CN114487952A - Quench detection system and method using acoustic optical fiber - Google Patents
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Abstract
The invention provides a quench detection system using acoustic fiber, comprising: the system comprises an acousto-optic fiber wire, an optical fiber sound monitor and a voiceprint analysis server; one end of the acoustic optical fiber wire is wound on the outer ring of the superconducting magnet, and the other end of the acoustic optical fiber wire is connected to an optical transceiving port of the optical fiber sound monitor; the data communication port of the optical fiber sound monitor is connected with the voiceprint analysis server; the optical signal that the optic fibre sound monitor sent has partial signal reflection to the optic fibre sound monitor through the rayleigh reflection, and optic fibre sound monitor restores the reflected light signal into sound signal, transmits to the voiceprint analysis server afterwards, carries out the voiceprint analysis to sound signal by the voiceprint analysis server. The voice print recognition method based on the MFCC vector performs voice print recognition through voice print sensing, and has the advantages of high detection precision, strong anti-interference capability, high calculation speed and low maintenance cost.
Description
Technical Field
The invention relates to the technical field of quench detection for a superconducting magnet, in particular to a method for carrying out quench diagnosis on the superconducting magnet by using an acoustic fiber as a sensor and adopting a voiceprint recognition technology.
Background
A major breakthrough in the research of controllable thermonuclear fusion energy is to successfully apply the superconducting technology to the coil generating the Tokamak strong magnetic field. The full-superconducting Tokamak can realize steady-state operation, and lays engineering technology and physical foundation for future steady-state and advanced fusion reactors by greatly improving constraint under steady-state operation conditions. Future commercial stacks must therefore be fully superconducting to achieve steady state operation.
The superconducting magnet system is also one of the most important and most expensive components (accounting for 25 percent of the total cost) of the superconducting fusion device, once the superconducting magnet is quenched, the extremely high electromagnetic heat stored in the superconductor is converted into the heat energy, the quenching protection is not timely, the magnet is irreversibly damaged within a few seconds, the superconducting device cannot operate, and huge economic loss is produced! Therefore, the quench diagnosis and protection system is the highest level of guarantee for ensuring the safe operation of the large superconducting device.
When the magnet is quenched, a series of physical quantities such as resistance value, temperature, pressure and the like can be changed, and the quenching diagnosis is that the physical quantities are converted into electric signals to be used as a quenching judgment basis. The conventional superconducting coil superconducting diagnosis method mainly comprises the following steps: resistance voltage detection, temperature rise detection, pressure detection, flow detection, ultrasonic detection, and the like.
The temperature rise detection method is used for judging the quench by detecting the temperature change or the temperature change rate of the superconducting coil in real time, such as a superconducting energy storage system SMSE (simple substance integration) of a department of Chinese academy of sciences, and the detection method is close to the essence of a physical phenomenon, and is high in detection speed and sensitivity. But face many temperature distribution points, and simultaneously require that the temperature sensor and the subsequent measurement system have very high measurement accuracy and stability.
The pressure and flow detection is also the basis for mainly judging quench, but has the defects of slow response, delay and non-single influence factor of the sensor. Voltage detection methods are more commonly used in quench detection depending on the difficulty of the detection circuit construction. The voltage detection method quench is judged according to the principle that the voltage of a superconducting detection signal exceeds a threshold voltage and the quench is determined to occur after a set delay time.
The most commonly used voltage threshold discrimination methods include bridge methods and co-winding detection methods. The quench detection method utilizes the Wheatstone bridge balance principle to obtain quench detection signals, and has the advantages of convenient use and less quench detection circuits, but on one hand, quench detection blind spots exist, and on the other hand, the anti-electromagnetic interference capability is not strong. The quench detection method is mainly applied to EAST devices of plasma institute of Chinese academy of sciences, and utilizes quench detection sampling circuit formed by co-winding of co-winding and superconducting coils to obtain reference voltage signal, and at the same time utilizes sensors to respectively measure current differential signal of all electrified coils as secondary compensation voltage, and adds the secondary compensation voltage and reference voltage according to a certain compensation coefficient to counteract induction voltage produced by coupling of self coil and other quick alternating coils, and uses the voltage obtained after addition as quench detection quantity. The method can realize quench detection in the environment of rapidly changing magnetic field, but has the defect that once the same winding is broken, the line can not be repaired.
In summary, the conventional quench diagnosis techniques all have certain technical defects, and in particular, in the aspects of quench positioning, quench early warning and quench auxiliary judgment, the importance of quench diagnosis on large-scale superconducting devices is considered, and a more effective diagnosis technique is needed as effective compensation in the prior art.
Disclosure of Invention
In view of the above drawbacks and the improvement needs of the prior art, the present invention provides a method for diagnosing quench of a superconducting magnet by using an acoustic fiber as a sensor and using a voiceprint recognition technique. The superconducting magnet armor has the advantages that the superconducting magnet armor only needs to be laid outside the superconducting magnet armor, is high-voltage resistant and anti-electromagnetic interference, is high in sensitivity, shortens the response time by 20%, is high in true quench discrimination accuracy, and can be used as a quench positioning and auxiliary quench discrimination method.
In order to solve the above technical problem, the present invention provides a quench detection system using an acoustic optical fiber, the system including: the system comprises an acousto-optic fiber wire, an optical fiber sound monitor and a voiceprint analysis server; one end of the acoustic optical fiber wire is wound on the outer ring of the superconducting magnet, and the other end of the acoustic optical fiber wire is connected to an optical transceiving port of the optical fiber sound monitor; the data communication port of the optical fiber sound monitor is connected with the voiceprint analysis server; the optical signal that the optic fibre sound monitor sent has partial signal reflection to the optic fibre sound monitor through the rayleigh reflection, and optic fibre sound monitor restores the reflected light signal into sound signal, transmits to the voiceprint analysis server afterwards, carries out the voiceprint analysis to sound signal by the voiceprint analysis server.
Furthermore, the optical fiber sound monitor comprises a light emitting unit, a light receiving unit, an amplifying unit, a demodulating unit and a communication unit, wherein the light emitting unit emits a light signal according to a certain frequency, the light signal is deformed when encountering a sound wave generated by a magnet in the process of advancing in an optical fiber wire, part of the light signal is reflected back to the optical fiber sound monitor after Rayleigh reflection, and the reflected light signal is obtained after the light signal is received and processed by the light receiving unit and the amplifying unit; the demodulation unit restores the reflected light signal to a sound signal, and then the communication unit transmits the sound signal and the length information to the voiceprint analysis server. The communication unit comprises an Ethernet communication unit, a WIFI module, a 4G module, a 5G module and the like. The voiceprint analysis server firstly performs weighted dimension reduction optimization on the voice signals based on the MFCC characteristic vectors, secondly identifies the optimized voice signals by applying a vector quantization algorithm, and finally judges whether the quench occurs.
The invention also provides a quench detection method using the acoustic fiber, which applies the system and comprises the following steps:
step S1, the optical fiber sound monitor sends out optical signals;
step S2, the optical fiber sound monitor receives the reflected light signal and restores the reflected light signal into a sound signal;
s3, the voiceprint analysis server performs weighted dimension reduction optimization on the voice signal based on the MFCC feature vector;
and step S4, the voiceprint analysis server identifies the optimized voice signal by using a vector quantization algorithm, and finally judges whether the quench occurs.
The step S3 includes a voiceprint preprocessing step including:
step 31, framing the voiceprint signal, wherein the framing relation is expressed as M = n-Lb/[ L (1-b) ], where M is the frame number, n is the audio signal length, L is the frame length, and b is the overlap ratio; preferably, 20ms is taken as the frame length L of one frame, and 40% is taken as the overlapping rate b;
and step 32, respectively calculating MFCC characteristic vectors from the framing signals to form a characteristic vector group, wherein the calculation process comprises FFT (fast Fourier transform), Mel (Mel Filter), logarithmic transformation and discrete cosine transformation.
The step S3 further includes performing dimension reduction optimization on the preprocessed sound signal: specifically, the dimensionality reduction and simplification are carried out on the high-dimensional MFCC vector obtained through calculation through a PCA algorithm; the Mel filtering and PCA algorithm are described in detail in the description of the embodiments.
Further, the voiceprint analysis server analyzes the target signal to obtain a dimension-reduced optimized target MFCC feature vector v, v1, …, vh, variance contribution rate and accumulated variance contribution rate, and inputs the feature vector v, v1, …, vh, variance contribution rate and accumulated variance contribution rate into a trained vector quantization algorithm, and finally obtains a quench determination result.
The detection system and the detection method provided by the invention can be used for carrying out superconducting quench detection based on the voiceprint recognition technology, the detection system and the superconducting system are relatively independent, faults cannot occur in the superconducting system, and the overhaul cost is low; meanwhile, the invention carries out comprehensive judgment through the MFCC vector and the neural network, can extract the depth information of the fault, and has high detection precision and strong anti-interference capability; finally, in the voiceprint analysis process, the invention utilizes dimension reduction optimization, further reduces the calculated amount while ensuring the precision and improves the calculation efficiency.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is a schematic diagram of a system provided by an embodiment of the present invention;
fig. 2 is a voiceprint analysis graph and a current comparison graph of timeout provided by an embodiment of the invention.
Detailed Description
For the purpose of making the present invention more comprehensible, and for the purpose of making the present application more comprehensible, embodiments and advantages thereof, the present invention will be further described with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
The present invention provides a quench detection system using an acoustic optical fiber, as shown in fig. 1, the system including: the system comprises an acousto-optic fiber wire, an optical fiber sound monitor and a voiceprint analysis server; one end of the acoustic optical fiber wire is wound on the outer ring of the superconducting magnet, and the other end of the acoustic optical fiber wire is connected to an optical transceiving port of the optical fiber sound monitor; and a data communication port of the optical fiber sound monitor is connected with the voiceprint analysis server.
The optical fiber sound monitor comprises a light emitting unit, a light receiving unit, an amplifying unit, a demodulating unit and a communication unit, wherein the light emitting unit emits light signals according to a certain frequency, the light signals are deformed (can be regarded as modulation of the sound waves on the light signals) when encountering the sound waves generated by a magnet in the process of advancing in an optical fiber wire, part of the light signals are reflected back to the optical fiber sound monitor through Rayleigh reflection, the reflected light signals are obtained after receiving and processing through the light receiving unit and the amplifying unit, the demodulating unit reduces the reflected light signals into sound signals, then the communication unit transmits the sound signals and length information to a voiceprint analysis server, and the voiceprint analysis server performs voiceprint analysis on the sound signals.
Preferably, the communication unit includes an ethernet communication unit, a WIFI module, a 4G module, a 5G module, and the like.
The voiceprint analysis server firstly performs weighted dimension reduction optimization on the voice signals based on the MFCC characteristic vectors, secondly identifies the optimized voice signals by applying a vector quantization algorithm, and finally judges whether the quench occurs.
Further, the invention also provides a quench detection method using the acoustic optical fiber, which relies on the quench detection system, and the method comprises the following steps:
step S1, the optical fiber sound monitor sends out optical signals;
step S2, the optical fiber sound monitor receives the reflected light signal and restores the reflected light signal into a sound signal;
s3, the voiceprint analysis server performs weighted dimension reduction optimization on the voice signal based on the MFCC feature vector;
and step S4, the voiceprint analysis server identifies the optimized voice signal by using a vector quantization algorithm, and finally judges whether the quench occurs.
Preferably, the weighted dimension reduction optimization of the sound signal comprises voiceprint preprocessing and dimension reduction optimization.
Firstly, a voiceprint preprocessing step: the voiceprint preprocessing comprises two substeps of framing and windowing.
(1) When framing a voiceprint signal, two frames are generally overlapped to ensure continuity between two adjacent frames. The framing relationship can be expressed as M = n-Lb/[ L (1-b) ],
where M is the number of frames, n is the audio signal length, L is the frame length, and b is the overlap ratio.
When detecting quench with voiceprint, it is preferable to take 20ms as the frame length L and 40% as the overlap rate b.
(2) The preprocessed signals need discrete Fourier transform, specifically, a Hamming window is applied to each frame signal and then discrete Fourier transform is performed to increase continuity of two ends of the signals, so that distortion caused by Fourier transform is reduced.
Wherein the MFCC coefficients are based on cepstral coefficients of the Mel frequency domain, wherein the Mel frequency is a frequency domain transformed according to the human auditory perception characteristics: b (h) =25951g (1 h/700).
And respectively solving MFCC characteristic vectors from the frame signals to form a characteristic vector group, wherein the solving process comprises FFT (fast Fourier transform), Mel filtering, logarithmic transformation and discrete cosine transformation.
Furthermore, the Mel filtering is implemented by a filter bank consisting of a plurality of triangular band-pass filters. Setting the number of filters as p, obtaining p parameters after filtering the signal
The calculation formula is as follows:
n is the number of FFT points, X (k) is the FFT of the preprocessed framed signal, Hi(k) As filter parameters, it can be expressed as:
wherein: f [ i ] is the center frequency of the triangular filter;
and after mi is obtained through calculation, taking a logarithm of the mi, performing discrete cosine transform, and obtaining a result c (i) which is the MFCC feature vector of the framing signal.
Secondly, dimension reduction and optimization: in analyzing the sound signal, the higher MFCC feature vector dimension can ensure sufficient extraction of the sound signal features, but too high dimension also consumes a lot of computation time and increases the computational complexity. In order to improve the calculation efficiency, the calculated high-dimensional MFCC vector is subjected to dimensionality reduction and simplification through a PCA algorithm, and meanwhile, the accuracy of the features is ensured.
The PCA algorithm is as follows:
(1) e eigenvectors are set to form a matrix G, the dimension of each eigenvector is h, and G can be expressed as:
(2) calculating a correlation matrix R of G as
，
The eigenvalue of the correlation matrix R is calculated according to the above
And corresponding feature vectors
。
(3) Calculating variance contribution rate and accumulated variance contribution rate, which are respectively:
by means of vector quantization algorithm prediction of the feature data and the audio features during quench, the occurrence of quench can be accurately judged.
By combining the voiceprint analysis chart and the current comparison chart of the quench time shown in fig. 2, experiments prove that the detection system can accurately judge the quench occurrence based on the analysis of the voiceprint.
At the same time, it was also observed experimentally: at present, the interference of the magnetic field strength to the sensor is tested, but the interference strength is kept at a lower level, the influence on the receiving of a normal voiceprint signal is avoided, and the optical fiber is not interfered by the change of the magnetic field at all, so that the anti-interference capability of the application is further verified.
Further, the vector quantization algorithm is obtained by a neural network training, and comprises the following processes:
selecting a voiceprint training set, executing the first step and the second step on the training set to obtain an MFCC feature vector, a variance contribution rate and an accumulated variance contribution rate after dimension reduction optimization, and calibrating the training set;
secondly, training a machine learning model by taking the MFCC characteristic vectors (v, v1, …, vh) after the dimensionality reduction optimization of the training set, the variance contribution rate and the accumulated variance contribution rate as input and taking the corresponding quench result as output to obtain a trained vector quantization algorithm;
and (III) inputting the dimensionality-reduced optimized target MFCC feature vector, the variance contribution rate and the accumulated variance contribution rate which are obtained by analyzing the target signal by the voiceprint analysis server into a vector quantization algorithm, and finally obtaining a quench judgment result.
The invention also provides various programmable processors (FPGA, ASIC or other integrated circuits) for running programs, wherein the steps in the above embodiments are performed when the programs are run.
The invention also provides corresponding computer equipment, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the steps in the embodiment are realized when the memory executes the program.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the scope of the present invention should be determined by the following claims.
Claims (10)
1. A quench detection system utilizing an acoustic fiber, the system comprising: the system comprises an acousto-optic fiber wire, an optical fiber sound monitor and a voiceprint analysis server; one end of the acoustic optical fiber wire is wound on the outer ring of the superconducting magnet, and the other end of the acoustic optical fiber wire is connected to an optical transceiving port of the optical fiber sound monitor; the data communication port of the optical fiber sound monitor is connected with the voiceprint analysis server;
the optical signal that the optic fibre sound monitor sent has partial signal reflection to the optic fibre sound monitor through the rayleigh reflection, and optic fibre sound monitor restores the reflected light signal into sound signal, transmits to the voiceprint analysis server afterwards, carries out the voiceprint analysis to sound signal by the voiceprint analysis server.
2. The system of claim 1, wherein: the optical fiber sound monitor comprises a light emitting unit, a light receiving unit, an amplifying unit, a demodulating unit and a communication unit,
the optical signal is deformed when encountering the sound wave generated by the magnet in the process of moving in the optical fiber wire, the optical signal is reflected back to the optical fiber sound monitor by Rayleigh reflection and part of the signal is received by the optical receiving unit and the amplifying unit to obtain a reflected optical signal;
the demodulation unit restores the reflected light signal to a sound signal, and then the communication unit transmits the sound signal and the length information to the voiceprint analysis server.
3. The system of claim 2, wherein: the communication unit comprises an Ethernet communication unit, a WIFI module, a 4G module, a 5G module and the like.
4. The system of any one of claims 1-3, wherein: the voiceprint analysis server firstly performs weighted dimension reduction optimization on the voice signals based on the MFCC characteristic vectors, secondly identifies the optimized voice signals by applying a vector quantization algorithm, and finally judges whether the quench occurs.
5. A quench detection method using an acoustic fiber, using the system according to any of claims 1-4, characterized in that the method comprises the steps of:
step S1, the optical fiber sound monitor sends out optical signals;
step S2, the optical fiber sound monitor receives the reflected light signal and restores the reflected light signal into a sound signal;
s3, the voiceprint analysis server performs weighted dimension reduction optimization on the voice signal based on the MFCC feature vector;
and step S4, the voiceprint analysis server identifies the optimized voice signal by using a vector quantization algorithm, and finally judges whether the quench occurs.
6. The method according to claim 5, wherein the step S3 includes a voiceprint preprocessing step, the voiceprint preprocessing step including:
step 31, framing the voiceprint signal, wherein the framing relation is expressed as M = n-Lb/[ L (1-b) ], where M is the frame number, n is the audio signal length, L is the frame length, and b is the overlap ratio;
and step 32, respectively calculating MFCC characteristic vectors from the framing signals to form a characteristic vector group, wherein the calculation process comprises FFT (fast Fourier transform), Mel (Mel Filter), logarithmic transformation and discrete cosine transformation.
7. The method of claim 6, wherein: the Mel filtering is realized by a filter bank composed of a plurality of triangular band-pass filters, the number of the filters is p, and p parameters can be obtained after the signals are filtered
The calculation formula is as follows:
n is the number of FFT points, X (k) is the FFT of the preprocessed framed signal, Hi(k) Is the filter parameter, expressed as:
wherein: f [ i ] is the center frequency of the triangular filter;
and after mi is obtained through calculation, taking a logarithm of the mi, performing discrete cosine transform, and obtaining a result c (i) which is the MFCC feature vector of the framing signal.
8. The method according to claim 5 or 6, wherein the step S3 further comprises performing dimension reduction optimization on the preprocessed sound signals: specifically, the dimensionality reduction and simplification are carried out on the high-dimensional MFCC vector obtained through calculation through a PCA algorithm;
the PCA algorithm is as follows:
(1) e eigenvectors are set to form a matrix G, the dimension of each eigenvector is h, and G can be expressed as:
(2) calculating a correlation matrix R of G as
；
The eigenvalue of the correlation matrix R is calculated according to the above
And corresponding feature vectors
；
(3) Calculating variance contribution rate and accumulated variance contribution rate, which are respectively:
9. the method of claim 8, wherein the dimension-reduced optimized target MFCC feature vectors v, v1, …, vh, the variance contribution rate and the accumulated variance contribution rate obtained by analyzing the target signal by the voiceprint analysis server are input into a trained vector quantization algorithm, and finally the quench determination result is obtained.
10. The method of claim 7, wherein 20ms is taken as a frame length L, and 40% is taken as an overlap ratio b.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116741493A (en) * | 2023-08-09 | 2023-09-12 | 中国科学院合肥物质科学研究院 | Fusion reactor superconducting magnet system |
| CN117590300A (en) * | 2023-11-21 | 2024-02-23 | 兰州大学 | Superconducting magnet quench detection system and method based on distributed optical fiber acoustic wave sensing |
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