CN101509604B - Method and device for detecting and assessing deposit in metal pipe - Google Patents
Method and device for detecting and assessing deposit in metal pipe Download PDFInfo
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- CN101509604B CN101509604B CN2009100612100A CN200910061210A CN101509604B CN 101509604 B CN101509604 B CN 101509604B CN 2009100612100 A CN2009100612100 A CN 2009100612100A CN 200910061210 A CN200910061210 A CN 200910061210A CN 101509604 B CN101509604 B CN 101509604B
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Abstract
The invention provides a method for detecting and evaluating deposits in a metal tube and a device thereof. The method comprises the steps: 1) knocking the exciting part of the metal tube through an exciting device to excite acoustic waves capable of reflecting the pulse response characteristics of the structural body; 2) sending and acquiring the excited acoustic wave signals through a microphone and a data acquisition card; 3) recording sounds through an acquisition software and storing the sounds in a computer; 4) carrying out wavelet packet analysis, time domain analysis and/or spectrum analysis on the signals, computing characteristic parameters such as energy value of each spectrum, duration of time domain signal and/or maximum resonance frequency amplitude of spectrogram of the wavelet packet analysis; and 5) inputting the characteristic parameters into a neural network expert diagnosis system for identification, determining whether the deposits exist in the tube or not and quantifying the deposits, and evaluating the plugging degree in the tube according to the quantification result. The device comprises an exciting device, a microphone, and a computer having a data acquisition card, wherein the microphone is fixed at the metal tube, and the microphone and the data acquisition card are connected with the computer host respectively.
Description
Technical field
The present invention relates to a kind of deposit in metal pipe detection and assessment method and equipment therefor, belong to the pipe interior technical field of nondestructive testing.
Background technique
Along with the development of industrial technology, various metallic conduits and equipment increase day by day.For guaranteeing its safe operation, require often to check.Because pipeline and equipment availability environmental limit; Be difficult to directly to carry out inner parts inspect, cause various security incidents thus, as power plant units shut down or start-up course in; Because of generator tube inner wall oxide skin different with the steel base thermal expansion coefficient; Scale is peeled off to stop up and is caused the pipe circulation area to reduce, and makes that elbow is in the overtemperature state for a long time under the boiler tube, causes the boiler pipe explosion accident.Therefore, pressing for the employing appropriate method detects accurately and evaluates bend pipe inside deposit.
At present; The method that detects deposit in the metallic conduit roughly has two kinds: a kind of is to utilize the radiographic inspection method; Because of the interior deposit of metal tube and pipe is the diverse two kinds of materials of structure; When carrying out sensitivity of film, they are different to the Absorbed dose of ray, therefore can on egative film, reflect the stacking states of deposit.Utilize the method for radiography more directly perceived, but its sensitivity is low, is difficult to distinguish definite that for the deposit of negligible amounts especially problem is more outstanding when pipe thickness increases, and omission takes place easily from image.For still attached to the thin layer on the tube wall, uniform coating particularly, the radiographic inspection method almost can't detect.Receive simultaneously and use the restriction of ray irradiation harmfulness, the radiographic inspection detecting method influences other service work, is unfavorable for shortening maintenance duration, influences the business economic benefit, and examined spatial limitation, is difficult to realize complete detection.Another kind is the industrial endoscope method, can carry out the diagnosis and the prevision fault of pipeline and equipment effectively, obtains real video image of pipe interior or image detection information, than the radiographic inspection method accurately, intuitively.But limited by pipeline configuration complexity and caliber size, endoscope can't detect each position; The pipe cutting that also needs for there not being the endoscope inlet passage detects, and belongs to diminishing the detection category.
Summary of the invention
For remedying the deficiency that traditional radiographic inspection and endoscope detect method; The invention provides the detection and the assessment method of deposit in a kind of metal winding pipe; And provide this method used device; Adopt this method and apparatus can effectively solve equipment failure or security incident problem that metal tube inside, particularly metal winding pipe inside cause because of there being deposit.
The technological scheme that realizes the object of the invention is: a kind of detection of deposit in metal pipe and assessment method, and its concrete steps are following:
1) knocks the excitation position of metal tube with exciting bank, motivate the sound wave of the impulse response characteristic that can reflect structural body;
2) with microphone and data collecting card the acoustic signals that motivates is spread out of and gathers;
3) acquisition software recorded voice, and deposit computer in;
4) signal is carried out wavelet packet analysis, time-domain analysis and/or frequency analysis, calculate each band energy value, time-domain signal endurance and/or these characteristic parameters of spectrogram maximum resonant frequency amplitude of wavelet packet analysis;
5) characteristic parameter input neural network expert diagnostic system is discerned, qualitative and quantitative to whether there being deposit to carry out in managing, and based on chocking-up degree in the quantitative result evaluation pipe.
Wherein the concrete steps of neuron network expert diagnostic system identification are: 1) gather training sample, comprise each band energy value and chocking-up degree of time-domain signal endurance, spectrogram maximum resonant frequency amplitude and/or wavelet packet analysis; 2) structure and training network are input to the sample data in the step 1) in the good BP neuron network of structure, network are trained after configuring training parameter, and training will stop automatically when the mean square error of training reaches requirement; 3) adopt the network that trains to carry out diagnostic test, promptly import the characteristic parameter of pipe to be measured, can obtain chocking-up degree in the metal tube.
The present invention also provides the detection and the used device of assessment method of above-mentioned deposit in metal pipe; This device comprises the computer of exciting bank, microphone, band data collecting card; The acquisition software that is used for signal recording, signal processing software and the neuron network expert diagnostic system that is used for signal is carried out wavelet packet analysis, time-domain analysis and/or frequency analysis and calculated characteristics parameter are installed in the computer; Microphone fixing is on metal tube, and microphone links to each other with computer main respectively with data collecting card.
Can know by technique scheme; The detection of deposit in metal pipe of the present invention and assessment method are the sound detecting method of shaking; Be a kind ofly to produce mechanical vibration (sound wave) through the excitation test specimen, the voice signal property of measuring its vibration comes Dynamic Non-Destruction Measurement that detected object is judged.Because present computer and signal processing technology have obtained develop rapidly, science and the accuracy of therefore using the sound method of shaking to detect and evaluate deposit in metal pipe can reach high level.
The detection of deposit in metal pipe provided by the invention and assessment method compared with prior art have following major advantage:
(1) can judge metal tube fast, comprise whether deposit is arranged in the metal winding pipe, and chocking-up degree in the quantitative assessment pipe.
(2) not examined spatial limitation need not to destroy pipeline, is superior to radiographic inspection method and endoscope.
(3) easy and simple to handle, detect accurately, efficiently solve equipment failure or security incident problem that metal winding pipe inside causes because of there being deposit, practical, have a extensive future.
Description of drawings
Fig. 1 is the schematic representation of detection device provided by the invention.
Fig. 2 is an excitation of the present invention position schematic representation.
Fig. 3 is the flow chart that utilizes chocking-up degree in the BP neuron network diagnostic tube of the present invention.
Embodiment
Below in conjunction with embodiment the present invention is done further explain, but the present invention is not limited to the embodiment that provided.Embodiment is a measurand with the metal winding pipe that is difficult at present detect all.
Embodiment 1: as shown in Figure 1; Detection device provided by the invention comprises the computer 3 of exciting bank 1, microphone 2, band data collecting card; The acquisition software that is used for signal recording is installed in the computer 3, is used for signal is carried out wavelet packet analysis, time-domain analysis and/or frequency analysis, and can calculate the signal processing software and the neuron network expert diagnostic system of each band energy value, time-domain signal endurance and/or these characteristic parameters of spectrogram maximum resonant frequency amplitude of wavelet packet analysis.Microphone 2 is fixed on the metal winding pipe, is positioned at the forward entrance position of sound wave, and microphone 2 links to each other with computer main respectively with data collecting card.For motivating the sound wave of the impulse response characteristic that reflects metal winding pipe inner structure body, exciting bank can adopt the steel tup in the present embodiment 1.Excitation position on the metal tube should be that bend pipe is prone to block part, preferentially selects the lower portion A of elbow under the metallic conduit, if detect limited space, can select down elbow side B or position, top C excitation (see figure 2).
Embodiment 2: the detection device with embodiment 1 is following to the concrete steps that deposit in the metal winding pipe detects and evaluates:
1) knocks the lower portion A of metal winding pipe with the steel tup, motivate the sound wave of the impulse response characteristic that can reflect structural body;
2) with microphone and data collecting card the acoustic signals that motivates is spread out of and gathers;
3) use the acquisition software recorded voice, and deposit computer in;
4) signal is carried out wavelet packet analysis, time-domain analysis and/or frequency analysis, calculate each band energy value, time-domain signal endurance and/or these characteristic parameters of spectrogram maximum resonant frequency amplitude of wavelet packet analysis;
5) characteristic parameter input neural network expert diagnostic system is discerned, qualitative and quantitative to whether there being deposit to carry out in managing, and based on chocking-up degree in the quantitative result evaluation pipe.
The flow chart of neuron network expert diagnostic system identification is seen Fig. 3, and its concrete steps are following:
1) gathers training sample: comprise the time-domain signal endurance, spectrogram maximum resonant frequency amplitude, each band energy value and chocking-up degree of wavelet packet analysis.
2) structure and training network are input to the sample data in the step 1) in the good BP neuron network of structure, network are trained after configuring training parameter, and training will stop automatically when the mean square error of training reaches requirement.
3) adopt the network that trains to carry out diagnostic test, promptly import the characteristic parameter of pipe to be measured, just can be managed interior chocking-up degree accordingly from the output vector value.
The BP neuron network that makes up among the present invention is a kind of multilayer feedforward network, can realize from being input to any Nonlinear Mapping of output.Described BP neuron network expert diagnostic system can utilize existing MATLAB software platform to realize, as long as the characteristic parameter of input pipe to be measured just can be managed interior chocking-up degree from the output vector value accordingly.
The spectrogram maximum resonant frequency amplitude that neuron network expert diagnostic system among the present invention can adopt the energy value of each frequency range that WAVELET PACKET DECOMPOSITION obtains, time-domain signal endurance that time-domain analysis obtains and/or frequency analysis to obtain is diagnosed identification as characteristic parameter; The energy value of each frequency range that WAVELET PACKET DECOMPOSITION obtains, time-domain signal endurance, these three kinds of characteristic parameters of spectrogram maximum resonant frequency amplitude can be respectively applied for diagnosis identification; Also can diagnose on the base of recognition as characteristic parameter and comprehensively diagnose identification and/or diagnose identification as characteristic parameter as characteristic parameter, reach the purpose that improves precision with spectrogram maximum resonant frequency amplitude with the time-domain signal endurance at the energy value of each frequency range of using WAVELET PACKET DECOMPOSITION to obtain.The energy value of each frequency range that present embodiment only obtains with WAVELET PACKET DECOMPOSITION is that example is diagnosed identification with BP neuron network expert diagnostic system as characteristic parameter.
Among the present invention, with 8 frequency band energies after 3 layers of WAVELET PACKET DECOMPOSITION as the input node; With the line clogging degree: unimpeded, a small amount of obstruction of pipeline, to stop up more, serious obstruction be that output node is organized neuron network.Selection to output node sees the following form.
| Output unit 1 | Output unit 2 | Meaning |
| 0 | 0 | Pipeline is unimpeded |
| 0 | 1 | The a small amount of obstruction |
| 1 | 0 | Stop up more |
| 1 | 1 | The serious obstruction |
The sample of training network is one group of data of known blocked state; During training network, configure training sample input vector and corresponding output vector after, working procedure in MATLAB; BP network using Levenberg-Marquardt optimized Algorithm; As the training function, network stops after reaching the precision of setting automatically during training with trainlm, adopts the network that trains that sample to be tested is tested at last.
The concrete data of present embodiment are following:
Adopt 30 groups of known chocking-up degree to come training network, 12 groups of signals of the unknown chocking-up degree that test collects.
30 groups of data that are used to train are:
30.0495 18.4179 14.6217 9.604 ?1.2107 2.2465 12.8384 3.0631；
28.2124 16.9243 18.6037 9.3183 1.1545 2.2111 16.9503 3.1863；
30.6729 18.5736 15.4031 9.3168 1.2411 2.2405 13.7539 2.841；
31.5199 16.1455 8.6163 7.4801 1.0547 1.8589 7.2009 2.0322；
29.3098 16.693 ?20.538 9.1304 1.1516 2.1648 18.7914 3.1577；
33.1014 16.9648 5.8163 6.8013 1.062 ?1.7588 3.9724 1.7494；
34.1644 15.4385 9.8281 7.1821 0.9769 1.7512 8.5037 2.0858；
23.6146 12.0159 9.6093 7.2466 0.8723 1.6928 8.3176 2.6076；
22.1163 11.351 ?12.1397 6.7117 0.8908 1.5488 11 2.566；
22.7624 11.6882 12.5572 7.9192 0.8946 1.7671 11.2059 2.9013；
22.7271 1.612 10.9272 7.1139 0.8482 1.7065 9.7939 2.4573；
21.6667 11.113 ?11.095 7.3857 0.868 ?1.694 ?9.9033 2.7154；
19.0865 11.8117 10.6225 8.8218 0.8411 2.0432 9.3954 3.0273；
22.4018 11.6643 10.7664 7.1807 0.8929 1.7129 9.6013 2.6466；
18.4965 9.3241 ?6.894 ?4.9953 0.6971 1.2059 5.9303 1.7506；
20.6543 11.2943 8.6274 5.8514 0.8424 1.4129 7.4631 2.019；
20.3756 11.482 ?7.9329 5.9992 0.8106 1.4381 6.6095 2.1217；
18.7483 9.9208 ?8.626 ?5.6166 0.7624 1.2935 7.5942 2.045；
19.4512 10.5363 8.8685 5.2879 0.7718 1.3214 7.9413 1.8085；
20.0309 10.7921 7.8509 5.4841 0.8183 1.3939 6.7776 2.0888；
19.443 11.1617 7.6568 5.0871 0.7642 1.3334 6.5187 1.7238；
17.1874 9.8051 ?8.8757 4.5728 0.7267 1.1347 7.8838 1.7536；
16.9546 9.5353 ?7.6742 5.2076 0.7476 1.2378 6.7484 1.9521；
16.1874 9.2767 ?7.5292 4.7548 0.7098 1.2115 6.6599 1.7472；
16.6872 9.4724 ?6.3118 4.835 ?0.7002 1.1473 5.3364 1.6791；
17.526 10.0279 7.2432 5.27 ?0.7397 1.2601 6.2707 1.8005；
16.2897 9.2727 ?6.4617 4.9421 0.6802 1.1712 5.5427 1.6939；
16.3995 9.0821 ?7.4974 4.9787 0.7113 1.1775 6.6992 1.7883；
18.7414 10.4255 6.815 ?5.0967 0.7771 1.191 ?5.6654 1.6386；
16.8314 9.6273 ?6.9508 4.245 ?0.6977 1.0531 5.9724 1.5462
12 groups of data that are used to test are:
29.6227 17.6801 19.46551 0.2675 1.2334 2.4326 17.6177 3.5355；
31.5177 18.698 ?15.7807 ?9.8632 1.2486 2.3671 14.071 ?3.2618；
29.766 18.0832 16.4785 9.5238 1.2275 2.288 14.8059 3.2531；
26.2162 13.9517 10.3812 ?7.1666 0.9572 1.6761 8.9609 ?2.5129；
25.6382 14.1059 8.1882 8.1704 0.9905 1.935 ?6.7521 2.8516；
22.6363 11.2832 7.2127 6.3094 0.8145 1.4425 5.9081 2.1353；
18.2451 9.5414 ?6.9163 4.9789 0.6904 1.1664 5.9753 1.6873；
20.387 10.4255 8.2459 5.0596 0.8114 1.248 7.2317 1.8474；
18.4716 9.6129 ?7.8849 4.9423 0.6834 1.1595 6.9823 1.6983；
16.2074 9.1581 ?8.0225 4.708 ?0.6868 1.177 ?7.0942 1.8232；
15.4307 8.5706 ?6.1938 4.4521 0.6819 1.1381 5.3196 1.6066；
15.3517 8.6671 ?6.112 ?4.6578 0.6668 1.0989 5.3329 1.5494
Carrying out neuron network diagnosis back neuron network output result is:
Interpretation of result:
Ducted virtual condition contrast is found during with result who obtains and test, and the 4th group of test diagnosis mistake, actual conditions are " a small amount of obstruction ", and the neuron network erroneous judgement is " unimpeded ".The result that other states obtain and met in practice.The diagnosis accuracy of identification of the inventive method that hence one can see that is very high.
The present invention also can diagnose identification with time-domain signal endurance or spectrogram maximum resonant frequency amplitude as characteristic parameter separately respectively, but the energy value of each frequency range that the two diagnosis accuracy of identification of evidence does not obtain with WAVELET PACKET DECOMPOSITION diagnoses the precision of identification high as characteristic parameter.Can know through test; Diagnose on the base of recognition as characteristic parameter at the energy value of each frequency range of obtaining of utilization WAVELET PACKET DECOMPOSITION and comprehensively to diagnose identification and/or to diagnose identification as characteristic parameter as characteristic parameter, can improve the diagnosis accuracy of identification with spectrogram maximum resonant frequency amplitude with the time-domain signal endurance.
Claims (7)
1. the detection of a deposit in metal pipe and assessment method, its concrete steps are following:
1) knock the excitation position of metal tube with exciting bank, motivate the sound wave of the impulse response characteristic that can reflect structural body, wherein exciting bank is the steel tup, and the excitation position is that metal tube is prone to block part;
2) with microphone and data collecting card the acoustic signals that motivates is spread out of and gathers;
3) use the acquisition software recorded voice, and deposit computer in;
4) signal is carried out wavelet packet analysis, time-domain analysis and/or frequency analysis, calculate each band energy value, time-domain signal endurance and/or these characteristic parameters of spectrogram maximum resonant frequency amplitude of wavelet packet analysis;
5) characteristic parameter input neural network expert diagnostic system is discerned, qualitative and quantitative to whether there being deposit to carry out in managing, and based on chocking-up degree in the quantitative result evaluation pipe.
2. based on the detection and the assessment method of the said deposit in metal pipe of claim 1; The concrete steps that it is characterized in that the identification of neutral net expert diagnostic system are: 1) gather training sample, comprise each band energy value, time-domain signal duration and/or the spectrogram maximum resonant frequency amplitude and the chocking-up degree of wavelet packet analysis; 2) structure and training network are input to the sample data in the step 1) in the good BP neutral net of structure, network are trained after configuring training parameter, and training will stop automatically when the mean square error of training reaches requirement; 3) adopt the network that trains to carry out diagnostic test, promptly import the characteristic parameter of pipe to be measured, can obtain chocking-up degree in the metal tube.
3. according to the detection and the assessment method of the said deposit in metal pipe of claim 1, it is characterized in that: chocking-up degree is divided into unimpeded, a small amount of obstruction of pipeline, stops up more and seriously stops up four ranks.
4. according to the detection and the assessment method of the said deposit in metal pipe of claim 1, it is characterized in that: the excitation position is the lower portion of elbow under the metallic conduit.
5. the used device of the detection of the said deposit in metal pipe of claim 1 and assessment method; The computer that comprises exciting bank, microphone, band data collecting card; The acquisition software that is used for signal recording, signal processing software and the neuron network expert diagnostic system that is used for signal is carried out wavelet packet analysis, time-domain analysis and/or frequency analysis and calculated characteristics parameter are installed in the computer; Microphone fixing is on metal tube, and microphone links to each other with computer main respectively with data collecting card.
6. device according to claim 5 is characterized in that: microphone is positioned at the forward entrance position of sound wave.
7. device according to claim 5 is characterized in that: said characteristic parameter comprises each band energy value, time-domain signal endurance and/or the spectrogram maximum resonant frequency amplitude of wavelet packet analysis.
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