CN208156464U - Fluorescence temperature demodulating system based on radial base neural net - Google Patents
Fluorescence temperature demodulating system based on radial base neural net Download PDFInfo
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- CN208156464U CN208156464U CN201820556309.2U CN201820556309U CN208156464U CN 208156464 U CN208156464 U CN 208156464U CN 201820556309 U CN201820556309 U CN 201820556309U CN 208156464 U CN208156464 U CN 208156464U
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Abstract
The fluorescence temperature demodulating system based on radial base neural net that the utility model discloses a kind of, it is compared by the data that master controller acquires fluorescence temperature (FBG) demodulator and insulating box, and contrast signal is forwarded to PC machine, data are handled in PC machine, finally obtain the radial base neural net for meeting production requirement, and it is burned onto fluorescence temperature (FBG) demodulator, this system can be realized datamation acquisition, program batch is downloaded, test of product performance, it accomplishes scale production, it solves calibration process and needs to expend a large amount of manpower financial capacity's material resources, production efficiency is low, problem with high costs.
Description
Technical field
The utility model belongs to fibre optical sensor field, and in particular to a kind of fluorescence temperature based on radial base neural net
Demodulating system.
Background technique
Fluorescence thermometry has high temperature resistant, electromagnetism interference, high-voltage isulation, size is small, high sensitivity and service life are long etc.
The incomparable advantage of electric sensing technology obtains extensive concern and research.Nowadays the commercialization application stage is stepped into, is answered
For industries such as high-voltage electrical equipment, Microwave Industry, heat treatment unit, petrochemical industry, aerospaces.However, by more than 30 years
Development, fluorescence thermometric product is still small range, small-scale application, and application is not achieved in mainly measurement accuracy of tracing it to its cause
Demand, calibration process are cumbersome, at high cost and system stability is to be improved.
Fluorescence temperature-measurement principle be based on certain physical attribute of fluorescent material itself and the relationship of temperature, as fluorescence intensity with
Fluorescence lifetime carries out temperature measurement using fluorescence lifetime because its apparent advantage has been increasingly becoming major way.According to glimmering
The generality of flash ranging temperature is theoretical, and phosphorescence afterglow curve is exponential curve, twilight sunset constant and excitation light source intensity, coupling efficiency, light
Other variables of the systems such as fine length, loss are unrelated, but find in practicing its influence factor be it is various, such as pop one's head in consistent
Property, channel characteristic, light source drift, environmental change etc. can all influence temperature measurement accuracy and stability.Thus, practical decay curve is presented
Non-exponential dynamic trend out.
To solve the above-mentioned problems, 2013104125995 (Authorization Notice No. of Patent No.：CN103428982B China)
Utility model patent《A kind of Self-adjustment of luminous intensity circuit for fluorescence optical fiber excitation》It is middle to use feedback control technology, pass through control
The electric current that illuminator flows through, adjust automatically light intensity export constant light intensity in scheduled range, eliminate light-intensity variation to measurement
Interference, but motivating light intensity is one of influence factor, fails the influence for eliminating other external factor；Jia Dan equality people exists《Fluorescence
The non-exponential component of twilight sunset and processing》Changing the non-exponential of decay curve in article is approximately multi index option function superposition, is used
Each service life of Prony method split cavity oscillator twilight sunset, but solution procedure is complicated, it is more demanding to hardware device, still remain calibration
Difficult, production capacity deficiency problem.Above-mentioned improvement project improves the precision and stability of fluorescence temperature measurement system to a certain extent, but all
It is not system total solution with one-sidedness.
Summary of the invention
Purpose of the utility model is to overcome the above-mentioned shortcomings and provide a kind of fluorescence temperatures based on radial base neural net
Demodulating system solves the problems, such as that calibration process of the existing technology is cumbersome, production capacity is insufficient, at high cost, precision and stability is not high.
In order to achieve the above object, the fluorescence temperature demodulating system based on radial base neural net, including master controller, it is main
Controller connection fluorescence temperature (FBG) demodulator, insulating box and PC machine, fluorescence temperature (FBG) demodulator and PC machine are all connected with CD writers, fluorescence temperature
It spends (FBG) demodulator and connects insulating box, CD writers connect several burning bases；
Master controller is used to acquire fluorescence temperature (FBG) demodulator and the data of insulating box compare, and contrast signal is forwarded
To PC machine；
PC machine is used to screen qualified radial base neural net, and through CD writers burning in fluorescence temperature (FBG) demodulator.
Fluorescence temperature (FBG) demodulator accesses insulating box by fluorescent probe.
Master controller includes control module, is had in control module for connecting the cd-rom recorder interface of cd-rom recorder and for connecting
The Host Controler Interface of PC machine is connect, control module connects optical system by two paths of signals, and control module passes through on signal all the way
Optical system is connected after the excitation light drive module of setting, control module passes through at the data being disposed on another way signal
Optical system is connected after reason module and fluorescence signal detecting module, fluorescence of the optical system connection for connecting with insulating box is visited
Head, control module connect display.
Fluorescence temperature (FBG) demodulator includes CPU, on CPU there is insulating box interface, fluorescence temperature (FBG) demodulator interface, display to connect
Mouth and PC machine interface.
The CPU of master controller uses STM32F103C8T6.
Master controller passes through serial bus interface and RS485 conversion circuit and insulating box, fluorescence temperature (FBG) demodulator and PC machine
It is connected.
Compared with prior art, the system of the utility model acquires fluorescence temperature (FBG) demodulator and insulating box by master controller
Data compare, and contrast signal is forwarded to PC machine, data is handled in PC machine, finally obtain satisfaction production
It is required that radial base neural net, and be burned onto fluorescence temperature (FBG) demodulator, this system can be realized datamation acquisition, journey
Sequence batch is downloaded, and test of product performance is accomplished scale production, and is solved calibration process and is needed to expend a large amount of manpower financial capacity's material resources,
Production efficiency is low, problem with high costs.
Detailed description of the invention
Fig. 1 is the system principle diagram of the utility model；
Fig. 2 is the master controller functional block diagram of the utility model；
Fig. 3 is the fluorescence temperature (FBG) demodulator functional block diagram of the utility model；
In figure：1- insulating box, 2- fluorescence temperature (FBG) demodulator, 21- fluorescent probe, 22- optical system, 23- fluorescence signal are visited
Survey module, 24- data processing module, 25- excitation light drive module, 26- control module, 27- cd-rom recorder interface, 28- main control
Device interface, 29- display, 3- master controller, 31- insulating box interface, 32- fluorescence temperature (FBG) demodulator interface, 33-CPU, 34- are aobvious
Show device interface, 35-PC machine interface, 4-PC machine, 5- cd-rom recorder, 6- cd-rom recorder seat.
Specific embodiment
The utility model is described in further detail with reference to the accompanying drawing.
Referring to Fig. 1, the fluorescence temperature demodulating system based on radial base neural net, including master controller 3, master controller 3
Connection fluorescence temperature (FBG) demodulator 2, insulating box 1 and PC machine, fluorescence temperature (FBG) demodulator 2 and PC machine are all connected with CD writers 5, fluorescence temperature
(FBG) demodulator 2 connects insulating box 1, if fluorescence temperature (FBG) demodulator 2, which accesses 1 CD writers 5 of insulating box by fluorescent probe 21, connects dry combustion method
Record base 6；
Master controller 3 is for acquiring fluorescence temperature (FBG) demodulator 2 and the data of insulating box 1 compare, and by contrast signal
It is forwarded to PC machine；
PC machine is used to screen qualified radial base neural net, and through CD writers burning in fluorescence temperature (FBG) demodulator 2.
Referring to fig. 2, master controller 3 includes control module 26, has the burning for connecting cd-rom recorder 5 in control module 26
Device interface 27 and Host Controler Interface 28 for connecting PC machine, control module 26 connect optical system 22 by two paths of signals,
Control module 26 connects optical system 22 after passing through the excitation light drive module 25 being arranged on signal all the way, and control module 26 passes through
Optical system 22, light are connected after the data processing module 24 and fluorescence signal detecting module 23 that are disposed on another way signal
System 22 connects the fluorescent probe 21 for connecting with insulating box 1, and control module 26 connects display 29.
Referring to Fig. 3, fluorescence temperature (FBG) demodulator 2 includes CPU 33, has insulating box interface 31, fluorescence temperature solution on CPU 33
Adjust instrument interface 32, display interface device 34 and PC machine interface 35.
Master controller CPU uses STM32F103C8T6.Master controller 3 passes through serial bus interface and RS485 conversion circuit
Realization is connected with insulating box 1, fluorescence temperature (FBG) demodulator 2 and PC machine 4.
The working method of fluorescence temperature demodulating system based on radial base neural net, including off-line training step and online
It selects and carrys out the stage；
Wherein, off-line training step includes the following steps：
3 operating mode of master controller is set off-line training by step 1, and minimum acquisition temperature is arranged in master controller 3
Degree, highest temperature collection and temperature acquisition interval, when insulating box 1 reaches assigned temperature, master controller 3 send acquisition to
Fluorescence temperature (FBG) demodulator 2 carries out data acquisition, chooses two sections that time interval in phosphorescence afterglow curve is Δ t, seeks voltage respectively
Signal value cumulative and, using the two values as the input of neural network, be sent to PC machine with reference temperature composition data collection, guarantor
Deposit data, and the temperature acquisition interval set according to main controller, it is automatic to carry out acquisition next time until terminating；
Between be divided into two sections of decay curves of Δ t, first starting point time is pulse termination time t1, it is assumed that afterglow intensity
For I0, then it is I that second starting point, which is afterglow intensity value,0T at the time of correspondence when/e2, Δ t value is 5 to 20 sampled points；
Collected data set is done normalized by step 2；
Step 3 is used as test set as training set and 20% for the 80% of data set；
Training set three layers of radial base neural net of input are trained, and save number of results by step 4, initiation parameter
According to；
The training process of radial base neural net includes three aspects：Hidden layer Gaussian kernel is determined according to all input samples
The central value t of functioni, width csj, on the basis of determining implicit layer parameter, hidden layer is sought to defeated using least square method
Connection weight ω between layer outi, specific step is as follows：
4.1, the central value t of radial basis function is sought using k-means clustering algorithmi；ti(n) nth iteration i-th is indicated
A cluster centre, wherein i=1,2,3 ..., I, I value are rule of thumb chosen or randomly choosed；
4.2, initialization；Select I mutually different vectors as initial cluster center ti(O) (i=1,2,3 ..., I)；
4.3, calculate each sample point X of the input spacekWith the Euclidean distance ‖ X of cluster centre pointk-ti(n) ‖ (k=1,2,
3 ..., N), N is total sample number；
4.4, Similarity matching；Enable i*The subscript for representing competition triumph hidden node, to each input sample XkAccording to its with it is poly-
The minimum euclidean distance at class center determines that it sorts out i*(Xk), that is, work as i*(Xk)=min ‖ Xk-ti(n) when ‖, XkIt is classified as i-th*Class,
To which whole samples are divided into I subset U1(n),U2(n),…,UI(n), each subset constitutes one using cluster centre as allusion quotation
The Clustering Domain that type represents；
4.5, update all kinds of cluster centres；It is adjusted using competition learning rule
η is Learning Step, 0<η<1, n value is added 1, goes to third step；
Third step is repeated to the 5th step, until all cluster centres meet formula：|ti(n+1)-ti(n)|<ε, ε are setting
Threshold value, indicate work as cluster centre tiVariation be less than this threshold value when no longer update, at this point, obtained tiAs radial base
The final Basis Function Center of neural network；
4.6, solve width csj；Kernel function using Gaussian function as radial base neural net, then width csjIt can be by formula
It solves：In formula, CmaxMaximum distance between selected center；
4.7, using the connection weight of least square method calculating hidden layer to output layer neuron, calculation formula is as follows：
4.8, according to the cluster centre and width acquired above, gaussian kernel function is expressed as：
4.9, it follows that the output of radial base neural net is：
In formula, ωiConnection weight i.e. between hidden layer and output layer, yiIndicate input sample XkThe reality output of network.
Step 5 tests the radial base neural net after training using test set；
Step 6 is downloaded the trained radial base neural net of PC machine by cd-rom recorder (5) if meeting required precision
Into CPU used in fluorescence temperature (FBG) demodulator 2；
If being unsatisfactory for required precision, increase the data of acquisition, repartition training set and test set, repeats step 3 extremely
Step 6 is until meeting the requirements；
Carry out the stage in line selection to include the following steps：
Step 1, the operating mode of setting master controller 2 are product test, and temperature measurement accuracy is arranged；
Step 2, the data that fluorescence temperature (FBG) demodulator 2 passes through acquisition insulating box 1；
Step 3 passes through the radial base nerve after training using real-time data collection as the input of radial base neural net
Network obtains real time temperature；
Step 4, master controller 3 judge whether 1 temperature of insulating box and 2 temperature measurement accuracy of fluorescence temperature (FBG) demodulator permit in error
Perhaps it in range, meets the requirements, then fluorescence temperature (FBG) demodulator meets the service check before factory；It is undesirable, then it needs to product
The links of production are examined again, comprehensive descision.
The method of the utility model has very strong non-linear mapping capability by radial base neural net, can map and appoint
The non-linear relation for complexity of anticipating solves mathematical models and is difficult to the non-exponential variation that accurate description phosphorescence afterglow curve shows
Problem, and the fault-tolerant ability of radial base neural net and self-learning capability can effectively overcome different fluorescent probes repetition calibration to ask
Topic realizes probe non-calibrating, interchangeable；The used radial base neural net of the utility model avoids compared with BP neural network
Local minimum is fallen into, faster, anti-interference ability is stronger for convergence rate, and approximation accuracy is higher；It, can compared with wavelet neural network
It is difficult to determine that problem, design are more convenient to avoid the number of hidden nodes.
Claims (6)
1. the fluorescence temperature demodulating system based on radial base neural net, which is characterized in that including master controller (3), main control
Device (3) connection fluorescence temperature (FBG) demodulator (2), insulating box (1) and PC machine (4), fluorescence temperature (FBG) demodulator (2) and PC machine are all connected with burning
Record machine (5), fluorescence temperature (FBG) demodulator (2) connect insulating box (1), and CD writers (5) connect several burning bases (6)；
Master controller (3) is used to acquire fluorescence temperature (FBG) demodulator (2) and the data of insulating box (1) compare, and comparison is believed
Number it is forwarded to PC machine (4)；
PC machine (4) is used to screen qualified radial base neural net, and by CD writers burning in fluorescence temperature (FBG) demodulator (2)
In.
2. a kind of fluorescence temperature demodulating system based on radial base neural net according to claim 1, which is characterized in that
Fluorescence temperature (FBG) demodulator (2) passes through fluorescent probe (21) access insulating box (1).
3. a kind of fluorescence temperature demodulating system based on radial base neural net according to claim 1, which is characterized in that
Master controller (3) includes control module (26), has the cd-rom recorder interface for connecting cd-rom recorder (5) in control module (26)
(27) Host Controler Interface (28) and for connecting PC machine (4), control module (26) connect optical system by two paths of signals
(22), control module (26) connects optical system (22) by the excitation light drive module (25) being arranged on signal all the way afterwards, controls
Molding block (26) passes through the data processing module (24) and fluorescence signal detecting module (23) being disposed on another way signal
Optical system (22) are connected afterwards, optical system (22) connects the fluorescent probe (21) for connecting with insulating box (1), control module
(26) display (29) are connected.
4. a kind of fluorescence temperature demodulating system based on radial base neural net according to claim 1, which is characterized in that
Fluorescence temperature (FBG) demodulator (2) includes (33) CPU, has insulating box interface (31), fluorescence temperature (FBG) demodulator interface on CPU (33)
(32), display interface device (34) and PC machine interface (35).
5. a kind of fluorescence temperature demodulating system based on radial base neural net according to claim 1, which is characterized in that
The CPU of master controller (3) uses STM32F103C8T6.
6. a kind of fluorescence temperature demodulating system based on radial base neural net according to claim 1, which is characterized in that
Master controller (3) passes through serial bus interface and RS485 conversion circuit and insulating box (1), fluorescence temperature (FBG) demodulator (2) and PC machine
(4) it is connected.
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| CN108415336A (en) * | 2018-04-17 | 2018-08-17 | 江苏博亨光电科技有限公司 | Fluorescence temperature demodulating system based on radial base neural net and method |
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