CN109063762A - A kind of line clogging fault recognition method based on DT-CWT and S4VM - Google Patents
A kind of line clogging fault recognition method based on DT-CWT and S4VM Download PDFInfo
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Abstract
The invention discloses a kind of line clogging fault recognition methods based on DT-CWT and S4VM, belong to fault detection technique field, the present invention detects the sound pressure signal data that pipeline obtains pipeline first, then it is carried out using acoustic signal data of the dual-tree complex wavelet transform algorithm to acquisition decomposed and reconstituted, obtain the component of each frequency range, effective frequency range component is selected from the component of each frequency range, and sound pressure level transformation is carried out to effective frequency range component, then the pulse factor of effective frequency range component and average sound energy density after converting are extracted, the pulse factor and average sound energy density as the feature vector for being used to classify and will be trained to obtain training aids model in feature vector input S4VM classifier, the different degrees of blocking of pipeline is identified using training aids model and excludes the interference of three-way piece, this method overcomes existing pipeline inspection It surveys signal and is difficult to the problem of being effectively treated, it is small to pipe damage, it is at low cost, it is easy for installation, it is practical.
Description
Technical field
The present invention relates to a kind of line clogging fault recognition methods based on DT-CWT and S4VM, belong to fault detection technique
Field.
Background technique
About 710,000 kilometers of China's water supply line total length in 2015, and it is annual in rising trend, but China and developed country
It compares, the pipeline leak rate height that is averaged wherein the greasy filth, rust deposite caused by blocking in pipe can solidify causes former caliber to become smaller；Mud
It can be precipitated in pipeline, be easy to produce the flammable explosive gas such as hydrogen sulfide, cause environmental pollution and easily cause booster.If too late
When remove foreign matter in pipeline, will cause line clogging, gently then endanger water safety, it is heavy then cause to leak, booster, to resource,
Environment and China's economy bring about great losses and endanger.So timely detect line clogging, control line clogging quantitative change is generated
Harm is dangerous, and to saving, water resource, guarantee urban water, the promotion economic sustainable and healthy development in China are significant.
The detection in buried drain pipe road at present is concentrated mainly on subsequent detection, exists in method and excavates damage, relies on artificial
Operation, the main problems such as equipment valuableness.Detection method based on acoustics has the characteristics that inexpensive, easy for installation.Guided waves
Belong to one of acoustics field of non destructive testing fresh approach, can be used to line clogging and leakage detection and localization, this method
It is propagated using mechanical stress wave along pipeline extending direction, propagation distance is long and decays small, and sometimes single position detection can
Up to hundreds of meters, it is only necessary to handle the pipeline external surface of sensor mounting location, the pipe for being difficult to closely detect can be detected
Road position.When pipeline blocks, conduit cross-sectional area is caused to become smaller, when medium changes, acoustic characteristic can be sent out guided wave
Raw corresponding variation, guided wave also will receive the influence of pipeline conventional components such as three-way piece in communication process.
Summary of the invention
The purpose of the present invention is to provide a kind of line clogging fault recognition method based on DT-CWT and S4VM, DT-CWT
As dual-tree complex wavelet transform, S4VM are safe semisupervised support vector machines, and the present invention overcomes existing pipe detections
Signal is difficult to the problem of being effectively treated, and present invention guided wave obtains the echo in pipeline to line clogging and leak detection, obtains
Echo-signal be non-linear, non-stationary signal, from blocking acoustical signal mechanism, analysis blocking acoustical signal feature and is utilized
The feature extracting method that dual-tree complex wavelet transform and sound pressure level combine is to Signal Pretreatment, according to the propagation of sound wave in the duct
Mechanism, extracts sound energy density and the pulse factor is characterized, using safe semisupervised support vector machines, that is, S4VM to different blockings
Situation and three-way piece have carried out more classification annotations.
The technical scheme is that the sound pressure signal data of detection pipeline acquisition pipeline first, then multiple using double trees
Wavelet Transformation Algorithm is decomposed and reconstituted to the acoustic signal data progress of acquisition, the component of each frequency range is obtained, from the component of each frequency range
The middle effective frequency range component of selection, and sound pressure level transformation is carried out to effective frequency range component, then extract effective frequency range component after transformation
The pulse factor and average sound energy density, using the pulse factor and average sound energy density as being used for the feature vector of classification simultaneously
Feature vector is inputted in S4VM classifier and is trained to obtain training aids model, identifies pipeline difference journey using training aids model
The blocking of degree and the interference for excluding three-way piece.
The specific steps of the present invention are as follows:
(1) acoustic instrument is placed in pipeline first, pipeline is externally provided with data signal sampling and processing equipment, the tail of pipeline
Hold semiclosed, sound encounters pipeline tail end and reflection, refraction, scattering occurs in the communication process of pipeline, generates and a large amount of carries pipe
Road fault of construction information reflection echo, the echo of data signal sampling and processing equipment acquisition reflection obtain the acoustic pressure letter of pipeline
Number；
(2) data signal sampling and processing equipment handles signal data, using dual-tree complex wavelet transform algorithm pair
Sound pressure signal data are decomposed, are reconstructed, and the component of each frequency range is obtained, and the component of each frequency range is indicated with time domain figure respectively, are seen
Time-domain diagram is examined, the frequency range component that the big time-domain diagram of signals and associated noises indicates is removed, using the component of other frequency ranges as effective frequency range
Component, and sound pressure level transformation is carried out to effective frequency range component, obtain the sound pressure level signal of each effective frequency range component；
(3) the pulse factor of the sound pressure level signal of each effective frequency range component of extraction step (2) and average sound energy density,
In each effective frequency range component average acoustic energy metric density formulas Extraction it is as follows:
Wherein,Indicate average acoustic energy metric density, peIndicate effective acoustic pressure, andpaIndicate original acoustic pressure, ρ0
Indicate density, c0Indicate sound spread speed in the duct,For the time average of sound energy density, V0For pipeline body
Product；
(4) the pulse factor and average sound energy density extracted step (3) be used as classification feature vector and by
Classification based training, which is carried out, in feature vector input S4VM classifier obtains the model of each classification results；
(5) step (1)~(3) are repeated when testing other pipeline, obtain the pulse factor for testing pipeline data and average sound
Energy density, then calculates the Euclidean distance between test data and the model of step (4) each classification results, and by the Europe of calculating
Formula distance is test result of the representative classification results of minimum value as the test pipeline, completes the failure to test pipeline
Identification.
The model of four kinds of classification results is obtained in the step (4), respectively normally, slight to block, middle severe blocking, three
Parts interference.
Acoustic instrument includes more than one hydrophone and a loudspeaker in the step (1), and data acquire and signal
Processing equipment includes PC machine, sound card, filter, preamplifier and power amplifier；More than one hydrophone respectively with filter
The connection of wave device, filter are connect with power amplifier, and power amplifier is connect with sound card, and loudspeaker passes through preamplifier and sound
Card connection, sound card are connect with PC machine.
Compared with prior art, beneficial effects of the present invention are as follows:
(1) present invention is using sound wave as detection means, active detecting defect of pipeline, destroys in practical application to pipe-line system
Property it is small, detecting distance is long, and cost is relatively low for other opposite detection modes.
(2) present invention uses dual-tree complex wavelet transform algorithm, and it is mixed which can overcome Traditional Wavelet to decompose the frequency occurred
Folded phenomenon obtains accurately each frequency component.
(3) present invention identifies that semi-supervised learning can also effectively identify difference to different degrees of blocking using S4VM
Pipeline fault, and reduce the problem of mass data can not mark in practical pipe detection.
Detailed description of the invention
Fig. 1 is detection schematic diagram of the invention；
Fig. 2 (a) is the time-domain diagram that 1 dual-tree complex wavelet of the embodiment of the present invention decomposes sound pressure signal；
Fig. 2 (b) is the transformed time-domain diagram of 1 sound pressure level of the embodiment of the present invention；
Fig. 3 is that 1 liang of category feature of the embodiment of the present invention clusters visualization result.
Specific embodiment
Below in conjunction with the drawings and specific embodiments, the invention will be further described.
Embodiment 1:(1) pipe detection schematic illustration as shown in Figure 1, pipeline head end place acoustic instrument, acoustics instrument
Device includes four hydrophones and a loudspeaker, and pipeline is externally provided with data signal sampling and processing equipment, data acquisition and signal
Processing equipment includes PC machine, sound card, filter, preamplifier and power amplifier；4 hydrophones connect with filter respectively
It connects, filter is connect with power amplifier, and power amplifier is connect with sound card, and loudspeaker is connected by preamplifier and sound card
It connects, sound card is connect with PC machine, and the sinusoidal signal that frequency of use range is 50 to 7000Hz is as pumping signal, the tail end half of pipeline
Closing, sound encounter pipeline tail end and occur to reflect, refraction, scatter, generate a large amount of carrying pipeline knots in the communication process of pipeline
Structure defect information reflection echo, the echo of data signal sampling and processing equipment acquisition reflection, every 0.1s acquire 4410 points, adopt
Sample frequency is 44100Hz, obtains the sound pressure signal data of pipeline；
(2) data signal sampling and processing equipment handles signal data, using dual-tree complex wavelet transform algorithm pair
Sound pressure signal data carry out 8 layers and decompose and reconstruct, and obtain the component of each frequency range, the component of each frequency range is used time domain chart respectively
Show, as shown in Fig. 2, by Fig. 2 (a) dual-tree complex wavelet decomposition result figure as it can be seen that d1, d2, d3 frequency are higher, fault characteristic information compared with
Less and contain noise information, so not handling d1, d2, d3 of high frequency section, remove the component of d1, d2, d3 frequency range,
According to the number of plies of decomposition, every layer of decomposition frequency becomes upper one layer of fs/2n, and Decomposition order is higher, and frequency is lower, selects remaining
D4, d5, d6, d7, d8, a8 carry out sound pressure level transformation as active constituent, by active constituent respectively, as shown in Fig. 2 (b)；
(3) the pulse factor of the sound pressure level signal of each effective frequency range component of extraction step (2) and average sound energy density,
In each effective frequency range component average acoustic energy metric density formulas Extraction it is as follows:
Wherein,Indicate average acoustic energy metric density, peIndicate effective acoustic pressure, andpaIndicate original acoustic pressure, ρ0
Indicate density, c0Indicate sound spread speed in the duct,For the time average of sound energy density, V0For pipeline body
Product；The pulse factor P of extractionjWith average sound energy density PiMatrix it is as follows:
(4) the pulse factor and average sound energy density extracted step (3) be used as classification feature vector and by
Classification based training is carried out in feature vector input S4VM classifier and obtains the model of each classification results, obtains the mould of four kinds of classification results
Type, respectively normally, slight to block, middle severe blocking, three-way piece interference specially carries out the set of the feature vector of extraction
Singular value decomposition dimensionality reduction, by 6 dimension sign vectors drop to 3 dimensions carry out classification and 3 dimension coordinates under visualize, as shown in figure 3,
Four kinds of operating mode feature distribution characteristics vector aggregations are obvious, there is good separability, so feature set can be used to fault type
Classify.
(5) step (1)~(3) are repeated when testing other pipeline, obtain the pulse factor for testing pipeline data and average sound
Energy density, then calculates the Euclidean distance between test data and the model of step (4) each classification results, and by the Europe of calculating
Formula distance is test result of the representative classification results of minimum value as the test pipeline, completes the failure to test pipeline
Identification.
Specific embodiments of the present invention are explained in detail above in conjunction with attached drawing, but the present invention is not limited to above-mentioned realities
Example is applied, it within the knowledge of a person skilled in the art, can also be without departing from the purpose of the present invention
Various changes can be made.
Claims (4)
1. a kind of line clogging fault recognition method based on DT-CWT and S4VM, which is characterized in that detection pipeline first obtains
Then the sound pressure signal data of pipeline carry out the sound pressure signal data of acquisition using dual-tree complex wavelet transform algorithm decomposing weight
Structure obtains the component of each frequency range, effective frequency range component is selected from the component of each frequency range, and carry out acoustic pressure to effective frequency range component
Grade converts, and the pulse factor of effective frequency range component and average sound energy density after converting then is extracted, by the pulse factor and averagely
Sound energy density is classified as the feature vector for classification and by being trained in feature vector input S4VM classifier
As a result model identifies the different degrees of blocking of pipeline using the model of classification results and excludes the interference of three-way piece.
2. the line clogging fault recognition method according to claim 1 based on DT-CWT and S4VM, it is characterised in that: tool
Steps are as follows for body:
(1) acoustic instrument is placed in pipeline first, pipeline is externally provided with data signal sampling and processing equipment, the tail end half of pipeline
Closing, sound encounter pipeline tail end and occur to reflect, refraction, scatter, generate a large amount of carrying pipeline knots in the communication process of pipeline
Structure defect information reflection echo, the echo of data signal sampling and processing equipment acquisition reflection, obtains the sound pressure signal number of pipeline
According to；
(2) data signal sampling and processing equipment handles signal data, using dual-tree complex wavelet transform algorithm to acoustic pressure
Signal data is decomposed, is reconstructed, and the component of each frequency range is obtained, and the component of each frequency range is indicated with time domain figure respectively, when observation
Domain figure removes the frequency range component that the big time-domain diagram of signals and associated noises indicates, using the component of other frequency ranges as effective frequency range component,
And sound pressure level transformation is carried out to effective frequency range component, obtain the sound pressure level signal of each effective frequency range component；
(3) the pulse factor of the sound pressure level signal of each effective frequency range component of extraction step (2) and average sound energy density, wherein respectively
The formulas Extraction of the average acoustic energy metric density of effective frequency range component is as follows:
Wherein,Indicate average acoustic energy metric density, peIndicate effective acoustic pressure, andpaIndicate original acoustic pressure, ρ0It indicates
Density, c0Indicate sound spread speed in the duct,For the time average of sound energy density, V0For conduit volume；
(4) the pulse factor and average sound energy density extracted step (3) are used as the feature vector for classifying and by features
Classification based training, which is carried out, in vector input S4VM classifier obtains the model of each classification results；
(5) step (1)~(3) are repeated when testing other pipeline, obtain the pulse factor for testing pipeline data and average acoustic energy
Density, then calculates the Euclidean distance between test data and the model of step (4) each classification results, and by calculating it is European away from
From test result of the representative classification results as the test pipeline for minimum value, complete to know the failure of test pipeline
Not.
3. the line clogging fault recognition method according to claim 2 based on DT-CWT and S4VM, it is characterised in that: institute
It states in step (4) and obtains the model of four kinds of classification results, respectively normally, slight to block, middle severe blocking, three-way piece interference.
4. the line clogging fault recognition method according to claim 2 based on DT-CWT and S4VM, it is characterised in that: institute
Stating acoustic instrument in step (1) includes more than one hydrophone and a loudspeaker, data signal sampling and processing equipment packet
Include PC machine, sound card, filter, preamplifier and power amplifier；More than one hydrophone is connect with filter respectively, filter
Wave device is connect with power amplifier, and power amplifier is connect with sound card, and loudspeaker is connect by preamplifier with sound card, sound card
It is connect with PC machine.
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| CN110673199A (en) * | 2019-08-30 | 2020-01-10 | 昆明理工大学 | U-shaped tube blockage state assessment method based on low-frequency sound pressure signal analysis |
| CN110940889A (en) * | 2019-12-06 | 2020-03-31 | 西安锐驰电器有限公司 | Fault detection method for high-voltage power equipment |
| CN111815561A (en) * | 2020-06-09 | 2020-10-23 | 中海石油(中国)有限公司 | Pipeline defect and pipeline assembly detection method based on depth space-time characteristics |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109827081A (en) * | 2019-02-28 | 2019-05-31 | 昆明理工大学 | A kind of buried drain pipe road plugging fault and branch pipe tee connection part diagnostic method based on acoustics active detecting |
| CN109827081B (en) * | 2019-02-28 | 2020-12-11 | 昆明理工大学 | Buried drainage pipeline blocking fault and pipeline tee part diagnosis method based on acoustic active detection |
| CN110673199A (en) * | 2019-08-30 | 2020-01-10 | 昆明理工大学 | U-shaped tube blockage state assessment method based on low-frequency sound pressure signal analysis |
| CN110673199B (en) * | 2019-08-30 | 2022-05-13 | 昆明理工大学 | U-shaped tube blockage state assessment method based on low-frequency sound pressure signal analysis |
| CN110940889A (en) * | 2019-12-06 | 2020-03-31 | 西安锐驰电器有限公司 | Fault detection method for high-voltage power equipment |
| CN111815561A (en) * | 2020-06-09 | 2020-10-23 | 中海石油(中国)有限公司 | Pipeline defect and pipeline assembly detection method based on depth space-time characteristics |
| CN111815561B (en) * | 2020-06-09 | 2024-04-16 | 中海石油(中国)有限公司 | Pipeline defect and pipeline assembly detection method based on depth space-time characteristics |
| CN112908356A (en) * | 2021-01-19 | 2021-06-04 | 昆明理工大学 | Buried drainage pipeline voiceprint recognition method based on BSE and GMM-HMM |
| CN112908356B (en) * | 2021-01-19 | 2022-08-05 | 昆明理工大学 | Buried drainage pipeline voiceprint recognition method based on BSE and GMM-HMM |
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