CN106645424B - Steel rail crack online monitoring noise filtering and crack judging method - Google Patents
Steel rail crack online monitoring noise filtering and crack judging method Download PDFInfo
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- CN106645424B CN106645424B CN201611127509.8A CN201611127509A CN106645424B CN 106645424 B CN106645424 B CN 106645424B CN 201611127509 A CN201611127509 A CN 201611127509A CN 106645424 B CN106645424 B CN 106645424B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/14—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object using acoustic emission techniques
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/02—Indexing codes associated with the analysed material
- G01N2291/023—Solids
- G01N2291/0234—Metals, e.g. steel
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/02—Indexing codes associated with the analysed material
- G01N2291/028—Material parameters
- G01N2291/0289—Internal structure, e.g. defects, grain size, texture
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/26—Scanned objects
- G01N2291/262—Linear objects
- G01N2291/2623—Rails; Railroads
Abstract
The invention discloses a method for online monitoring, noise filtering and crack judging of steel rail cracks, which comprises the following steps: the acoustic emission sensor is used for transmitting a steel rail sound signal, and the acquisition controller is used for transmitting the steel rail sound signal back to the monitoring end for processing; carrying out layered decomposition on the steel rail sound signal by using a db6 wavelet to obtain wavelet packet reconstruction signal components, time, frequency and wavelet coefficients; high-frequency interference caused by impact in wavelet packet reconstruction signal components is eliminated by a method of calculating a frequency band energy ratio; reconstructing wavelet coefficients of the residual wavelet packet reconstruction signal components to obtain a reconstruction signal; performing classical acoustic emission parameter analysis on the reconstructed signal to obtain an acoustic emission parameter; and judging whether the steel rail has crack propagation or not through analyzing the correlation diagram and the experience diagram. The invention can effectively remove noise interference signals in the acoustic emission signals, can separate crack signals from the interference signals and has high separation precision; the method is not only used for processing the stationary signals, but also has a function of locally analyzing the non-stationary signals.
Description
Technical Field
The invention belongs to the technical field of rail crack detection, and particularly relates to a method for online monitoring, noise filtering and crack judging of rail cracks.
Background
Modern railways have increased load carrying capacity (faster operating speeds and greater axle weights), increased dynamic loading of the rails, and increased likelihood of line degradation. In the long-term operation process, the steel rail is subjected to extrusion deformation and rolling contact fatigue for a long time, so that cracks, fractures or other damages are generated on the surface and inside of the steel rail, and the crack propagation of the steel rail is a main cause of accidents caused by the fracture of the steel rail. In order to maintain high safety level railway operation, real-time on-line detection of railway operation conditions is important. Acoustic emission can detect whether a rail is undergoing crack propagation under a driving load. The phenomenon of local sources in materials that rapidly release energy to generate transient elastic waves is called Acoustic Emission (AE), sometimes also called stress wave Emission. The deformation and crack propagation of the material under the action of stress are important mechanisms for structural failure. This source, which is directly related to the deformation and fracture mechanism, is called the acoustic emission source.
The conventional acoustic emission detection method is to characterize the acoustic emission signal by a plurality of simplified waveform characteristic parameters, and then analyze and process the waveform characteristic parameters.
However, the relatively high traffic noise environment and limited signal of crack propagation, especially at low crack propagation rates, acoustic emission signal analysis is very complex. Therefore, a large number of analyses of the acoustic emission signal, separation of the crack signal from the disturbance signal, and in particular a quantitative characterization of each acoustic waveform caused by crack propagation, are required. Conventional signal processing methods (fourier transforms) can only be used to process stationary signals, and lack local analysis functionality for non-stationary signals.
Since the wavelet transform performs a further decomposition only on the low frequency part of the signal, and does not continue to decompose on the high frequency part, i.e., the detailed part of the signal, the wavelet transform can well characterize a large class of signals with low frequency information as the main component, but it cannot well decompose and represent signals containing a large amount of detailed information (fine edges or textures).
Disclosure of Invention
In order to solve the problems, the invention provides a method for filtering noise and judging cracks of steel rail cracks through online monitoring, which can effectively remove noise interference signals in acoustic emission signals, can separate crack signals from the interference signals and has high separation precision; the method is not only used for processing the stationary signals, but also has a function of locally analyzing the non-stationary signals.
In order to achieve the purpose, the invention adopts the technical scheme that:
a method for online monitoring, noise filtering and crack judging of steel rail cracks comprises the following steps:
s01: collecting steel rail sound signals by an acoustic emission sensor, and transmitting the collected steel rail sound signals back to a monitoring end;
s02: the monitoring end carries out layered decomposition on the steel rail sound signal through a db6 wavelet to obtain a wavelet packet, wherein the wavelet packet comprises a reconstructed signal component, and time, frequency and wavelet coefficients of the reconstructed signal component;
s03: eliminating high-frequency interference caused by impact in wavelet packet reconstruction signal components by a method of calculating a frequency band energy ratio;
s04: reconstructing wavelet coefficients of the residual wavelet packet reconstruction signal components to obtain a reconstruction signal;
s05: carrying out acoustic emission parameter analysis on the reconstructed signal to obtain an acoustic emission parameter;
s06: and drawing a correlation diagram and a history diagram through the acoustic emission parameters, analyzing the correlation diagram and the history diagram, and judging whether the steel rail has crack propagation.
Further, in step S01, determining and excluding the rim contact signal in the sound signal of the steel rail according to the duration of the sound signal of the steel rail during the collecting process; if the duration is long, the signal is a rim-sticking signal, and if the duration is short, the signal is not a rim-sticking signal.
Further, in step S01, it is determined whether or not the rail sound signal is a friction signal, and a slow crack propagation stage is determined, so that a warning is generated before the micro-cracks become macro-cracks.
Further, if the sound signal of the steel rail is a signal that the peaks and the valleys interact to form concave-convex staggering, the peaks of the staggering part can emit a series of acoustic emission signals of elastic waves, and then the sound signal of the steel rail is a friction signal.
The friction signal is a sound emission signal which is generated by that when metal objects in contact with two surfaces slide relatively, micro-peaks and valleys on the surfaces interact to form concave-convex staggered signals, and the peaks of the staggered parts are broken or deformed, so that a series of elastic waves are emitted.
Further, in step S02, the rail sound signal is decomposed four-layer by db6 wavelet to obtain 16 wavelet packet reconstructed signal components of (4,0), (4,1), (4,2), (4,3), (4,4), (4,5), (4,6), (4,7), (4,8), (4,9), (4,10), (4,11), (4,12), (4,13), (4,14) and (4,15), and time t, frequency f and wavelet coefficient W are obtained.
Further, in step S05, the acoustic emission parameters include ring count, event count, peak value and duration parameters.
On the other hand, the invention also provides a system for online monitoring and noise filtering of the steel rail cracks, which comprises an acoustic emission sensor, an acquisition controller and a monitoring end;
the acoustic emission sensor is arranged on the steel rail to acquire a steel rail sound signal;
the acquisition controller comprises a processor and a communication circuit, an input interface is connected with the acoustic emission sensor, and an output interface is connected with the monitoring end and transmits the sound signal data of the steel rail;
and the monitoring end is used for carrying out signal processing and crack judgment.
Further, the monitoring end comprises a wavelet decomposition module, an interference removal module, a wavelet packet reconstruction module, an acoustic emission parameter analysis module and a crack judgment module which are connected in sequence;
a wavelet decomposition module: carrying out layered decomposition on the steel rail sound signal by using a db6 wavelet to obtain wavelet packet reconstruction signal components, and obtaining time, frequency and wavelet coefficients;
an interference removal module: eliminating high-frequency interference caused by impact in wavelet packet reconstruction signal components by calculating a frequency band energy ratio;
wavelet packet reconstruction module: reconstructing wavelet coefficients of the residual wavelet packet reconstruction signal components to obtain a reconstruction signal;
an acoustic emission parameter analysis module: performing classical acoustic emission parameter analysis on the reconstructed signal to obtain an acoustic emission parameter;
a crack judging module: and (4) analyzing a correlation diagram and a history diagram of the acoustic emission parameters to judge whether the steel rail has crack propagation.
The beneficial effects of the technical scheme are as follows:
the invention can effectively remove noise interference signals in the acoustic emission signals, can separate crack signals from the interference signals and has high separation precision; the method is not only used for processing the stationary signals, but also has a function of locally analyzing the non-stationary signals.
The acoustic emission signal is composed of a plurality of groups of waves with rich frequency spectrum, and the acoustic emission signal often contains noise due to a propagation path and a transducer. The characteristics of different frequency components in the acoustic emission signal can be analyzed on different frequency bands by decomposing the acoustic emission signal into different frequency channels by using wavelet transform.
The wavelet packet transformation is added, so that the wavelet packet transformation can provide finer decomposition for a high-frequency part, and the decomposition has no redundancy or omission, so that better time-frequency localization analysis can be performed on signals containing a large amount of medium-frequency and high-frequency information.
The Db6 wavelet transform has good regularity, that is, the smooth error introduced by the wavelet as sparse basis is not easy to be perceived, so that the signal reconstruction process is relatively smooth. The Db6 wavelet is characterized in that the order of vanishing moment is larger along with the increase of the order, wherein the higher the vanishing moment is, the better the smoothness is, the stronger the localization capability of the frequency domain is, and the better the frequency band division effect is.
Drawings
FIG. 1 is a schematic flow chart of a method for online monitoring, noise filtering and crack determination of a rail crack according to the present invention;
FIG. 2 is a schematic diagram of a four-layer decomposition of a rail acoustic signal using a db6 wavelet according to an embodiment of the present invention;
FIG. 3 is a schematic illustration of acoustic emission parameters in an embodiment of the present invention;
FIG. 4 is a diagram illustrating the time lapse of the ring count in an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a system for online monitoring and noise filtering of rail cracks according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described with reference to the accompanying drawings.
In the embodiment of the invention, referring to fig. 1, the invention provides a method for online monitoring, noise filtering and crack judgment of a steel rail crack, which comprises the following steps:
s01: and collecting a steel rail sound signal by the acoustic emission sensor, and transmitting the steel rail sound signal back to the monitoring end for processing.
In step S01, the duration of the sound signal is determined, and the rim contact signal in the sound signal is removed.
In step S01, if the rail sound signal is a signal in which the peaks and the valleys interact to form a concave-convex staggered structure, and the peaks of the staggered structure emit a series of acoustic emission signals of elastic waves, the rail sound signal is a friction signal. The crack is determined in the slow propagation stage of crack formation, so that an early warning is formed before the micro-cracks become the macro-cracks.
The friction signal is a sound emission signal which is generated by that when metal objects in contact with two surfaces slide relatively, micro-peaks and valleys on the surfaces interact to form concave-convex staggered signals, and the peaks of the staggered parts are broken or deformed, so that a series of elastic waves are emitted.
S02: and the monitoring end carries out layered decomposition on the steel rail sound signal through the db6 wavelet to obtain a wavelet packet, wherein the wavelet packet comprises a reconstructed signal component and time, frequency and wavelet coefficients of the reconstructed signal component.
In step S02, as shown in fig. 2, the rail sound signal is decomposed four-layer by db6 wavelet to obtain 16 wavelet packet reconstructed signal components of (4,0), (4,1), (4,2), (4,3), (4,4), (4,5), (4,6), (4,7), (4,8), (4,9), (4,10), (4,11), (4,12), (4,13), (4,14) and (4,15), and time t, frequency f and wavelet coefficient W are obtained.
S03: high-frequency interference caused by impact in wavelet packet reconstruction signal components is eliminated by a method for calculating a frequency band energy ratio.
The signal is mainly concentrated in the small wave packets of (4,1) - (4, 10); high-frequency interference caused by partial impact is eliminated by calculating the energy ratio of the frequency band; and reconstructing the wavelet coefficient to obtain a reconstructed signal.
S04: and then reconstructing the wavelet coefficients of the residual wavelet packet reconstruction signal components to obtain a reconstruction signal.
And (3) reconstructing wavelet coefficients: the coefficients of the nodes (4,0) and (4,11) - (4,15) are set to zero, and then are restored together with the coefficients of the nodes (4,1) - (4,10), so as to obtain a reconstructed signal.
S05: and carrying out classical acoustic emission parameter analysis on the reconstructed signal to obtain acoustic emission parameters.
In step S05, the acoustic emission parameters include ring count, event count, peak, energy, and duration parameters.
The specific analysis process is as follows, as shown in fig. 3:
ringing: a sound signal of the steel rail which exceeds a threshold and enables a certain channel to acquire data is called ringing;
event: the local change of the primary steel rail material which generates the steel rail sound signal is called an acoustic emission event;
counting: crossing the number of oscillations of the threshold signal;
start time: the time of arrival of a wave of the rail acoustic signal at the transducer, shown as t 1;
rise time: the Time interval from the first Time the rail sound signal crosses the threshold to the maximum amplitude, Time _ up t3-t 1;
duration: the Time interval from the first Time that the sound signal of the steel rail crosses the threshold to finally fall to the threshold, wherein the Time _ hold is t2-t 1;
peak value: the maximum amplitude value of the signal waveform, denoted Vmax in the figure;
energy: the area under the envelope of the signal detection,
s06: and judging whether the steel rail has crack propagation or not by analyzing the acoustic emission parameter correlation diagram and the experience diagram.
The more ringing counts indicate that the crack propagation speed of the steel rail is higher; the more the event counts, the faster the crack propagation speed of the steel rail is; higher peak values indicate greater rail crack widths; higher energy indicates greater rail crack width; longer duration indicates a faster rail crack propagation rate.
As shown in fig. 4, the time history of the ringing count can be seen, and the crack starts to slowly and slightly propagate, and then rapidly progresses; thus, acoustic emission can provide real-time or continuous information that the defect changes with external variables such as load, time, temperature, etc., and is therefore suitable for online monitoring and early or near-failure prediction of industrial processes.
In order to match the realization of the method of the invention, based on the same inventive concept, as shown in fig. 5, the invention also provides a system for online monitoring and noise filtering of the rail cracks, which comprises an acoustic emission sensor, an acquisition controller and a monitoring end;
the acoustic emission sensor is arranged on the steel rail to acquire a steel rail sound signal;
the acquisition controller comprises a processor and a communication circuit, an input interface is connected with the acoustic emission sensor, and an output interface is connected with the monitoring end and transmits the sound signal data of the steel rail;
and the monitoring end is used for carrying out signal processing and crack judgment.
The monitoring end comprises a wavelet decomposition module, an interference removal module, a wavelet packet reconstruction module, an acoustic emission parameter analysis module and a crack judgment module which are sequentially connected;
a wavelet decomposition module: carrying out layered decomposition on the steel rail sound signal by using a db6 wavelet to obtain wavelet packet reconstruction signal components, and obtaining time, frequency and wavelet coefficients;
an interference removal module: eliminating high-frequency interference caused by impact in wavelet packet reconstruction signal components by calculating a frequency band energy ratio;
wavelet packet reconstruction module: reconstructing wavelet coefficients of the residual wavelet packet reconstruction signal components to obtain a reconstruction signal;
an acoustic emission parameter analysis module: performing classical acoustic emission parameter analysis on the reconstructed signal to obtain an acoustic emission parameter;
a crack judging module: and (4) analyzing a correlation diagram and a history diagram of the acoustic emission parameters to judge whether the steel rail has crack propagation.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (4)
1. A method for filtering noise and judging cracks of steel rail cracks through online monitoring is characterized by comprising the following steps:
s01: collecting steel rail sound signals by an acoustic emission sensor, and transmitting the collected steel rail sound signals back to a monitoring end; in the collecting process, the wheel rim attaching signal in the sound signal of the steel rail is judged and eliminated according to the duration time of the sound signal of the steel rail;
s02: the monitoring end carries out layered decomposition on the steel rail sound signal through a db6 wavelet to obtain a wavelet packet, wherein the wavelet packet comprises a reconstructed signal component, and time, frequency and wavelet coefficients of the reconstructed signal component;
s03: eliminating high-frequency interference caused by impact in wavelet packet reconstruction signal components by a method of calculating a frequency band energy ratio;
s04: reconstructing wavelet coefficients of the residual wavelet packet reconstruction signal components to obtain a reconstruction signal;
s05: carrying out acoustic emission parameter analysis on the reconstructed signal to obtain acoustic emission parameters, wherein the acoustic emission parameters comprise ringing count, event count, peak value and duration parameters;
s06: and drawing a correlation diagram and a history diagram through the acoustic emission parameters, analyzing the correlation diagram and the history diagram, and judging whether the steel rail has crack propagation.
2. The method for filtering noise and judging cracks of the steel rail crack on-line monitoring of the claim 1, wherein in the step S01, the sound signal of the steel rail is judged whether to be a friction signal or not so as to form the slow propagation stage judgment of the crack.
3. The method for filtering noise and judging the cracks of the steel rail according to claim 2, wherein if the sound signal of the steel rail is a signal that the peaks and the valleys interact to form concave-convex staggering, the peaks of the staggering part can emit a series of acoustic emission signals of elastic waves, and the sound signal of the steel rail is a friction signal.
4. The method for online monitoring and noise filtering and crack determination of steel rail cracks according to any one of claims 1 to 3, wherein in step S02, the steel rail sound signal is decomposed into four layers by db6 wavelet to obtain 16 wavelet packet reconstructed signal components of (4,0), (4,1), (4,2), (4,3), (4,4), (4,5), (4,6), (4,7), (4,8), (4,9), (4,10), (4,11), (4,12), (4,13), (4,14) and (4,15), and time t, frequency f and wavelet coefficient W are obtained.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101221151A (en) * | 2007-12-21 | 2008-07-16 | 浙江大学 | Sound wave detecting method for catalyzer coke content in reactor |
| CN101435799A (en) * | 2008-12-19 | 2009-05-20 | 清华大学 | Failure diagnosis method and apparatus of hydroturbine based on acoustic emission technology |
| CN102928517A (en) * | 2012-11-15 | 2013-02-13 | 河北省电力公司电力科学研究院 | Method for denoising acoustic testing data of porcelain insulator vibration based on wavelet decomposition threshold denoising |
| CN103808810A (en) * | 2014-03-14 | 2014-05-21 | 哈尔滨工业大学 | Steel rail crack acoustic emission feature extraction method based on wavelet time-frequency spectrum and group velocity curve |
| CN103901111A (en) * | 2014-03-24 | 2014-07-02 | 上海电机学院 | Nondestructive detection system and method of blades of wind turbine generator system |
| CN104634878A (en) * | 2014-12-16 | 2015-05-20 | 北京林业大学 | Wood damage monitoring method based on acoustic emission technique |
-
2016
- 2016-12-09 CN CN201611127509.8A patent/CN106645424B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101221151A (en) * | 2007-12-21 | 2008-07-16 | 浙江大学 | Sound wave detecting method for catalyzer coke content in reactor |
| CN101435799A (en) * | 2008-12-19 | 2009-05-20 | 清华大学 | Failure diagnosis method and apparatus of hydroturbine based on acoustic emission technology |
| CN102928517A (en) * | 2012-11-15 | 2013-02-13 | 河北省电力公司电力科学研究院 | Method for denoising acoustic testing data of porcelain insulator vibration based on wavelet decomposition threshold denoising |
| CN103808810A (en) * | 2014-03-14 | 2014-05-21 | 哈尔滨工业大学 | Steel rail crack acoustic emission feature extraction method based on wavelet time-frequency spectrum and group velocity curve |
| CN103901111A (en) * | 2014-03-24 | 2014-07-02 | 上海电机学院 | Nondestructive detection system and method of blades of wind turbine generator system |
| CN104634878A (en) * | 2014-12-16 | 2015-05-20 | 北京林业大学 | Wood damage monitoring method based on acoustic emission technique |
Non-Patent Citations (2)
| Title |
|---|
| 基于小波包分析的拉索损伤声发射信号特征提取;邓扬 等;《振动与冲击》;20101231;第29卷(第6期);全文 * |
| 小波变换在声发射检测中的应用;张平 等;《无损检测》;20021031;第24卷(第10期);全文 * |
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