Rotor packing compression amount monitoring method and system of double-water internal-cooling phase modifier

Abstract

The invention discloses a rotor packing compression amount monitoring method and system for a double-water internal-cooling phase modifier. The method comprises the following steps: acquiring a reflection signal of a rotor packing adjusting bolt when a continuous wave emission signal of which the frequency linearly changes along with time is emitted to the rotor packing adjusting bolt of the double-water internal-cooling phase modifier through a radar; filtering the reflected signal; calculating the distance from the radar to a rotor packing adjusting bolt of the double-water internal cooling phase modifier based on the filtered reflection signal; and subtracting the calculated distance from the radar to the rotor packing adjusting bolt of the double-water internal-cooling phase modifier from the distance from the radar to the rotor packing adjusting bolt when the radar and the double-water internal-cooling rotor packing are compressed to the tightest position to obtain the residual compression amount of the rotor packing. The objective of the invention is to improve the real-time performance and accuracy of the compression amount monitoring of the rotor packing of the double-water internal-cooling phase modifier, and realize the accurate monitoring of the compression amount of the rotor packing of the double-water internal-cooling phase modifier.

Description

Technical Field

[0001] The present invention relates to the technical field of phase shifter displacement monitoring, and in particular to a method and system for monitoring the compression of rotor packing of a double water-cooled phase shifter. Background Art

[0002] The rotor packing of the double-water internally cooled condenser is used as the sealing material in the water inlet support (it mainly seals and lubricates the cooling water when it is transferred between the stationary parts and the rotating parts). During operation and maintenance, it is necessary to manually adjust its sealing degree according to the displacement of the support. If it is adjusted too loosely, the displacement will increase, which will bring safety hazards; if it is adjusted too tight, it will cause serious wear and shorten its service life. If the rotor packing is adjusted too tight, the rotor packing will wear too quickly after running for a period of time, and it will not meet the dynamic and static sealing requirements of the rotor water inlet parts. The rotor packing of the double-water internally cooled condenser is cooled by cooling water. The cooling water of the rotor packing flows through the cooling water return hole of the rotor packing and the leakage pipe of the rotor packing into the rotor packing leakage monitoring device. When a large amount of water leaks from the rotor packing, if the rotor water cut-off protection is activated, the condenser will trip and be handled according to the shutdown processing flow. If the cooling water flow of the rotor packing is too large, the adjusting bolts (number A / B / C / D) can be appropriately tightened. The maximum compression of the rotor packing is 6.5mm. If the compression reaches the maximum limit, it is necessary to shut down and replace the rotor packing. The current problem is that the compression of the rotor packing cannot be effectively monitored. When the cooling water flow of the rotor packing is too large, tightening the adjusting bolts can easily cause the packing to be compressed to the maximum limit, resulting in damage. Millimeter waves are electromagnetic waves with a vacuum wavelength of 0.1 to 1.0 cm, and the corresponding frequency range is from 30 to 300 GHz. The unique frequency range of millimeter waves gives it a unique advantage in radar detection. Compared with infrared, visible light and other frequency bands, millimeter waves have better penetration and can easily penetrate snow, smoke, dust, etc., and have the ability to work all day in extreme environments. Compared with low-frequency radio frequency bands, millimeter waves have shorter wavelengths, can obtain better resolution, and the required antenna size is also smaller, which is conducive to miniaturization. Millimeter wave radar has irreplaceable advantages in vehicle-mounted radar, intelligent robots, biometrics recognition, gesture recognition, etc. Continuous wave microwave radar transmits electromagnetic wave signals of specific frequencies and receives echo signals, realizes target detection based on baseband signal processing, and mainly realizes the monitoring of target distance and speed information through time difference calculation and Doppler frequency shift. How to improve the real-time and accuracy of monitoring the compression of the rotor packing of a double-water internally cooled condenser is still a key technical issue that needs to be solved urgently. Summary of the invention

[0003] Technical problem to be solved by the present invention: In view of the above-mentioned problems in the prior art, a method and system for monitoring the compression of the rotor packing of a double-water-cooled phase-shifting calibrator is provided. The present invention aims to improve the real-time and accuracy of monitoring the compression of the rotor packing of a double-water-cooled phase-shifting calibrator, and to achieve accurate monitoring of the compression of the rotor packing of a double-water-cooled phase-shifting calibrator.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A method for monitoring the compression of rotor packing of a double water-cooled phase regulator comprises the following steps:

[0006] Acquire the reflected signal of the rotor packing adjusting bolt when a continuous wave transmission signal with a frequency linearly changing with time is transmitted to the rotor packing adjusting bolt of the double water-cooled phase shifter by a millimeter wave frequency modulated continuous wave FMCW radar;

[0007] Filtering the reflected signal;

[0008] Based on the filtered reflected signal, the distance from the FMCW radar to the rotor packing adjustment bolt of the double water-cooled phase shifter is calculated;

[0009] The remaining compression of the rotor packing is obtained by subtracting the calculated distance between the FMCW radar and the rotor packing adjusting bolts of the double water-cooled phase shifter from the distance between the FMCW radar and the rotor packing adjusting bolts when the rotor packing is compressed to the tightest point.

[0010] Optionally, the function expression for calculating the distance between the frequency modulated continuous wave FMCW radar and the rotor packing adjusting bolt of the double water-cooled phase shifter based on the filtered reflection signal is:

[0011]

[0012] In the above formula, R is the distance from the FMCW radar to the rotor packing adjustment bolt of the double water-cooled phase shifter, c is the speed of light, and T is m is the period of the reflected signal after filtering, B W is the sweep bandwidth, and Δf is the frequency difference between the transmitted signal and the reflected signal.

[0013] Optionally, the filter used to filter the reflected signal is a parameter-adjustable digital filter with an adaptive algorithm, and the function expression of the parameter-adjustable digital filter is:

[0014] y(n)=X T (n)W(n),

[0015] In the above formula, y(n) is the output of the digital filter at time n, X(n) is the input of the digital filter at time n, and W(n) is the weight vector of the digital filter at time n. The weight vector is updated and generated by the adaptive algorithm as an adjustable parameter of the digital filter, and the function expression of the adaptive algorithm to update and generate the weight vector is:

[0016] W(n+1)=W(n)+2μe(n)X(n),

[0017] In the above formula, W(n+1) is the weight vector of the digital filter at time n+1, μ is the step factor, e(n) is the estimated error at time n, e(n)=d(n)-y(n), where d(n) is the expected output value at time n.

[0018] Optionally, the filter used to filter the reflected signal is a parameter-adjustable digital filter with an adaptive algorithm, and the function expression of the parameter-adjustable digital filter is:

[0019] y(n)=X T (n)W(n),

[0020] In the above formula, y(n) is the output of the digital filter at time n, X(n) is the input of the digital filter at time n, W(n) is the weight vector of the digital filter at time n, and the weight vector is updated and generated by an adaptive algorithm as an adjustable parameter of the digital filter, and the adaptive algorithm is a variable step-size LMS adaptive filtering algorithm based on fast Fourier, and updating and generating the weight vector by the variable step-size LMS adaptive filtering algorithm based on fast Fourier includes:

[0021] Get the kth block of input data X(k) and its error E k (K);

[0022] The gradient on the error surface is calculated according to the following formula

[0023]

[0024] In the above formula, IFFT stands for Inverse Fast Fourier Transform, X(k) is the kth block of input data, and E k (K) is the error of the kth block of input data;

[0025] Update the generated weight vector according to the following formula:

[0026]

[0027] In the above formula, W k+1 is the weight vector of the digital filter at time k+1, W k is the weight vector of the digital filter at time k, and μ is the step size factor.

[0028] Optionally, the function expression of the k-th block of input data X(k) is:

[0029] X(k)=[x(kN),x(kN+1),…,x(kN+N-1)] T ,

[0030] In the above formula, x(kN)~x(kN+N-1) are the kNth~kN+N-1th input data respectively.

[0031] Optionally, the error E k The calculation function expression of (K) is:

[0032]

[0033] In the above formula, FFT stands for Fast Fourier Transform, e(k) is the error, 0≤K≤2N-1, N is the number of weight coefficients of the digital filter and the number of data input to the digital filter at one time, and:

[0034] e(k)=[d kN -y kN ,d kN+1 -y kN+1 ,…,d kN+N+1 -y kN+N+1 ] T ,

[0035] In the above formula, d kN ~d kN+N+1 are the expected output values ​​of the kNth to kN+N-1th input data, respectively. kN ~

[0036] y kN+N+1 They are the actual output values ​​of the input data from kN to kN+N-1 respectively.

[0037] Optionally, the updating of the generated weight vector further includes updating the step size factor according to the following formula:

[0038] μ=β{1-exp[-α|e(k)| 2 ]},

[0039] In the above formula, α and β are parameters, and α>0 and β>0.

[0040] In addition, the present invention also provides a rotor packing compression monitoring system for a double water-cooled phase regulator, comprising:

[0041] A data acquisition program unit is used to acquire a reflection signal of a rotor packing adjusting bolt when a continuous wave transmission signal whose frequency changes linearly with time is transmitted to a rotor packing adjusting bolt of a double water-cooled phase shifter by a millimeter wave frequency modulated continuous wave FMCW radar;

[0042] A filter program unit, used for filtering the reflected signal;

[0043] A distance calculation program unit is used to calculate the distance from the frequency modulated continuous wave FMCW radar to the rotor packing adjustment bolt of the double water internal cooling phase regulator based on the filtered reflection signal;

[0044] The compression amount calculation program unit is used to subtract the calculated distance between the FMCW radar and the rotor packing adjusting bolts of the double water-cooled phase shifter from the distance between the FMCW radar and the rotor packing adjusting bolts when the FMCW radar and the double water-cooled phase shifter are compressed to the tightest point, so as to obtain the remaining compression amount of the rotor packing.

[0045] Optionally, the distance calculation program unit calculates the functional expression of the distance from the frequency modulated continuous wave FMCW radar to the rotor packing adjustment bolt of the double water-cooled phase shifter based on the filtered reflection signal as follows:

[0046]

[0047] In the above formula, R is the distance from the FMCW radar to the rotor packing adjustment bolt of the double water-cooled phase shifter, c is the speed of light, and T is m is the period of the reflected signal after filtering, B w is the sweep bandwidth, and Δf is the frequency difference between the transmitted signal and the reflected signal.

[0048] Optionally, the filter used by the filtering program unit to filter the reflected signal is a parameter-adjustable digital filter with an adaptive algorithm, and the function expression of the parameter-adjustable digital filter is:

[0049] y(n)=X T (n)W(n),

[0050] In the above formula, y(n) is the output of the digital filter at time n, X(n) is the input of the digital filter at time n, and W(n) is the weight vector of the digital filter at time n. The weight vector is updated and generated by the adaptive algorithm as an adjustable parameter of the digital filter, and the function expression of the adaptive algorithm to update and generate the weight vector is:

[0051] W(n+1)=W(n)+2μe(n)X(n),

[0052] In the above formula, W(n+1) is the weight vector of the digital filter at time n+1, μ is the step factor, e(n) is the estimated error at time n, e(n)=d(n)-y(n), where d(n) is the expected output value at time n.

[0053] Optionally, the filter used by the filtering program unit to filter the reflected signal is a parameter-adjustable digital filter with an adaptive algorithm, and the function expression of the parameter-adjustable digital filter is:

[0054] y(n)=X T (n)W(n),

[0055] In the above formula, y(n) is the output of the digital filter at time n, X(n) is the input of the digital filter at time n, W(n) is the weight vector of the digital filter at time n, and the weight vector is updated and generated by an adaptive algorithm as an adjustable parameter of the digital filter, and the adaptive algorithm is a variable step-size LMS adaptive filtering algorithm based on fast Fourier, and updating and generating the weight vector by the variable step-size LMS adaptive filtering algorithm based on fast Fourier includes:

[0056] Get the kth block of input data X(k) and its error E k (K);

[0057] The gradient on the error surface is calculated according to the following formula

[0058]

[0059] In the above formula, IFFT stands for Inverse Fast Fourier Transform, X(k) is the kth block of input data, and E k (K) is the error of the k-th block of input data;

[0060] Update the generated weight vector according to the following formula:

[0061]

[0062] In the above formula, W k+1 is the weight vector of the digital filter at time k+1, W k is the weight vector of the digital filter at time k, and μ is the step size factor.

[0063] Optionally, the function expression of the k-th block of input data X(k) is:

[0064] X(k)=[x(kN),x(kN+1),…,x(kN+N-1)] T ,

[0065] In the above formula, x(kN) to x(kN+N-1) are the kNth to kN+N-1th input data respectively; the error E k The calculation function expression of (K) is:

[0066]

[0067] In the above formula, FFT stands for Fast Fourier Transform, e(k) is the error, 0≤K≤2N-1, N is the number of weight coefficients of the digital filter and the number of data input to the digital filter at one time, and:

[0068] e(k)=[d kN -y kN ,d kN+1 -y kN+1 ,…,d kN+N+1 -y kN+N+1 ] T ,

[0069] In the above formula, d kN ~d kN+N+1 are the expected output values ​​of the kNth to kN+N-1th input data, respectively. kN ~y kN+N+1 They are the actual output values ​​of the input data from kN to kN+N-1 respectively.

[0070] Optionally, the updating of the generated weight vector further includes updating the step size factor according to the following formula:

[0071] μ=β{1-exp[-α|e(k)| 2 ]},

[0072] In the above formula, α and β are parameters, and α>0 and β>0.

[0073] In addition, the present invention also provides a rotor packing compression monitoring system for a double water-cooled calibrator, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the rotor packing compression monitoring method for the double water-cooled calibrator.

[0074] In addition, the present invention also provides a computer-readable storage medium, in which a computer program or instruction is stored. The computer program or instruction is programmed or configured to execute the rotor packing compression monitoring method of the double water-cooled phase regulator through a processor.

[0075] In addition, the present invention also provides a computer program product, including a computer program or an instruction, wherein the computer program or the instruction is programmed or configured to execute the rotor packing compression monitoring method of the double water internally cooled phase regulator through a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0077] Figure 1 The figure is a basic flow chart of a method according to a first embodiment of the present invention.

[0078] Figure 2 It is a schematic diagram of the structure of a digital filter with adjustable parameters in Embodiment 1 of the present invention.

[0079] Figure 3 This is a schematic diagram of the principle of detecting a stationary target by a frequency modulated continuous wave radar in Embodiment 1 of the present invention.

[0080] Figure 4 It is the time curve of the intermediate frequency in the first embodiment of the present invention. DETAILED DESCRIPTION

[0081] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Among them, the drawings are only used for illustrative purposes and represent only schematic diagrams, which cannot be understood as limitations on the present invention; in order to better illustrate the embodiments of the present invention, the drawings may be omitted and do not represent the size of the actual product; for those skilled in the art, it is understandable that some descriptions in the drawings may be omitted.

[0082] Embodiment 1:

[0083] like Figure 1 As shown, the rotor packing compression monitoring method of the double water internal cooling phase regulator of this embodiment includes the following steps:

[0084] Acquire the reflected signal of the rotor packing adjusting bolt when a continuous wave transmission signal with a frequency linearly changing with time is transmitted to the rotor packing adjusting bolt of the double water-cooled phase shifter by a millimeter wave frequency modulated continuous wave FMCW radar;

[0085] Filtering the reflected signal;

[0086] Based on the filtered reflected signal, the distance from the FMCW radar to the rotor packing adjustment bolt of the double water-cooled phase shifter is calculated;

[0087] The remaining compression of the rotor packing is obtained by subtracting the calculated distance between the FMCW radar and the rotor packing adjusting bolts of the double water-cooled phase shifter from the distance between the FMCW radar and the rotor packing adjusting bolts when the rotor packing is compressed to the tightest point.

[0088] In this embodiment, there are 4 adjustment bolts (numbered A / B / C / D) distributed on the rotor packing of the double water-cooled phase-shifting calibrator. The transmitting antenna of the millimeter wave frequency modulated continuous wave FMCW radar transmits a continuous wave with a frequency that changes linearly with time to the adjustment bolts (numbered A / B / C / D) of the rotor packing of the double water-cooled phase-shifting calibrator to generate a transmission signal. The receiving antenna of the frequency modulated continuous wave FMCW radar receives the reflected signal of the adjustment bolts (numbered A / B / C / D) of the rotor packing of the double water-cooled phase-shifting calibrator.

[0089] like Figure 2 As shown, the filter used to filter the reflected signal in this embodiment is a parameter-adjustable digital filter with an adaptive algorithm, and the function expression of the parameter-adjustable digital filter is:

[0090] y(n)=X T (n)W(n),

[0091] In the above formula, y(n) is the output of the digital filter at time n, X(n) is the input of the digital filter at time n, and W(n) is the weight vector of the digital filter at time n. The weight vector is updated and generated by the adaptive algorithm as an adjustable parameter of the digital filter, and the function expression of the adaptive algorithm to update and generate the weight vector is:

[0092] W(n+1)=W(n)+2μe(n)X(n),

[0093] In the above formula, W(n+1) is the weight vector of the digital filter at time n+1, μ is the step size factor, e(n) is the estimated error at time n, e(n)=d(n)-y(n), where d(n) is the expected output value at time n. Figure 2 As shown, the parameter-adjustable digital filter with an adaptive algorithm includes two basic processes: 1) the filtering process includes: calculating the response of the linear filter output to the input signal, and generating an estimated error by comparing the output result with the expected response; 2) the adaptive process: automatically adjusting the filter parameters according to the estimated error.

[0094] Figure 3 FIG. 1 is a schematic diagram of the principle of detecting a stationary target by a frequency modulated continuous wave radar in the first embodiment. The frequency modulated continuous wave FMCW radar transmits a continuous wave whose frequency changes linearly with time, and calculates the target distance by the frequency difference between the transmitted wave and the reflected wave. The distance measurement principle is as follows: Figure 3 As shown in the figure, the relationship between the instantaneous frequency and time of the FMCW signal is plotted, with the horizontal axis being time and the vertical axis being the frequency of the signal, and the frequency presenting a triangular waveform over time. The intermediate frequency obtained after mixing represents the target distance. In this embodiment, the function expression for calculating the distance from the frequency modulated continuous wave FMCW radar to the rotor packing adjustment bolt of the double water internally cooled phase regulator based on the filtered reflection signal is:

[0095]

[0096] In the above formula, R is the distance from the FMCW radar to the rotor packing adjustment bolt of the double water-cooled phase regulator, c is the speed of light, and T is m is the period of the reflected signal after filtering, B w is the sweep bandwidth, Δf is the frequency difference between the transmitted signal and the reflected signal, and the frequency difference curve in this embodiment is as follows Figure 4 shown.

[0097] In addition, this embodiment also provides a rotor packing compression monitoring system for a double water-cooled phase regulator, including:

[0098] A data acquisition program unit is used to acquire a reflection signal of a rotor packing adjusting bolt when a continuous wave transmission signal whose frequency changes linearly with time is transmitted to a rotor packing adjusting bolt of a double water-cooled phase shifter by a millimeter wave frequency modulated continuous wave FMCW radar;

[0099] A filter program unit, used for filtering the reflected signal;

[0100] A distance calculation program unit is used to calculate the distance from the frequency modulated continuous wave FMCW radar to the rotor packing adjustment bolt of the double water internal cooling phase regulator based on the filtered reflection signal;

[0101] The compression amount calculation program unit is used to subtract the calculated distance between the FMCW radar and the rotor packing adjusting bolts of the double water-cooled phase shifter from the distance between the FMCW radar and the rotor packing adjusting bolts when the FMCW radar and the double water-cooled phase shifter are compressed to the tightest point, so as to obtain the remaining compression amount of the rotor packing.

[0102] In this embodiment, the filter used by the filter program unit to filter the reflected signal is a parameter-adjustable digital filter with an adaptive algorithm. The function expression of the parameter-adjustable digital filter is:

[0103] y(n)=X T (n)W(n),

[0104] In the above formula, y(n) is the output of the digital filter at time n, X(n) is the input of the digital filter at time n, and W(n) is the weight vector of the digital filter at time n. The weight vector is updated and generated by the adaptive algorithm as an adjustable parameter of the digital filter, and the function expression of the adaptive algorithm to update and generate the weight vector is:

[0105] W(n+1)=W(n)+2μe(n)X(n),

[0106] In the above formula, W(n+1) is the weight vector of the digital filter at time n+1, μ is the step factor, e(n) is the estimated error at time n, e(n)=d(n)-y(n), where d(n) is the expected output value at time n.

[0107] In this embodiment, the distance calculation program unit calculates the functional expression of the distance from the frequency modulated continuous wave FMCW radar to the rotor packing adjustment bolt of the double water internally cooled phase shifter based on the filtered reflection signal:

[0108]

[0109] In the above formula, R is the distance from the FMCW radar to the rotor packing adjustment bolt of the double water-cooled phase shifter, c is the speed of light, and T is m is the period of the reflected signal after filtering, B W is the sweep bandwidth, and Δf is the frequency difference between the transmitted signal and the reflected signal.

[0110] In addition, this embodiment also provides a rotor packing compression monitoring system for a double water-cooled calibrator, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the rotor packing compression monitoring method for the double water-cooled calibrator.

[0111] In addition, this embodiment also provides a computer-readable storage medium, in which a computer program or instruction is stored. The computer program or instruction is programmed or configured to execute the rotor packing compression monitoring method of the double water-cooled phase regulator through a processor.

[0112] In addition, this embodiment also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the rotor packing compression monitoring method of the double water-cooled phase regulator through a processor.

[0113] Embodiment 2:

[0114] This embodiment is a further improvement on the first embodiment. In this embodiment, the filter used to filter the reflected signal is a parameter-adjustable digital filter with an adaptive algorithm. The function expression of the parameter-adjustable digital filter is:

[0115] y(n)=X T (n)W(n),

[0116] In the above formula, y(n) is the output of the digital filter at time n, X(n) is the input of the digital filter at time n, W(n) is the weight vector of the digital filter at time n, and the weight vector is updated and generated by an adaptive algorithm as an adjustable parameter of the digital filter, and the adaptive algorithm is a variable step-size LMS adaptive filtering algorithm based on fast Fourier, and updating and generating the weight vector by the variable step-size LMS adaptive filtering algorithm based on fast Fourier includes:

[0117] Get the kth block of input data X(k) and its error E k (K);

[0118] The gradient on the error surface is calculated according to the following formula

[0119]

[0120] In the above formula, IFFT stands for Inverse Fast Fourier Transform, X(k) is the kth block of input data, and E k (K) is the error of the kth block of input data;

[0121] Update the generated weight vector according to the following formula:

[0122]

[0123] In the above formula, W k+1 is the weight vector of the digital filter at time k+1, W k is the weight vector of the digital filter at time k, and μ is the step size factor.

[0124] Among them, the function expression of the kth block of input data X(k) is:

[0125] X(k)=[x(kN),x(kN+1),…,x(kN+N-1)] T ,

[0126] In the above formula, x(kN)~x(kN+N-1) are the kNth~kN+N-1th input data respectively.

[0127] Among them, the error E k The calculation function expression of (K) is:

[0128]

[0129] In the above formula, FFT stands for Fast Fourier Transform, e(k) is the error, 0≤K≤2N-1, N is the number of weight coefficients of the digital filter and the number of data input to the digital filter at one time, and:

[0130] e(k)=[d kN -y kN ,d kN+1 -y kN+1 ,…,d kN+N+1 -y kN+N+1 ] T ,

[0131] In the above formula, d kN ~d kN+N+1 are the expected output values ​​of the kNth to kN+N-1th input data, respectively. kN ~

[0132] y kN+N+1 They are the actual output values ​​of the input data from kN to kN+N-1 respectively.

[0133] Specifically, the variable step size LMS adaptive filtering algorithm based on the Fast Fourier Transform FFT is used to filter the signal. The Fast Fourier Transform (FFT) algorithm is based on the basic principle that the discrete Fourier transform of a sequence of length N can be decomposed into shorter discrete Fourier transforms for calculation. Assume that the length of the sequence X(n) is N, and satisfies: N = 2 M , M is a natural number. According to the parity of n, X(n) is decomposed into two subsequences of N / 2 points:

[0134] X1(r)=X(2r) r=0,1,…,(N / 2-1),

[0135] X2(r)=X(2r+1) r=0,1,…,(N / 2-1),

[0136] In the above formula, X1(r) and X2(r) are two subsequences of N / 2 points.

[0137] Then the fast Fourier transform FFT of X(n) decimated in time is:

[0138]

[0139]

[0140] In the above formula, X1(k) and X2(k) are the kth points in X1(r) and X2(r), respectively. is the weight coefficient matrix. In this way, the N-point discrete Fourier DFT is decomposed into two N / 2-point discrete Fourier DFT operations. Following the same steps, the fast Fourier algorithm is formed.

[0141] In the variable step size LMS adaptive filtering VSFLMS algorithm based on FFT in this embodiment, the input data sequence is added to the adaptive filter one block at a time. After collecting each block of data samples, a fast Fourier transform is first performed, and then the weights of the adaptive filter are fast Fourier transformed. Next, the output of the filter is calculated, and then the result is inversely transformed to obtain the time domain form. Since the data is input in blocks, each block is N points long (the same as the number of weights of the adaptive filter), and the weight coefficients are updated once every N points, and each update is controlled by the superposition result of N error signal samples. The input of the kth block is represented by X(k), which is a real number sequence with a length of N, expressed as:

[0142] X(k)=[x(kN),x(kN+1),…,x(kN+N-1)] t ,

[0143] In the above formula, x(kN)~x(kN+N-1) is input data with a length of N.

[0144] Assume that the number of weight coefficients of the digital filter is also N, and the weight coefficient sequence corresponding to the k-th input data block is:

[0145] w(k)=[w0(k),w1(k),…,w N-1 (k)] T ,

[0146] The output of the filter is also a data block of length N, and the output is expressed as: y(n) (0≤n≤N-1). The k-th block of output y(n) is actually the linear convolution of the k-th block of input with the corresponding weight coefficient w(k), expressed as follows:

[0147]

[0148] In the above formula, Y kN+i is the k-th block output of output y(n), W jk is the weight coefficient w(k) of the kth block, X kN+i+j is the input data of the kth block of input; where 0≤i≤N-1. For a series of blocks, for example the kth block, X(k-1) and X(k) form a 2N point sequence. If i=1 (i.e. the first block), N zeros are added in front of X(1); similarly, the corresponding weight coefficient block w(k) adds N zeros after it to form a 2N point sequence. The following is the weight coefficient vector W with a length of N k (K) Perform a fast Fourier transform after adding N zero extensions:

[0149]

[0150] In the above formula, FFT stands for Fast Fourier Transform, is the weight coefficient vector W of length N k The transpose of (K), where K satisfies 0≤K≤2N-1. The fast Fourier transform of the combination of the k-1th block and the kth block is:

[0151] X k (K) = FFT[X (k-1)N ,X (k-1)N+1 ,…,X kN-1 ,X kN ,X kN+1 ,…,X kN+N+1 ],

[0152] In the above formula, X k (K) is the fast Fourier transform result after the k-1th block and the kth block are combined, X (k-1)N ~X kN+N+1 is the 2N data in the k-1th block and the kth block. Since the frequency domain form of the filter output is calculated, it cannot be directly used to calculate the error and adjust the step size, so the inverse fast Fourier transform is performed here:

[0153] y k (n) = IFFT[X k (i)W k (i)],

[0154] In the above formula, y k (n) is the result of inverse transformation, IFFT is inverse fast Fourier transform, X k (i) and W k (i) are the i-th input and weight coefficient vector respectively. Since the obtained y k (n) is the output of the previous input block and the current block. In fact, only the result of the current block is needed, so the actual output is the second half:

[0155] y(n)=[y kN ,y kN+1 ,…,y kN+N+1 ],

[0156] In the above formula, y kN ~y kN+N+1 for y k (n) The N outputs of the second half, N≤n≤2N-1.

[0157] All updates of block LMS weight coefficients are done after each data block is completely processed. The update here is the cumulative sum of the update amounts of N sample points:

[0158]

[0159] In the above formula, W k+1 is the weight vector of the digital filter at time k+1, W k is the weight vector of the digital filter at time k, μ is the step size factor, is the gradient on the error surface, denoted by Then we have:

[0160]

[0161] Depend on The obtained 2N×1 sequence only needs the first N data, and the value of n is: 0≤n≤N-1. Substituting the above formula into the cumulative sum of the N sample point updates, we get the function expression of the weight coefficient update, that is:

[0162]

[0163] In the above formula, W k+1 is the weight vector of the digital filter at time k+1, W k is the weight vector of the digital filter at time k, μ is the step factor, and substituting the above formula into the cumulative sum of the update amounts of N sample points will obtain the update of the weight coefficient.

[0164] It should be noted that the step factor may be a fixed or variable step factor as required. As an optional implementation, this embodiment also includes updating the step factor according to the following formula:

[0165] μ=β{1-exp[-α|e(k)| 2 ]},

[0166] In the above formula, α and β are parameters, and α>0 and β>0. The specific values ​​of these parameters are determined through experiments.

[0167] The existing algorithms of the adaptive filtering algorithm LMS include the basic LMS algorithm, the variable step size LMS algorithm (SVSLMS), and the transform domain LMS algorithm (FLMS). In order to verify the method of this embodiment, the variable step size LMS adaptive filtering algorithm (VSFLMS) based on fast Fourier adopted in this embodiment is compared with the existing LMS and FLMS methods. The LMS algorithm is based on the minimum mean square error criterion and the steepest descent method to iteratively update the weights to obtain the optimal weights; the transform domain LMS algorithm (FLMS) transforms the time domain signal into the transform domain signal through orthogonal transformation, and adopts the adaptive algorithm in the transform domain, and the results are shown in Table 1.

[0168] Table 1 Comparative experimental results

[0169] Serial number method Error rate / % 1 LMS 2.37 2 FLMS 1.29 3 VSFLMS 0.35

[0170] It can be seen from Table 1 that, compared with the existing LMS and FLMS methods, the rotor packing compression monitoring method (VSFLMS) of the double water-cooled phase-shifting camera in this embodiment has the lowest error rate of 0.35%, indicating that the variable step-size LMS adaptive filtering algorithm (VSFLMS) based on fast Fourier adopted in this embodiment is an effective improvement on the transform domain LMS method.

[0171] In addition, this embodiment also provides a rotor packing compression monitoring system for a double water-cooled calibrator, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the rotor packing compression monitoring method for the double water-cooled calibrator.

[0172] In addition, this embodiment also provides a computer-readable storage medium, in which a computer program or instruction is stored. The computer program or instruction is programmed or configured to execute the rotor packing compression monitoring method of the double water-cooled phase regulator through a processor.

[0173] In addition, this embodiment also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the rotor packing compression monitoring method of the double water-cooled phase regulator through a processor.

[0174] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the functions in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0175] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A method for monitoring the compression of rotor packing of a double water-cooled phase-shifting machine, characterized in that: The steps include: Acquire a reflected signal of a rotor packing adjusting bolt when a continuous wave transmission signal is transmitted to a rotor packing adjusting bolt of a dual water-cooled phase shifter by a millimeter wave frequency modulated continuous wave FMCW radar, wherein the frequency of the continuous wave transmission signal changes linearly with time; Filtering the reflected signal; Based on the filtered reflected signal, the distance from the FMCW radar to the rotor packing adjustment bolt of the double water-cooled phase shifter is calculated; The remaining compression of the rotor packing is obtained by subtracting the calculated distance between the FMCW radar and the rotor packing adjusting bolts of the double water-cooled phase shifter from the distance between the FMCW radar and the rotor packing adjusting bolts of the double water-cooled phase shifter when the rotor packing is compressed to the tightest point.

2. The method for monitoring the compression of rotor packing of a double water-cooled phase-shifting machine according to claim 1, characterized in that: The function expression for calculating the distance from the frequency modulated continuous wave FMCW radar to the rotor packing adjusting bolt of the double water internally cooled phase shifter based on the filtered reflection signal is: In the above formula, R is the distance from the FMCW radar to the rotor packing adjustment bolt of the double water-cooled phase shifter, c is the speed of light, and T is m is the period of the reflected signal after filtering, B w is the sweep bandwidth, and Δf is the frequency difference between the transmitted signal and the reflected signal.

3. The method for monitoring the compression of rotor packing of a double water-cooled phase-shifting machine according to claim 1, characterized in that: The filter used for filtering the reflected signal is a parameter-adjustable digital filter with an adaptive algorithm. The function expression of the parameter-adjustable digital filter is: y(n)=X T (n)W(n), In the above formula, y(n) is the output of the digital filter at time n, X(n) is the input of the digital filter at time n, and W(n) is the weight vector of the digital filter at time n. The weight vector is updated and generated by the adaptive algorithm as an adjustable parameter of the digital filter, and the function expression of the adaptive algorithm to update and generate the weight vector is: W(n+1)=W(n)+2μe(n)X(n), In the above formula, W(n+1) is the weight vector of the digital filter at time n+1, μ is the step factor, e(n) is the estimated error at time n, e(n)=d(n)-y(n), where d(n) is the expected output value at time n.

4. The method for monitoring the compression of rotor packing of a double water-cooled phase-shifting machine according to claim 1, characterized in that: The filter used for filtering the reflected signal is a parameter-adjustable digital filter with an adaptive algorithm. The function expression of the parameter-adjustable digital filter is: y(n)=X T (n)W(n), In the above formula, y(n) is the output of the digital filter at time n, X(n) is the input of the digital filter at time n, W(n) is the weight vector of the digital filter at time n, and the weight vector is updated and generated by an adaptive algorithm as an adjustable parameter of the digital filter, and the adaptive algorithm is a variable step-size LMS adaptive filtering algorithm based on fast Fourier, and updating and generating the weight vector by the variable step-size LMS adaptive filtering algorithm based on fast Fourier includes: Get the kth block of input data X(k) and its error E k (K); The gradient on the error surface is calculated according to the following formula In the above formula, IFFT stands for Inverse Fast Fourier Transform, X(k) is the kth block of input data, and E k (K) is the error of the k-th block of input data; Update the generated weight vector according to the following formula: In the above formula, W k+1 is the weight vector of the digital filter at time k+1, W k is the weight vector of the digital filter at time k, and μ is the step size factor.

5. The method for monitoring the compression of rotor packing of a double water internally cooled phase regulator according to claim 4 is characterized in that: The function expression of the k-th block of input data X(k) is: X(k)=[x(kN),x(kN+1),…,x(kN+N-1)] T , In the above formula, x(kN)~x(kN+N-1) are the kNth~kN+N-1th input data respectively; the error E k The calculation function expression of (K) is: In the above formula, FFT stands for Fast Fourier Transform, e(k) is the error, 0≤K≤2N-1, N is the number of weight coefficients of the digital filter and the number of data input to the digital filter at one time, and: e(k)=[d kN -y kN ,d kN+1 -y kN+1 ,…,d kN+N+1 -y kN+N+1 ] T , In the above formula, d kN ~d kN+N+1 are the expected output values ​​of the kNth to kN+N-1th input data, respectively. kN ~y kN+N+1 They are the actual output values ​​of the input data from kN to kN+N-1 respectively.

6. The method for monitoring the compression of rotor packing of a double water-cooled phase-shifting machine according to claim 4, characterized in that: The updating of the weight vector also includes updating the step size factor according to the following formula: μ=β{1-exp[-α|e(k)| 2 ]}, In the above formula, α and β are parameters, and α>0 and β>0.

7. A rotor packing compression monitoring system for a double water-cooled phase regulator, characterized in that: include: A data acquisition program unit is used to acquire a reflection signal of a rotor packing adjusting bolt when a continuous wave transmission signal whose frequency changes linearly with time is transmitted to a rotor packing adjusting bolt of a double water-cooled phase shifter by a millimeter wave frequency modulated continuous wave FMCW radar; A filter program unit, used for filtering the reflected signal; A distance calculation program unit is used to calculate the distance from the frequency modulated continuous wave FMCW radar to the rotor packing adjustment bolt of the double water internal cooling phase regulator based on the filtered reflection signal; The compression amount calculation program unit is used to subtract the calculated distance between the FMCW radar and the rotor packing adjusting bolts of the double water-cooled phase shifter from the distance between the FMCW radar and the rotor packing adjusting bolts when the FMCW radar and the double water-cooled phase shifter are compressed to the tightest point, so as to obtain the remaining compression amount of the rotor packing.

8. The rotor packing compression monitoring system of a double water internally cooled phase regulator according to claim 7 is characterized in that: The distance calculation program unit calculates the functional expression of the distance from the frequency modulated continuous wave FMCW radar to the rotor packing adjustment bolt of the double water internal cooling phase shifter based on the filtered reflection signal: In the above formula, R is the distance from the FMCW radar to the rotor packing adjustment bolt of the double water-cooled phase shifter, c is the speed of light, and T is m is the period of the reflected signal after filtering, B W is the sweep bandwidth, and Δf is the frequency difference between the transmitted signal and the reflected signal.

9. The rotor packing compression monitoring system of a double water internally cooled phase regulator according to claim 7 is characterized in that: The filter used by the filtering program unit to filter the reflected signal is a parameter-adjustable digital filter with an adaptive algorithm. The function expression of the parameter-adjustable digital filter is: y(n)=X T (n)W(n), In the above formula, y(n) is the output of the digital filter at time n, X(n) is the input of the digital filter at time n, and W(n) is the weight vector of the digital filter at time n. The weight vector is updated and generated by the adaptive algorithm as an adjustable parameter of the digital filter, and the function expression of the adaptive algorithm to update and generate the weight vector is: W(n+1)=W(n)+2μe(n)X(n), In the above formula, W(n+1) is the weight vector of the digital filter at time n+1, u is the step factor, e(n) is the estimated error at time n, e(n)=d(n)-y(n), where d(n) is the expected output value at time n.

10. The rotor packing compression monitoring system of a double water internally cooled phase regulator according to claim 7, characterized in that: The filter used by the filtering program unit to filter the reflected signal is a parameter-adjustable digital filter with an adaptive algorithm. The function expression of the parameter-adjustable digital filter is: y(n)=X T (n)W(n), In the above formula, y(n) is the output of the digital filter at time n, X(n) is the input of the digital filter at time n, W(n) is the weight vector of the digital filter at time n, and the weight vector is updated and generated by an adaptive algorithm as an adjustable parameter of the digital filter, and the adaptive algorithm is a variable step-size LMS adaptive filtering algorithm based on fast Fourier, and updating and generating the weight vector by the variable step-size LMS adaptive filtering algorithm based on fast Fourier includes: Get the kth block of input data X(k) and its error E k (K); The gradient on the error surface is calculated according to the following formula In the above formula, IFFT stands for Inverse Fast Fourier Transform, X(k) is the kth block of input data, and E k (K) is the error of the k-th block of input data; Update the generated weight vector according to the following formula: In the above formula, W k+1 is the weight vector of the digital filter at time k+1, W k is the weight vector of the digital filter at time k, and μ is the step size factor.

11. The rotor packing compression monitoring system of a double water internally cooled phase regulator according to claim 10, characterized in that: The function expression of the k-th block of input data X(k) is: X(k)=[x(kN),x(kN+1),…,x(kN+N-1)] T , In the above formula, x(kN)~x(kN+N-1) are the kNth~kN+N-1th data of the input respectively; The error E k The calculation function expression of (K) is: In the above formula, FFT stands for Fast Fourier Transform, e(k) is the error, 0≤K≤2N-1, N is the number of weight coefficients of the digital filter and the number of data input to the digital filter at one time, and: e(k)=[d kN -y kN ,d kN+1 -y kN+1 ,…,d kN+N+1 -y kN+N+1 ] T , In the above formula, d kN ~d kN+N+1 are the expected output values ​​of the kNth to kN+N-1th input data, respectively. kN ~y fN+N+1 They are the actual output values ​​of the input data from kN to kN+N-1 respectively.

12. The rotor packing compression monitoring system of a double water internally cooled phase regulator according to claim 10, characterized in that: The updating of the weight vector also includes updating the step size factor according to the following formula: μ=β{1-exp[-α|e(k)| 2 ]}, In the above formula, α and β are parameters, and α>0 and β>0.

13. A rotor packing compression monitoring system for a dual water-cooled phase regulator, comprising a microprocessor and a memory connected to each other, characterized in that: The microprocessor is programmed or configured to execute the rotor packing compression monitoring method of a double water-cooled phase regulator according to any one of claims 1 to 6.

14. A computer-readable storage medium having a computer program or instruction stored therein, characterized in that: The computer program or instruction is programmed or configured to execute the rotor packing compression monitoring method of the double water internally cooled phase regulator according to any one of claims 1 to 6 through a processor.

15. A computer program product comprising a computer program or instructions, characterized in that The computer program or instruction is programmed or configured to execute the rotor packing compression monitoring method of the double water internally cooled phase regulator according to any one of claims 1 to 6 through a processor.

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