned stages is based on the reset of the baseline signal, which are sequentially the first baseline signal stage, the second baseline signal stage, …, the N-th baseline signal stage. It should be noted that in the total icing thickness calculation model, although the cumulative coefficient α and the thickness conversion coefficient ξ can be applicable to all N baseline signal stages, since there is also a parameter of α×(i - 1) in the total icing thickness calculation model, there are still some changes in the parameters of the icing thickness calculation model used in different baseline signal stages. Regarding the determination method of the above α value, it can adopt various methods. Exemplarily, α can be equal to the average value between α2 and α N . Of course, it should be noted that the determination method of the above α value is not limited to being equal to the average value between α2 and α N . In addition, it can also be other values. Regarding the determination method of the above ξ value, it can adopt various methods. Exemplarily, ξ can be equal to the average value between ξ1 and ξ N . Of course, it should be noted that the determination method of the above ξ value is not limited to being equal to the average value between ξ1 and ξ N . In addition, it can also be other values. For example, it can be a value obtained by fitting based on the first signal difference coefficient change sample set to the N-th signal difference coefficient change sample set, and the icing thickness change sample set. It should be noted that the above-mentioned sub-icing thickness calculation models are not limited to the linear fitting method shown in the above embodiments. In addition, other non-linear fitting methods can also be adopted. Correspondingly, the total icing thickness calculation model is not limited to the linear fitting method shown in the above embodiments. In addition, non-linear fitting methods can also be adopted.

[0116] Exemplarily, referring to Figure 10 , the construction method may further include: setting an effective signal intercept range for obtaining the sampling signal used to calculate the first signal difference coefficient based on the amplitude and / or time distribution of the first baseline signal; while resetting the baseline signal, also resetting the effective signal intercept range for obtaining the sampling signal subsequently based on the amplitude and / or time distribution of the reset baseline signal. In the above embodiments, after each setting of the baseline signal, an effective signal intercept range corresponding to the reset baseline signal is also set, so as to obtain a sampling signal that is more conducive to accurately calculating the icing thickness, thereby improving the detection accuracy of the icing thickness.

[0117] Exemplarily, referring to Figure 10 , the effective signal truncation range corresponding to the i-th baseline signal is: the start and end time range of the first wave packet range on the i-th baseline signal; where i is any positive integer not less than 1. That is, the corresponding effective signal truncation range set for each baseline signal is the range formed between the start time point and the end time point of the first wave packet range on this baseline signal. Thus, based on this effective signal truncation range, the original sampled signal received by the receiver is truncated to obtain the sampled signal. For example, referring to Figure 10 , it respectively shows the effective signal truncation range corresponding to the first baseline signal without ice, the effective signal truncation range corresponding to the baseline signal when the ice accumulation thickness is equal to 0.5 mm, and the effective signal truncation range corresponding to the baseline signal when the ice accumulation thickness is equal to 1 mm. It can be seen that there are differences in the start and end time ranges of the effective signal truncation ranges corresponding to different baseline signals. Of course, it should be noted that preprocessing such as but not limited to denoising and filtering can also be performed on the original sampled signal received by the receiver to prevent errors caused by crosstalk and the like.

[0118] There are various ways to calculate the signal difference coefficient between the sampled signal and the currently set baseline signal. Some ways are introduced in the following embodiments. Exemplarily, calculating the signal difference coefficient between the sampled signal and the currently set baseline signal may include: using the following formula to calculate the signal difference coefficient between the sampled signal and the currently set baseline signal:

[0119]

[0120] where SDC represents the signal difference coefficient. Considering the current i-th baseline signal stage, it can be SDC i .

[0121] s0(t) represents the currently set baseline signal. Considering the current i-th baseline signal stage, it can be

[0122] s1(t) represents the sampled signal. Considering the current i-th baseline signal stage, it can be

[0123] Exemplarily, each of the N set values (the first set value to the N-th set value) respectively set for the N baseline stages can be between 0.8 - 0.95. Specifically, each set value can be any value between 0.8 - 0.95 such as 0.8, 0.85, 0.9, 0.95, etc.

[0124] Exemplarily, referring to Figure 11, shows a schematic diagram of the comparison of 8 linearly fitted sub-ice thickness calculation models and an overall ice thickness calculation model when N=8, where the first to eighth setting values ​​are all equal to 0.9. i +α×(i-1)), and It can be defined as the cumulative SDC value. The red fitting curve can be regarded as: the fitting curve obtained by fitting the cumulative SDC value in the total model for ice thickness calculation. The cumulative SDC value curve segment between two adjacent matching points corresponds to a baseline signal stage, and also corresponds to a sub-ice thickness calculation model, which can be regarded as the cumulative SDC value in the corresponding sub-ice thickness calculation model. It can be seen that each sub-ice thickness calculation model corresponds to a different time period, while the total model for ice thickness calculation corresponds to all time periods. And the cumulative SDC value of each sub-ice thickness calculation model in this time period, and the cumulative SDC value of the total model for ice thickness calculation are within an acceptable range, so it can be obtained that: the ice thickness calculated based on the sub-ice thickness calculation model, and the ice thickness calculated based on the total model for ice thickness calculation, the errors between the two are within an acceptable range.

[0125] The following uses some experimental data and simulation data as examples to prove the credibility of "the first signal difference coefficient calculated in the above manner also increases linearly with the increase of ice thickness" and "and the signal difference coefficient of the new baseline signal stage after resetting the new baseline signal also has a linear corresponding relationship with the ice thickness". An ultrasonic guided wave ice detection prototype experimental scheme is shown as an example below.

[0126] Overall design: Dispersion is a basic characteristic of ultrasonic guided waves propagating in waveguide media. The study of dispersion of ultrasonic guided waves in any complex ice accumulation structure is the basis for analyzing the propagation process of ultrasonic guided waves in ice accumulation structures and the influence of ice layer changes on ultrasonic guided waves. Figure 12 The figure shows the dispersion curve changes corresponding to different ice layer thicknesses on the substrate surface. It can be seen that with the increase of ice layer thickness, the group velocity of a specific mode (such as mode S0) in the low-frequency range gradually decreases. Therefore, changes in ice layer thickness and size will directly affect the arrival time of the guided wave signal, such as Figure 12The figure shows the influence of ice layer size change on guided wave signals. Therefore, the change of ultrasonic guided wave signals can be used as an important indicator for ice accretion detection. This patent intends to develop an ice accretion detection technology for wing surface based on ultrasonic guided waves, using ultrasonic guided waves as a tool for ice accretion detection, and integrating hardware such as a main control unit, a storage unit, a receiving unit, an excitation unit, and an amplification unit. Among them, the excitation unit acts as a transmitter, responsible for generating ultrasonic signals with specific frequencies; the receiving unit acts as a receiver, having a high sampling frequency and amplitude resolution to improve the recognition accuracy; the storage unit is responsible for saving the baseline signal and real-time sampling signals to facilitate subsequent data processing; the main control unit acts as a control device, responsible for signal processing work. A lightweight and efficient piezoelectric transducer is used as the ultrasonic probe in the transmitter and receiver for the conversion of ultrasonic guided wave vibration signals into electrical signals. The specific overall system solution is as Figure 13 shown.

[0127] Structural design: The structural design needs to focus on the stability and anti-interference ability of the system. First, effectively isolate the system's transmitting unit and receiving unit to prevent crosstalk signals generated by high-frequency electromagnetic waves. Use shielded wires to connect the system and the piezoelectric transducer. Inside the structure, install and fix the main control circuit, amplification circuit, and receiving circuit to enhance the system's shock and vibration resistance. The outer surface of the system uses a plastic shell for a closed design to prevent liquid from entering and to provide necessary insulation protection. The overall structure is as Figure 14 shown:

[0128] Introduction to the prototype functions:

[0129] (1) Prototype structural dimensions: The appearance of the ultrasonic guided wave ice accretion detection prototype is as follows Figure 15As shown in the figure, it is divided into two parts: the internal structure and the outer shell. The main structural components are prepared by 3D printing with white photosensitive resin as the material, which ensures a certain structural strength while improving the overall portability. The external dimensions are approximately 282mm×314mm×313mm. The outer shell part includes a top cover and a base. On the front and left sides of the top cover part, two slide rail covers are provided to facilitate the heat dissipation of internal devices and the internal buttons required for operation and debugging. A digital tube display window is reserved in the upper right corner of the front of the outer shell. By installing a digital tube, the icing index (SDC value, signal difference coefficient) can be displayed in real time, which is convenient for quickly judging the current icing situation. One large-size through hole and four small-size through holes are reserved on the right side of the outer shell. The large-size hole is used for some necessary external circuits, including power lines, data transmission lines, and signal lines, etc. The small-size through holes are reserved for terminal blocks. In subsequent improvements, standard terminal blocks can be installed to facilitate the connection of power lines, signal lines, etc., and improve the device integration. The interior contains a three-layer structure. The lower layer is an amplifier structure with a relatively large volume and mass and a relatively high floor height. This layer contains a separate power module and a signal amplification module. The middle layer is the main control layer, which includes an FPGA development board and a DA module. There are debugging buttons on the FPGA development board. The upper layer is other modules, which include two power modules, a small signal amplifier, and an ADC module.

[0130] (2) Hardware functions: The hardware system of the ultrasonic guided wave icing detection prototype includes an FPGA development board, a power module, a DA module, an amplifier, a small signal amplifier, an ADC module, and a digital tube. Among them, the FPGA development board is the central processing module, responsible for generating, collecting, storing, and transmitting signals; there are two power modules with an input voltage of 28V each, which supply power to the small signal amplifier and the FPGA development board respectively; the DA module can convert the data signal provided by the FPGA development board into an analog signal of about ±1.2V and output it to the amplifier; the amplifier can amplify the signal voltage by 50 times and support a maximum voltage output of ±150V; the small signal amplifier is responsible for amplifying the signal collected by the piezoelectric element to about ±500mV, and at the same time has a certain filtering function to avoid the influence of noise; the ADC module converts the amplified analog signal into a digital signal and transmits it to the FPGA development board for storage and subsequent processing and calculation; the digital tube displays the calculated SDC value result under the control of the FPGA board. The connection methods of the above functional modules are as follows Figure 16 As shown:

[0131] (3) Software functions: Refer to Figure 17 and Figure 18, the system software first needs to drive each hardware module to meet the generation, acquisition, storage, and transmission of ultrasonic signals, and then needs to process and calculate the data, and finally output the SDC value or the ice layer size result. The following is the software workflow of the current system. When the power is turned on, the system automatically starts working. The excitation terminal continuously outputs pulse signals, and the signal waveform is a 1-cycle sine signal with a pulse interval of 30 ms. Before icing starts, manually control the system to record the current signal as the baseline signal (ice-free signal) and store it in the SD card of the FPGA board. Then, during the icing process, the system continuously collects signals and records them in the buffer. At this time, through the control of the host computer, the currently collected signal or the baseline signal can be uploaded at any time for debugging work. Finally, calculate the SDC value of the sampled signal and the baseline signal and display it through the digital tube. At the same time, the calculation results will also be uploaded to the host computer in real time through the 422 interface at a frequency of once every 5 s.

[0132] Experimental objective: The ultrasonic guided wave ice accumulation detection prototype test experiment is to conduct basic functional feasibility tests on the basis of the initial prototype, aiming to ensure the availability and compatibility of each main hardware module, as well as the stability of the supporting software functions. At the same time, continuously discover problems and improve the design during the experiment to provide important references for the iterative design of subsequent prototypes.

[0133] Experimental setup: In addition to the prototype body, the equipment required for this experiment also includes: (1) 28V DC power supply. It can provide a low-voltage stable power supply, which is used to simulate the real on-board power supply environment in this experiment and provide ±28V DC power for the detection prototype. (2) Piezoelectric transducer. The piezoelectric transducer is used to complete the signal output and acquisition functions of ultrasonic guided waves. The piezoelectric transducer used is a circular thin sheet with a diameter of φ8 mm and a thickness of 0.5 mm. It is bonded to the surface of the aluminum plate with AB glue. The piezoelectric transducer can realize the mutual conversion between electrical signals and vibration signals, and its applicable range includes the 200 kHz signal required for this experiment, which is the most ideal choice for exciters and sensors. (3) Vacuum pump and sprayer. Connect the vacuum pump and the spraying device to make the nozzle spray water vertically downward at a position about 30 cm above the aluminum plate. Manually adjust the gas-liquid mixing ratio of the spray to ensure that ice droplets are quickly formed on the surface of the aluminum plate. In addition, it is also necessary to connect a computer (host computer) through a 422 interface to USB interface to control most of the software functions of the prototype and receive uploaded data. The experimental setup is as follows Figure 19 and Figure 20 As shown, the piezoelectric chips are at both ends of the aluminum plate, serving as transmitters and receivers respectively, and the middle is the icing area. Ultrasonic guided waves pass through the icing area during propagation and generate signal changes due to the influence of the ice layer.

[0134] Main experimental content: According to the experimental objectives, this experiment mainly focuses on functional testing, specifically including: (1) Acquisition signal analysis: By using the debugging function of the prototype, upload the signals collected by the prototype in real time, and compare the signal changes before and after icing to ensure the correctness of signal acquisition. (2) SDC value calculation and analysis: Collect real-time signals through the prototype and transmit them to the computer. Use MATLAB software to analyze the SDC values corresponding to the real-time signals and compare them with the SDC values output by the prototype to analyze the calculation error. (3) Ice layer detection experiment: Start continuous spraying and icing. At the same time, the prototype collects ultrasonic guided wave signals in real time and calculates the SDC value, records the uploaded data of the prototype, and analyzes the change trend of the SDC value during the icing process. After icing is completed, use a vernier caliper to calculate the current ice thickness and deduce the correlation coefficient between the SDC value and the ice layer thickness.

[0135] Debugging signal analysis of the ultrasonic guided wave ice accumulation detection prototype: Signal acquisition and comparison: The signals collected by the ultrasonic guided wave ice accumulation detection prototype are uploaded to the computer terminal and analyzed using MATLAB software. The results are as follows Figure 21 shown. Among them, there are three groups of signals. The black one is the baseline signal (corresponding to the ice-free situation), and the blue and red ones are the sampling signals when there is ice, and the ice layer thickness increases. From Figure 21 it can be seen that within the time range of 0 - 0.1×10-4s at the front of the three groups of signals, there are exactly the same signals, and the signal arrival time is the same as the excitation start time, that is, the propagation time is about 0. Therefore, this part of the signal should be crosstalk signal and is cut off in subsequent analysis. In the time range of 0.1 - 0.75×10-4s, the signal amplitude fluctuates randomly around 0V, which is a typical noise signal. Here, it can also be seen that the filtering function of the prototype system is good, and the early amplitude of the sampling signal is much smaller than the effective signal. In the time range of about 0.75 - 0.9×10-4s, there is an obvious signal wave packet. According to the arrival time calculation, the corresponding guided wave propagation speed is about 5200m / s, which is close to the theoretical group velocity value of ultrasonic guided wave Mode S0. Therefore, it can be determined that this wave packet corresponds to Mode S0. There is a wave packet with a relatively small amplitude following, and it can also be determined that this wave packet corresponds to Mode A0. The last wave packet is the boundary reflection signal of Mode S0. In practice, in order to avoid the influence of the boundary reflection signal on the subsequent signal analysis process, generally, the faster Mode S0 is used as the effective signal to compare the signal differences. The Figure 21 wave packet corresponding to Mode S0 is intercepted as the effective signal in the above. After amplification, it can be clearly seen that with the appearance of the ice layer, the signal arrival time is delayed, and as the ice layer thickness increases, the signal arrival time delay further increases. Therefore, the change trend of this sampling signal conforms to the theoretical result, which proves the accuracy and usability of the prototype sampling signal.

[0136] Calculation and comparison of signal difference coefficient: The signal difference coefficient (SDC) is used to quantify the difference between the sampled signal and the baseline signal, serving as an indicator of icing severity.

[0137]

[0138] Among them, s1(t) is the real-time sampled signal (after multiple averaging), and s0(t) is the baseline signal. The baseline signal is measured and stored at the start of the equipment operation, and all signals collected during the subsequent icing process are compared with the baseline signal. It should be noted that before calculating the SDC value, the sampled signal needs to be intercepted according to the analysis results of the previous subsection, usually within a range of 301 signal sampling points. The SDC values under different icing conditions are shown in the following table:

[0139] Table 1 Comparison of SDC values for different ice layer thicknesses using an independent amplifier when the ice layer area is 20 cm × 5 cm

[0140]

[0141] Table 2 Comparison of SDC values for different ice layer thicknesses using an integrated amplifier when the ice layer area is 20 cm × 5 cm

[0142]

[0143] Table 3 Comparison of SDC values for different ice layer thicknesses using an integrated amplifier when the ice layer area is 20 cm × 10 cm

[0144]

[0145] Analysis of the SDC values obtained from two different calculation formulas is as follows Figure 22 and Figure 23 as shown. It can be clearly seen that the two results are highly consistent. Considering the possible minor time differences and noise effects during manual signal recording, it can be considered that the SDC values calculated by the prototype are very accurate and meet the expected requirements.

[0146] Analysis of the SDC value of the ultrasonic guided wave ice detection prototype: Data format: According to the project requirements, during the signal acquisition process of the detection prototype, the difference between the current signal and the baseline signal is compared in real time, the SDC value is calculated and output to the host computer, and the transmission frequency is once every 5 s, and 9 data are received each time. Example data: 55AA 00 01 01 68 02 36 5F According to Section 2.3.6 of the "Circuit Technical Requirements for Ultrasonic Guided Wave Detectors", the above example data can be decoded as shown in the following table:

[0147] Table 3 Data format

[0148] 55 Synchronization Word 1 AA Synchronization Word 2 00 Status Word 01 Block Identification Word 01 Manufacturer Identification Code 68 Frame Sequence Number (Cyclic Count from 0 to 255) 02 Detector Status (Including Detector Status, Signal Alarm, Circuit Fault) 36 The First Two Decimal Places after the Decimal Point of the SDC Value 5F Checksum Word (The Complement of the Sum of All Previous Words with Respect to 256)

[0149] Among them, the 7th data is the detector status (including detector status, signal alarm, circuit fault), and the data needs to be converted into binary. Its possible values include the following:

[0150] Indication Converted to Binary Detector Status Signal Alarm Circuit Status 00 000 Normal No Alarm Normal 01 001 Fault No Alarm Normal 02 010 Normal Alarm Normal 03 011 Fault Alarm Normal 04 100 Normal No Alarm Fault 05 101 Fault No Alarm Fault 06 110 Normal Alarm Fault 07 111 Fault Alarm Fault

[0151] Experimental data analysis: Based on the above experimental scheme, multiple consecutive spray icing experiments were carried out, and the data uploaded by the prototype in real time was received by the computer. The data time interval was 5 s, and the format was as described above. Through the decoding and analysis of the continuous data, the following Figure 24 experimental results can be obtained. It can be seen that the results can be divided into three stages: In the first stage (from 1 to 8), the SDC values are all 0. At this time, icing has not started yet, and there is no obvious change in the guided wave signal, and the obtained SDC values are stable; In the second stage (from 9 to 20), the SDC values basically show a rapid linear increase. At this time, continuous spray icing starts on the aluminum plate surface, the ice layer thickness gradually increases with time, and the corresponding SDC values also increase. It can be preliminarily judged that the SDC value is linearly related to the ice layer thickness; In the third stage (from 21 to 42), by manually resetting the baseline signal, the SDC value quickly returns to zero and continues to increase with the increase of the ice layer thickness, but the corresponding fluctuations are greater. There may be two reasons: One is that when the ice layer thickness is large, the temperature of the aluminum plate also changes, and the icing rate is unstable; The other is that as the signal delay increases, the intercepted range of the effective signal does not change, which may lead to incomplete effective signals participating in the calculation and increasing the noise influence.

[0152] In this regard, by using MATLAB to analyze the real-time signal uploaded manually, the algorithm is further analyzed and optimized. As Figure 25 shown is the change of the SDC value with the spray time without changing the baseline signal. The results show fluctuating changes, which are similar to the theoretical results. By changing the algorithm, when the SDC value is greater than 0.8, the current signal is set as the new baseline signal, and at the same time, the starting point of the intercepted signal is reset according to the arrival time of the current signal, and then the subsequent calculation is continued. The change of the SDC value as shown in Figure 26 can be obtained. It can be seen that at this time, the SDC value shows a good monotonic relationship with the spray time, and the spray time determines the ice layer thickness. Therefore, the actual icing situation can be inferred accordingly.

[0153] Calculation of ice layer thickness coefficient: Through the above experiments and signal analysis, it can be known that under the current experimental conditions, the SDC value shows a certain linear relationship with the ice layer thickness, and accurate ice layer parameters can be obtained through simple calculations:

[0154] d ice =(SDC i +α×(i - 1))×ξ

[0155] For example, α can be set to 0.8, and the following formula can be obtained:

[0156] d ice =(SDC i +α×(i - 1))×ξ

[0157] In the formula, d ice represents the ice accretion thickness, i represents the number of times of accumulating the set baseline signal, and ξ represents the thickness conversion coefficient.

[0158] Among them, ξ needs to be determined through experiments. The following Figure 27 is the result of multiple repeated experiments. Each sampling point represents the ice layer thickness of a single experimental result and the corresponding accumulated SDC value (SDC i +α×(i - 1)). It can be seen from Figure 28 that the relationship between the ice layer thickness and the accumulated SDC value of multiple groups of experimental results is relatively stable. Through the fitting result, the corresponding thickness conversion coefficient ξ is 0.602.

[0159] Experimental summary: A series of verification experiments were carried out using the ultrasonic guided wave ice accretion detection prototype. A relatively comprehensive analysis was carried out on the basic functions, detection effects, etc. of the prototype, and the following conclusions were drawn: 1. The basic functions of the prototype are perfect and meet all the indicators in the established plan regarding the "Circuit Technical Requirements for Ultrasonic Guided Wave Detectors"; 2. The software algorithm function and SDC value calculation function of the prototype meet the requirements, and the calculation results are relatively accurate, meeting the needs of ice accretion detection; 3. The prototype can monitor the signal changes in real time during the experiment and output the corresponding SDC value, and its value shows a linear relationship with the spraying time (ice layer thickness) within a certain range. By resetting the baseline signal and the effective signal intercept range, better linear results can be obtained. Therefore, the SDC value calculated by this prototype can be used to evaluate the ice accretion size. 4. It is preliminarily calculated and determined that the thickness conversion coefficient is 0.602. Using this coefficient to evaluate the ice accretion thickness situation during the experiment, the absolute error is between 0 and 0.3 mm, and the error is within the acceptable range.

[0160] Example 4

[0161] This embodiment provides an aircraft, which includes: a body; a skin structure covering the body, wherein the surface of the skin structure has an area where icing is to be measured; and a detection device for any one of the above icing thicknesses. According to the icing thickness detection method, detection device and aircraft provided by the embodiments of the present application, by obtaining a sampling signal when there is no icing in the area where icing is to be measured and setting it as the first baseline signal; subsequently, after obtaining a sampling signal once, calculating a first signal difference coefficient between the latest obtained sampling signal and the first baseline signal; and based on the first signal difference coefficient and an icing thickness calculation model pre-constructed based on the coupling relationship between the signal difference coefficient and the icing thickness, determining the icing thickness of the area where icing is to be measured, realizing the measurement of the thickness dimension of the icing, so as to be able to detect the change of the icing thickness in real time.

Claims

1. A method for detecting the thickness of ice accretion, characterized in that Including: Obtain a sampling signal when there is no ice accumulation in the ice accumulation area to be measured through a transmitter-receiver pair arranged in the ice accumulation area to be measured and whose signal transmission path passes through the ice accumulation area to be measured, and set it as the first baseline signal; Subsequently, after obtaining the sampling signal once through the transmitter-receiver pair, calculate a first signal difference coefficient between the latest obtained sampling signal and the first baseline signal; Based on the first signal difference coefficient and an ice thickness calculation model pre-constructed based on the coupling relationship between the signal difference coefficient and the ice thickness, determine the ice thickness of the ice accumulation area to be measured.

2. The detection method according to claim 1, characterized in that, The determining the ice thickness of the ice accumulation area to be measured based on the first signal difference coefficient and an ice thickness calculation model pre-constructed based on the coupling relationship between the signal difference coefficient and the ice thickness includes: Substitute the first signal difference coefficient into the following first sub-ice thickness calculation model of the ice thickness calculation model to calculate the ice thickness of the ice accumulation area to be measured: d ice = SDC1 × ξ1 where d ice represents the ice accretion thickness; SDC1 represents the first signal difference coefficient; ξ1 represents the first thickness conversion coefficient.

3. The detection method according to claim 2, characterized in that, Also including: Starting from when there is no ice accumulation in the ice accumulation area to be measured, periodically obtain the sampling signal through the transmitter-receiver pair; When the latest calculated first signal difference coefficient is not greater than the first set value, substitute the first signal difference coefficient into the first sub-ice thickness calculation model to calculate the ice thickness of the ice accumulation area to be measured; When the latest calculated first signal difference coefficient is greater than the first set value, set the latest obtained sampling signal as the second baseline signal; Subsequently, after obtaining the sampling signal each time, calculate a second signal difference coefficient between the latest obtained sampling signal and the second baseline signal; Based on the second signal difference coefficient and the ice thickness calculation model, determine the ice thickness of the ice accumulation area to be measured.

4. The detection method according to claim 3, wherein, The determining the ice thickness of the ice accumulation area to be measured based on the second signal difference coefficient and the ice thickness calculation model includes: Substitute the second signal difference coefficient into the following second sub-ice thickness calculation model of the ice thickness calculation model to calculate the ice thickness of the ice accumulation area to be measured: d ice = (SDC2 + α2) × ξ2 Wherein, SDC2 represents the second signal difference coefficient; α2 represents the second accumulation coefficient; ξ2 represents the second thickness conversion coefficient.

5. The detection method according to claim 2, wherein Also including: Starting from when there is no ice accumulation in the ice accumulation area to be measured, periodically obtain the sampling signal through the transmitter-receiver pair; After obtaining the sampling signal each time, calculate an i-th signal difference coefficient between the latest obtained sampling signal and the currently set i-th baseline signal; where i is any positive integer not less than 1; When the i-th signal difference coefficient is greater than the i-th set value, set the latest obtained sampling signal as the (i + 1)-th baseline signal; For the sampling signal obtained after resetting the latest baseline signal, calculate the (i + 1)-th signal difference coefficient based on the latest set (i + 1)-th baseline signal; Substitute the (i + 1)-th signal difference coefficient into the following (i + 1)-th sub-ice thickness calculation model of the ice thickness calculation model to calculate the ice thickness of the ice accumulation area to be measured: Among them, SDC i+1 represents the (i + 1)-th signal difference coefficient; α j represents the pre-set j-th cumulative coefficient; ξ i+1 represents the (i + 1)-th thickness conversion coefficient.

6. The detection method according to claim 5, characterized in that, Further included are: When the i-th signal difference coefficient is not greater than the i-th set value, substitute the i-th signal difference coefficient into the i-th sub-ice thickness calculation model of the ice thickness calculation model to calculate the ice thickness of the ice accumulation area to be measured.

7. The detection method according to claim 1, wherein Further included are: Starting from when there is no ice accumulation in the ice accumulation area to be measured, periodically obtain the sampling signal through the transmitter-receiver pair; After each acquisition of the sampling signal, calculate the i-th signal difference coefficient between the latest acquired sampling signal and the currently set i-th baseline signal; where i is any positive integer not less than 1; If the i-th signal difference coefficient is not greater than the i-th set value, keep the currently set baseline signal unchanged, and substitute the i-th signal difference coefficient into the following total ice thickness calculation model of the ice thickness calculation model to calculate the ice thickness of the ice accumulation area to be measured: d ice =(SDC i +α×(i - 1))×ξ Among them, SDC i represents the i-th signal difference coefficient; α represents the accumulation coefficient; ξ represents the thickness conversion coefficient.

8. The detection method according to claim 7, wherein Further included are: If the i-th signal difference coefficient is greater than the i-th set value, set the latest acquired sampling signal as the (i + 1)-th baseline signal; For the sampling signal acquired after resetting the latest baseline signal, calculate the (i + 1)-th signal difference coefficient based on the newly set (i + 1)-th baseline signal; When the (i + 1)-th signal difference coefficient is not greater than the (i + 1)-th set value, keep the currently set baseline signal unchanged, and substitute the (i + 1)-th signal difference coefficient into the following total ice thickness calculation model to calculate the ice thickness of the ice accumulation area to be measured: d ice = (SDC i+1 + α × i) × ξ Among them, SDC i+1 represents the (i + 1)-th signal difference coefficient.

9. A detection device for the thickness of ice accretion, characterized in that, Included are: A transmitter-receiver pair disposed in the ice accumulation area to be measured and with a signal transmission path passing through the ice accumulation area to be measured, for acquiring a sampling signal; And, A control device, communicatively connected to the transmitter-receiver pair, for performing the ice thickness detection method according to any one of claims 1 to 8.

10. An aircraft, characterized in that, Included are: A body; A skin structure covering the body, wherein the surface of the skin structure has an ice accumulation area to be measured; And, The ice thickness detection device according to claim 9.

Images