CN114252509A - A three-stage locking type landslide precursor identification method based on acoustic emission signal - Google Patents

A three-stage locking type landslide precursor identification method based on acoustic emission signal Download PDF

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CN114252509A
CN114252509A CN202111549873.4A CN202111549873A CN114252509A CN 114252509 A CN114252509 A CN 114252509A CN 202111549873 A CN202111549873 A CN 202111549873A CN 114252509 A CN114252509 A CN 114252509A
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陈辉
杨述梅
李蕊
胡英
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Chengdu Univeristy of Technology
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Abstract

本发明公开了一种基于声发射信号的三段式锁固型滑坡前兆识别方法，包括以下步骤：S1、输入声发射信号xl(t)，l＝1,2,…,m，取l＝1；S2、对xl(t)的拟合曲线

Figure DDA0003416865270000011
寻峰：
Figure DDA0003416865270000012
计算最大峰值
Figure DDA0003416865270000013
和满足
Figure DDA0003416865270000014
的峰数n；S3、若1<n≤N，则执行S4，若n＝＝1，为突发型信号，执行S6，否则为连续型信号，执行S6；S4、若峰与峰相邻，则执行S5，否则为连续型信号，执行S6；S5、计算相邻峰之间的距离Lj，若
Figure DDA0003416865270000015
则为突发型信号，否则为连续型信号；S6、计算l＝l+1，若l>m，执行S7，否则返回S2；S7、根据上述分类结果，分别提取两类信号的特征参数；S8、分析上述提取的特征参数，识别三段式锁固型滑坡前兆信息。本发明可以有效挖掘声发射信号的岩体破裂前兆特征，为三段式锁固型滑坡预警提供科学依据。

Figure 202111549873

The invention discloses a three-segment locking type landslide precursor identification method based on acoustic emission signals, comprising the following steps: S1, input acoustic emission signals x l (t), l=1, 2,...,m, take l=1 ; S2, fitting curve to x l (t)

Figure DDA0003416865270000011
Peak search:
Figure DDA0003416865270000012
Calculate the maximum peak
Figure DDA0003416865270000013
and satisfied
Figure DDA0003416865270000014
The number of peaks is n; S3, if 1<n≤N, execute S4, if n==1, it is a burst signal, execute S6, otherwise it is a continuous signal, execute S6; S4, if the peak is adjacent to the peak , then execute S5, otherwise it is a continuous signal, execute S6; S5, calculate the distance L j between adjacent peaks, if
Figure DDA0003416865270000015
Then it is a burst type signal, otherwise it is a continuous type signal; S6, calculate l=l+1, if l>m, execute S7, otherwise return to S2; S7, according to the above classification results, extract the characteristic parameters of the two types of signals respectively; S8, analyze the above-mentioned extracted characteristic parameters, and identify the three-stage locking type landslide precursor information. The invention can effectively excavate the precursory characteristics of the rock mass rupture of the acoustic emission signal, and provide a scientific basis for the three-stage locking type landslide warning.

Figure 202111549873

Description

Three-section type locking landslide precursor identification method based on acoustic emission signals
Technical Field
The invention belongs to the field of signal processing and analysis, and particularly relates to a three-section type locking landslide precursor identification method based on acoustic emission signals.
Background
The rock is continuously cracked under the action of external load, and part of energy is released in the form of transient elastic waves, and the phenomenon is called Acoustic Emission (AE). The acoustic emission analysis method mainly comprises two methods of parameter analysis and waveform analysis. The AE characteristic parameters are mainly used for analyzing the AE signal arrival time, duration, AE event rate, ringing count, amplitude (a), AE energy, Rise Time (RT), peak frequency and the like. The parameter analysis method is only used for carrying out simple statistical analysis on the waveform, and cannot acquire key information in the rock mass carried by the acoustic emission waveform; waveform analysis can more fully reflect the rock failure mechanism and fracture information, and research mainly focuses on the main frequency (He et al 2010) and amplitude-frequency characteristics (Ji et al 2012). However, the current research on acoustic emission signals mainly focuses on the analysis of the overall evolution characteristics of the acoustic emission signals. In recent years, some researchers have classified acoustic emission signals. For example, Zhang et al 2019 and Wang Chuang et al 2020 classify acoustic emission signals by dominant and secondary dominant frequency characteristics. Liu hua (Liu 2008) and Zhao jinglong (Zhao 2010) separate the acoustic emission signals into burst type signals and continuous type signals according to their waveform characteristics. The burst type signal is a waveform that can be separated in the time domain, and the waveform is a pulse waveform. When a plurality of events occur simultaneously, the waveforms cannot be separated in the time domain, and become continuous signals. If the focus is limited to the AE global property only, the potential rock fracture information is easily lost.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a three-section type locking landslide precursor identification method based on acoustic emission signals. According to the method, the wave characteristics of the acoustic emission signals are researched and divided into burst type signals and continuous type signals, the characteristic parameter evolution rules of the signals are respectively researched, and the cracking precursor information is extracted. Compared with the traditional method, the method for identifying the precursor based on acoustic emission signal classification can excavate potential cracking precursor information and provide scientific basis for early warning.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a three-section type locking landslide precursor identification method based on acoustic emission signals comprises the following steps:
s1, inputting acoustic emission signals to be analyzed, and respectively using xl(t) represents, wherein l is 1,2, …, m, xl(t) is the first acoustic emission signal to be analyzed, m is the total number of the acoustic emission signals to be analyzed, and l is taken as 1;
s2, acoustic emission signal xl(t) fitting the envelope of the waveform, and fitting the waveform
Figure BDA0003416865250000021
The peak searching is carried out, namely, the peak value (amplitude:
Figure BDA0003416865250000022
) And peak position (time: t is ti) And (4) capturing. Calculating the maximum peak value as
Figure BDA0003416865250000023
Statistics of
Figure BDA0003416865250000024
The number n of the peak values in (1) is obtained, wherein k is an empirical parameter, k belongs to (0,1), I is the serial number of the fitting waveform peak, I is 1,2, …, and I are the total number of the fitting waveform peak;
s3, if 1< N ≦ N, performing S4, if N ≦ 1, then performing S6 for burst type signal, otherwise performing S6 for continuous type signal, where N is empirical parameter;
s4, judgment of satisfaction
Figure BDA0003416865250000025
If so, performing S5, otherwise, performing a continuous signal (performing S6);
s5, calculating to satisfy
Figure BDA0003416865250000026
Is the distance L between adjacent peaksjJ-1, …, n-1, wherein L1Represents satisfaction
Figure BDA0003416865250000027
A distance between the first peak and the second peak in the condition; l is2Represents satisfaction
Figure BDA0003416865250000028
The distance between the second and third peaks in the condition, and so on. If it is
Figure BDA0003416865250000029
Then it is a burst type signal, otherwise it is a continuous type signal, where L is an empirical value obtained by statistical analysis of waveform characteristics;
s6, calculating l to l +1, if l is greater than m, all the acoustic emission signals to be analyzed are analyzed, executing S7, and otherwise, returning to S2;
s7, respectively extracting characteristic parameters (event rate, accumulated event number, waveform maximum amplitude and dominant frequency) of the two types of signals according to the obtained acoustic emission signal classification result:
the acoustic emission event rate refers to the number of acoustic emission signals released per second, and reflects the activity and crack propagation condition of the acoustic emission signals; the accumulated event number refers to the number of the acoustic emission signals accumulated at the current moment and reflects the increasing trend of the acoustic emission signals.
The maximum amplitude of the waveform is:
xl(t)max＝max[xl(t)] (1)
wherein t represents time, xl(t) is the l-th acoustic emission signal to be analyzed.
The calculation method of the dominant frequency is as follows:
Figure BDA0003416865250000031
where t represents time and f represents frequency. I Xl(f) L is the signal xl(t) obtaining the maximum value | X of the amplitude spectruml(fi)|maxCorresponding frequency fiI.e. the dominant frequency.
S8, analyzing the characteristic parameters extracted in the steps, and identifying the three-section type locking landslide precursor information:
the burst type signal predicts the generation of microcracks, so the generation of the burst type signal can be used as the information of a crack precursor;
and secondly, in the AE signals, high frequency corresponds to the initiation of micro cracks, and low frequency corresponds to the formation of large cracks. Therefore, the generation of high-frequency acoustic emission signals can also be used as rupture precursor information;
and thirdly, the sudden increase of the acoustic emission event rate indicates that the rock mass cracks are expanded unstably and can be used as cracking precursor information.
Preferably, the step S2 is to signal xl(t) the method for envelope fitting of the waveform is: for signal xl(t) searching peaks for the waveform, and connecting the peaks by a smooth curve by using a cubic spline interpolation method;
preferably, the step S2 calculates the maximum peak of the fitting waveform as
Figure BDA0003416865250000032
Statistics of
Figure BDA0003416865250000033
Figure BDA0003416865250000034
Where k is an empirical parameter obtained by analyzing the waveform characteristics of the acoustic emission data, and k belongs to (0, 1). According to the characteristics of the acoustic emission signal of the three-section locking model test, the continuous signal peak value is almost at a higher level, and the burst signal peak value is foundAlthough the waveform is a pulse waveform, some peaks may appear near the maximum peak
Figure BDA0003416865250000041
Thus, k is 0.5;
preferably, the step S3 judges that the number 1 is<N is less than N, S4 is performed, if N is 1, it is a burst type signal (S6) and otherwise it is a continuous type signal (S6), where N is an empirical parameter. The characteristic analysis of the acoustic emission signal of the three-section locking type model test shows that the waveform of the burst type signal is near the maximum peak value, and some peak values larger than or equal to the maximum peak value may appear
Figure BDA0003416865250000042
And the appearance positions of the peaks are mostly one on the left side and the right side of the maximum peak, so that N is 3. In view of the above, 1<N is less than or equal to N, the waveform may be a burst type signal, and further judgment is needed. It is noted that when only one peak has a peak value equal to or greater than
Figure BDA0003416865250000043
I.e., n is 1, the current waveform must be burst-type. A peak value having a peak value of N or more
Figure BDA0003416865250000044
I.e. n>When N, the current waveform is a continuous type;
preferably, the step S4 judges that the condition is satisfied
Figure BDA0003416865250000045
If so, performing S5, otherwise, performing a continuous signal (performing S6); as can be seen from the above, the burst-type signal waveform may have some peaks larger than or equal to the maximum peak value
Figure BDA0003416865250000046
And the position of the peak is mostly one on the left and right sides. When the waveform satisfies a peak value of a plurality of peaks is equal to or more than
Figure BDA0003416865250000047
Whether peaks are adjacent or not needs to be considered, if the peaks are adjacent, the condition that the peaks are near the maximum peak value is met, and the signals are burst type signals, otherwise, the signals are continuous type signals;
preferably, the step S5 determines the peak position according to the distance between adjacent peaks
Figure BDA0003416865250000048
And L, which is a more appropriate empirical value obtained by performing statistical analysis on the waveform characteristics, and further determining the waveform type of the waveform. In order to improve the accuracy of judgment, the invention judges the distance between adjacent peaks
Figure BDA0003416865250000049
When the wave form is in use, the distance between the peak values of the wave form is smaller, and the peak value is more than or equal to the maximum peak value near the maximum peak value which is more consistent with the burst type
Figure BDA00034168652500000410
Peak and pulse waveform characteristics. According to the characteristics of the acoustic emission signal of the three-section type locking model test, taking L as 100;
preferably, in step S8, the generation of the burst-type signal, the generation of the high-frequency acoustic emission signal, and the sudden increase of the acoustic emission time rate are used as the cracking precursor information, and the two types of acoustic emission signal evolution characteristics and the generation principle are obtained according to the three-stage locking type landslide model test.
The idea of the invention is as follows:
firstly, inputting acoustic emission signals to be analyzed, respectively using xl(t) represents, wherein l is 1,2, …, m, xl(t) is the first acoustic emission signal to be analyzed, m is the total number of the acoustic emission signals to be analyzed, and l is taken as 1;
second, for acoustic emission signal xl(t) fitting the envelope of the waveform, and fitting the waveform
Figure BDA0003416865250000051
Performing peak searching, i.e. realizing the peak value (Amplitude value:
Figure BDA0003416865250000052
) And peak position (time: t is ti) And (4) capturing. Calculating the maximum peak value as
Figure BDA0003416865250000053
Statistics of
Figure BDA0003416865250000054
The number n of the peak values in (1) is obtained, wherein k is an empirical parameter, k belongs to (0,1), I is the serial number of the fitting waveform peak, I is 1,2, …, and I are the total number of the fitting waveform peak;
thirdly, if 1< N is less than or equal to N, executing the fourth step, if N is 1, then the signal is a burst type signal (executing the sixth step), otherwise the signal is a continuous type signal (executing the sixth step), wherein N is an empirical parameter;
fourth, it is judged that
Figure BDA0003416865250000055
If so, executing the fifth step, otherwise, executing the continuous signal (executing the sixth step);
fifth, calculate the satisfaction
Figure BDA0003416865250000056
Is the distance L between adjacent peaksjJ-1, …, n-1, wherein L1Represents satisfaction
Figure BDA0003416865250000057
A distance between the first peak and the second peak in the condition; l is2Represents satisfaction
Figure BDA0003416865250000058
The distance between the second and third peaks in the condition, and so on. If it is
Figure BDA0003416865250000059
Then a burst type signal, otherwise a continuous type signal, where L is obtained by statistical analysis of waveform characteristicsAn empirical value of (d);
sixthly, calculating l to be l +1, if l is larger than m, all the acoustic emission signals to be analyzed are analyzed, executing the seventh step, and otherwise, returning to the second step;
seventhly, respectively extracting characteristic parameters (event rate, accumulated event number, maximum waveform amplitude and dominant frequency) of the two types of signals according to the obtained acoustic emission signal classification result;
eighthly, analyzing the characteristic parameters extracted in the steps, and identifying the three-section type locking landslide precursor information:
the burst type signal predicts the generation of microcracks, so the generation of the burst type signal can be used as the information of a crack precursor;
and secondly, in the AE signals, high frequency corresponds to the initiation of micro cracks, and low frequency corresponds to the formation of large cracks. Therefore, the generation of high-frequency acoustic emission signals can also be used as rupture precursor information;
and thirdly, the sudden increase of the acoustic emission event rate indicates that the rock mass cracks are expanded unstably and can be used as cracking precursor information.
The working principle of the invention is as follows: inputting acoustic emission signals to be analyzed, calculating total number m, using x for acoustic emission signals to be analyzedl(t) is 1,2, …, m, where l is 1; for acoustic emission signal xl(t) fitting the envelope of the waveform, and fitting the waveform
Figure BDA0003416865250000061
The peak searching is carried out, namely, the peak value (amplitude:
Figure BDA0003416865250000062
) And peak position (time: t is ti) And (4) capturing. Calculating the maximum peak value as
Figure BDA0003416865250000063
Statistics of
Figure BDA0003416865250000064
The number of peak values n; if 1<N is less than or equal to N, the next step is executed, if N is 1, the signal is a burst type signal (the sixth step is executed: extractingTaking characteristic parameters), otherwise, the signal is a continuous signal (execute the sixth step: extracting characteristic parameters); judging satisfaction
Figure BDA0003416865250000065
If so, executing the next step, otherwise, executing a continuous signal (executing a sixth step, extracting characteristic parameters); computing satisfaction
Figure BDA0003416865250000066
Is the distance L between adjacent peaksjJ is 1, …, n-1, if
Figure BDA0003416865250000067
The signal is a burst type signal, otherwise, the signal is a continuous type signal; calculating l as l +1, if l>m, executing the next step, otherwise returning to the second step (for x)l(t) envelope fitting of the waveform); respectively extracting characteristic parameters (event rate, accumulated event number, maximum waveform amplitude and dominant frequency) of the two types of signals according to the obtained acoustic emission signal classification result; analyzing the extracted characteristic indexes and identifying the three-section locking type landslide precursor information. The method can dig out potential rock mass fracture information and provide a basis for three-section locking type landslide precursor identification.
The invention provides a three-section type locking landslide precursor identification method based on acoustic emission signals, aiming at covering potential rock mass fracture information by the overall characteristics of the acoustic emission signals. The wave analysis of the acoustic emission signal of the three-section locking type model test shows that the peak value of the continuous signal is almost at a higher level, the distance between the peaks is longer, the overall wave form of the burst type signal is a pulse wave form, but a higher peak value possibly appears near the maximum peak value, and the three-section locking type model test has the characteristics of less quantity, adjacent to the maximum peak value and shorter distance between the peaks. Classifying the acoustic emission signals according to the difference between the burst type signals and the continuous type signals in the aspect of waveforms; respectively extracting characteristic parameters of the two types of signals according to the classification result; and finally, providing corresponding rupture precursor information according to the evolution characteristics of the characteristic parameters. The method has good effect in the precursor identification of the three-section locking type landslide indoor test, and can identify effective precursor characteristics before the rock body is unstable, particularly before cracks are generated.
Drawings
FIG. 1 is a flow chart of the present invention
FIG. 2a is a graph showing an original waveform and a fitted waveform of a burst-type signal, in which a solid line is the original waveform and a dotted line is the fitted waveform
FIG. 2b shows an original waveform and a fitting waveform of a continuous signal, wherein the solid line is the original waveform and the dotted line is the fitting waveform
FIG. 3a is a graph showing the evolution of event rate and the number of accumulated events of a burst-type signal of a three-stage locked landslide model test processed by the method of the present invention, wherein the solid line is the event rate and the dotted line is the number of accumulated events
FIG. 3b is a graph showing the evolution of event rate and the number of accumulated events of a continuous signal of a three-stage locked landslide model test processed by the method of the present invention, wherein the solid line is the event rate and the dotted line is the number of accumulated events
FIG. 4a is a diagram showing a waveform maximum amplitude distribution of a burst type signal of a three-stage locking landslide model test processed by the method of the present invention
FIG. 4b is the waveform maximum amplitude distribution diagram of the continuous signal of the three-stage locked landslide model test processed by the method of the present invention
FIG. 5a is a graph of the main frequency distribution of burst type signals of a three-stage locked landslide model test processed by the method of the present invention
FIG. 5b is a schematic diagram of a main frequency distribution of a continuous signal of a three-stage locked landslide model test processed by the method of the present invention
Detailed Description
The invention will be further explained with reference to the drawings.
Example 1: referring to fig. 1, a three-stage locking type landslide precursor identification method based on acoustic emission signals includes the following steps:
s1, inputting acoustic emission signals to be analyzed, and respectively using xl(t) represents, wherein l is 1,2, …, m, xl(t) is the first acoustic emission signal to be analyzed, and m is the acoustic emission signal to be analyzedTaking 1 as the total number of the acoustic emission signals;
s2, acoustic emission signal xl(t) fitting the envelope of the waveform, and fitting the waveform
Figure BDA0003416865250000081
The peak searching is carried out, namely, the peak value (amplitude:
Figure BDA0003416865250000082
) And peak position (time: t is ti) And (4) capturing. Calculating the maximum peak value as
Figure BDA0003416865250000083
Statistics of
Figure BDA0003416865250000084
The number n of the peak values in (1) is obtained, wherein k is an empirical parameter, k belongs to (0,1), I is the serial number of the fitting waveform peak, I is 1,2, …, and I are the total number of the fitting waveform peak;
s3, if 1< N ≦ N, performing S4, if N ≦ 1, then performing S6 for burst type signal, otherwise performing S6 for continuous type signal, where N is empirical parameter;
s4, judgment of satisfaction
Figure BDA0003416865250000085
If so, performing S5, otherwise, performing a continuous signal (performing S6);
s5, calculating to satisfy
Figure BDA0003416865250000086
Is the distance L between adjacent peaksjJ-1, …, n-1, wherein L1Represents satisfaction
Figure BDA0003416865250000087
A distance between the first peak and the second peak in the condition; l is2Represents satisfaction
Figure BDA0003416865250000088
Second peak in conditionsDistance from the third peak, and so on. If it is
Figure BDA0003416865250000089
Then it is a burst type signal, otherwise it is a continuous type signal, where L is an empirical value obtained by statistical analysis of waveform characteristics;
s6, calculating l to l +1, if l is greater than m, all the acoustic emission signals to be analyzed are analyzed, executing S7, and otherwise, returning to S2;
s7, respectively extracting characteristic parameters (event rate, accumulated event number, waveform maximum amplitude and dominant frequency) of the two types of signals according to the obtained acoustic emission signal classification result:
the acoustic emission event rate refers to the number of acoustic emission signals released per second, and reflects the activity and crack propagation condition of the acoustic emission signals; the accumulated event number refers to the number of the acoustic emission signals accumulated at the current moment and reflects the increasing trend of the acoustic emission signals.
The maximum amplitude of the waveform is:
xl(t)max＝max[xl(t)] (1)
wherein t represents time, xl(t) is the l-th acoustic emission signal to be analyzed.
The calculation method of the dominant frequency is as follows:
Figure BDA0003416865250000091
where t represents time and f represents frequency. I Xl(f) L is the signal xl(t) obtaining the maximum value | X of the amplitude spectruml(fi)|maxCorresponding frequency fiI.e. the dominant frequency.
S8, analyzing the characteristic parameters extracted in the steps, and identifying the three-section type locking landslide precursor information:
the burst type signal predicts the generation of microcracks, so the generation of the burst type signal can be used as the information of a crack precursor;
and secondly, in the AE signals, high frequency corresponds to the initiation of micro cracks, and low frequency corresponds to the formation of large cracks. Therefore, the generation of high-frequency acoustic emission signals can also be used as rupture precursor information;
and thirdly, the sudden increase of the acoustic emission event rate indicates that the rock mass cracks are expanded unstably and can be used as cracking precursor information.
Preferably, the step S2 is to signal xl(t) the method for envelope fitting of the waveform is: for signal xl(t) searching peaks for the waveform, and connecting the peaks by a smooth curve by using a cubic spline interpolation method;
preferably, the step S2 calculates the maximum peak of the fitting waveform as
Figure BDA0003416865250000092
Statistics of
Figure BDA0003416865250000093
Figure BDA0003416865250000094
Where k is an empirical parameter obtained by analyzing the waveform characteristics of the acoustic emission data, and k belongs to (0, 1). According to the characteristics of acoustic emission signals of a three-section locking type model test, the continuous type signal peak value is almost at a higher level, and although the burst type signal is a pulse waveform, some peak values larger than or equal to the maximum peak value may appear near the maximum peak value
Figure BDA0003416865250000095
Thus, k is 0.5;
preferably, the step S3 judges that the number 1 is<N is less than N, S4 is performed, if N is 1, it is a burst type signal (S6) and otherwise it is a continuous type signal (S6), where N is an empirical parameter. The characteristic analysis of the acoustic emission signal of the three-section locking type model test shows that the waveform of the burst type signal is near the maximum peak value, and some peak values larger than or equal to the maximum peak value may appear
Figure BDA0003416865250000096
And the position of occurrence is mostly the maximumOne on each side of the peak, so that N is 3. In view of the above, 1<N is less than or equal to N, the waveform may be a burst type signal, and further judgment is needed. It is noted that when only one peak has a peak value equal to or greater than
Figure BDA0003416865250000101
I.e., n is 1, the current waveform must be burst-type. A peak value having a peak value of N or more
Figure BDA0003416865250000102
I.e. n>When N, the current waveform is a continuous type;
preferably, the step S4 judges that the condition is satisfied
Figure BDA0003416865250000103
If so, performing S5, otherwise, performing a continuous signal (performing S6); as can be seen from the above, the burst-type signal waveform may have some peaks larger than or equal to the maximum peak value
Figure BDA0003416865250000104
And the position of the peak is mostly one on the left and right sides. When the waveform satisfies a peak value of a plurality of peaks is equal to or more than
Figure BDA0003416865250000105
Whether peaks are adjacent or not needs to be considered, if the peaks are adjacent, the condition that the peaks are near the maximum peak value is met, and the signals are burst type signals, otherwise, the signals are continuous type signals;
preferably, the step S5 determines the peak position according to the distance between adjacent peaks
Figure BDA0003416865250000106
And L, which is a more appropriate empirical value obtained by performing statistical analysis on the waveform characteristics, and further determining the waveform type of the waveform. In order to improve the accuracy of judgment, the invention judges the distance between adjacent peaks
Figure BDA0003416865250000107
When the wave form is in use, the distance between the peak values of the wave form is smaller, and the peak value is more than or equal to the maximum peak value near the maximum peak value which is more consistent with the burst type
Figure BDA0003416865250000108
Peak and pulse waveform characteristics. According to the characteristics of the acoustic emission signal of the three-section type locking model test, taking L as 100;
preferably, in step S8, the generation of the burst-type signal, the generation of the high-frequency acoustic emission signal, and the sudden increase of the acoustic emission time rate are used as the cracking precursor information, and the two types of acoustic emission signal evolution characteristics and the generation principle are obtained according to the three-stage locking type landslide model test.
Referring to fig. 1 to 5, we take the acoustic emission signal of a three-stage locking model test as an example. Fig. 2 is a diagram of original waveforms of burst type signal and continuous type signal and their fitting waveforms, respectively, in which a solid line represents the original waveform and a dotted line represents the fitting waveform. FIG. 3 is a graph of the evolution of acoustic emission event rate and cumulative event number, wherein the solid line represents the acoustic emission event rate, the dashed line represents the cumulative event number, the abscissa represents time, and the ordinate represents the event number; FIG. 4 is a diagram of a maximum amplitude profile of a waveform, with time on the abscissa and amplitude on the ordinate; fig. 5 is a diagram of a main frequency distribution of acoustic emission, with the abscissa representing time and the ordinate representing frequency. The embodiment proves that the two types of signal characteristic evolution law diagrams obtained after the classification method provided by the invention are obviously different, the continuous type signal is obviously more than the burst type signal, and the burst type signal starts to be generated at 147.8s, which indicates the generation of cracks in the rock mass; high frequency acoustic emission signals are generated by microcracks, with high frequency burst-type signals and high frequency continuous-type signals being generated at 147.8s and 150.9s, respectively. The event rates of the two types of acoustic emission signals are obviously increased at 163s, and the sudden increase of the acoustic emission events indicates that the rock mass crack is converted from stable expansion to unstable expansion. The analysis shows that the method can effectively identify the rupture precursor information, and the classified burst type signal can respond to the rupture precursor information earlier.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (7)

1. A three-section type locking landslide precursor identification method based on acoustic emission signals is characterized by comprising the following steps:
s1, inputting acoustic emission signals to be analyzed, and respectively using xl(t) represents, wherein l ═ 1,2l(t) is the first acoustic emission signal to be analyzed, m is the total number of the acoustic emission signals to be analyzed, and l is taken as 1;
s2, acoustic emission signal xl(t) fitting the envelope of the waveform, and fitting the waveform
Figure FDA0003416865240000019
The peak searching is carried out, namely, the peak value (amplitude:
Figure FDA0003416865240000011
and peak position (time: t)i) Capturing, calculating the maximum peak value as
Figure FDA0003416865240000012
Statistics of
Figure FDA0003416865240000013
The number n of the peak values in (1) is obtained, wherein k is an empirical parameter, k belongs to (0,1), I is a serial number of the fitting waveform peak, and I is 1, 2.
S3, if N is greater than 1 and less than N, executing S4, if N is equal to 1, then the signal is a burst type signal (executing S6), otherwise the signal is a continuous type signal (executing S6), wherein N is an empirical parameter;
s4, judgment of satisfaction
Figure FDA0003416865240000014
If so, performing S5, otherwise, performing a continuous signal (performing S6);
s5, calculating to satisfy
Figure FDA0003416865240000015
Is the distance L between adjacent peaksj1, n-1, wherein L1Represents satisfaction
Figure FDA0003416865240000016
A distance between the first peak and the second peak in the condition; l is2Represents satisfaction
Figure FDA0003416865240000017
The distance between the second peak and the third peak in the condition is analogized if
Figure FDA0003416865240000018
Then it is a burst type signal, otherwise it is a continuous type signal, where L is an empirical value obtained by statistical analysis of waveform characteristics;
s6, calculating l to be l +1, if l is larger than m, all the acoustic emission signals to be analyzed are analyzed, executing S7, and otherwise, returning to S2;
s7, respectively extracting characteristic parameters (event rate, accumulated event number, waveform maximum amplitude and dominant frequency) of the two types of signals according to the obtained acoustic emission signal classification result:
the acoustic emission event rate refers to the number of acoustic emission signals released per second, and reflects the activity and crack propagation condition of the acoustic emission signals; the accumulated event number refers to the number of the acoustic emission signals accumulated at the current moment and reflects the growth trend of the acoustic emission signals;
the maximum amplitude of the waveform is:
xl(t)max＝max[xl(t)] (1)
wherein t represents time, xl(t) the first acoustic emission signal to be analyzed;
the calculation method of the dominant frequency is as follows:
Figure FDA0003416865240000021
where t represents time, f represents frequency, | Xl(f) L is the signal xl(t) obtaining the maximum value | X of the amplitude spectruml(fi)|maxCorresponding frequency fiThe frequency is the dominant frequency;
s8, analyzing the characteristic parameters extracted in the steps, and identifying the three-section type locking landslide precursor information:
the burst type signal predicts the generation of microcracks, so the generation of the burst type signal can be used as the information of a crack precursor;
in the acoustic emission signal, high frequency corresponds to the initiation of micro cracks, and low frequency corresponds to the formation of large cracks, so that the generation of the high frequency acoustic emission signal can also be used as cracking precursor information;
and thirdly, the sudden increase of the acoustic emission event rate indicates that the rock mass cracks are expanded unstably and can be used as cracking precursor information.
2. The method for three-stage acoustic emission signal-based landslide precursor recognition according to claim 1, wherein said step S2 is performed on signal xl(t) the method for envelope fitting of the waveform is: for signal xl(t) the waveform is subjected to peak searching, and the peaks are connected by a smooth curve by a cubic spline interpolation method.
3. The method for identifying three-stage locking type landslide precursors based on acoustic emission signals as claimed in claim 1, wherein said step S2 is to calculate the maximum peak value of the fitting waveform as
Figure FDA0003416865240000022
Statistics of
Figure FDA0003416865240000023
Figure FDA0003416865240000024
The number n of the peak values of (a), wherein k is an empirical parameter obtained by analyzing the waveform characteristics of the acoustic emission data, and belongs to (0, 1); according to the characteristics of acoustic emission signals of a three-section locking type model test, the continuous type signal peak value is almost at a higher level, and although the burst type signal is a pulse waveform, some peak values larger than or equal to the maximum peak value may appear near the maximum peak value
Figure FDA0003416865240000031
Therefore, k is 0.5.
4. The method of claim 1, wherein the step S3 is performed to determine if 1< N ≦ N, and perform S4 if N ≦ 1, and perform S6 if N ≦ 1, or perform S6 if N is a burst type signal; the characteristic analysis of the acoustic emission signal of the three-section locking type model test shows that the waveform of the burst type signal is near the maximum peak value, and some peak values larger than or equal to the maximum peak value may appear
Figure FDA0003416865240000032
Most of the appeared positions are one on the left side and the right side of the maximum peak, so that N is selected to be 3; considering the above situation, if N is more than 1 and less than or equal to N, the waveform may be a burst type signal, and further judgment is needed; it is noted that when only one peak has a peak value equal to or greater than
Figure FDA0003416865240000033
When n is 1, the current waveform is definitely in a burst type; a peak value having a peak value of N or more
Figure FDA0003416865240000034
I.e., N > N, the current waveform is continuous.
5. The method for three-stage acoustic emission signal-based landslide precursor recognition according to claim 1, wherein said step S4Judging satisfaction
Figure FDA0003416865240000035
If so, performing S5, otherwise, performing a continuous signal (performing S6); as can be seen from the above, the burst-type signal waveform may have some peaks larger than or equal to the maximum peak value
Figure FDA0003416865240000036
The peak of (2) and the position of the peak is mostly one on the left and right sides; when the waveform satisfies a peak value of a plurality of peaks is equal to or more than
Figure FDA0003416865240000037
Whether peaks are adjacent or not needs to be considered, if the peaks are adjacent, the condition that the peaks are near the maximum peak value is met, and the signal is a burst type signal, otherwise, the signal is a continuous type signal.
6. The method for identifying a three-stage locking-type landslide precursor according to claim 1, wherein said step S5 is performed based on the distance between adjacent peaks
Figure FDA0003416865240000038
And L, and further determining the waveform type of the waveform, wherein L is a more appropriate empirical value obtained by carrying out statistical analysis on the waveform characteristics; in order to improve the accuracy of judgment, the invention judges the distance between adjacent peaks
Figure FDA0003416865240000039
When the wave form is in use, the distance between the peak values of the wave form is smaller, and the peak value is more than or equal to the maximum peak value near the maximum peak value which is more consistent with the burst type
Figure FDA0003416865240000041
The peak and pulse waveform characteristics of (a); and taking L as 100 according to the characteristics of the acoustic emission signal of the three-section locking model test.
7. The method for identifying the three-stage locking type landslide precursor based on acoustic emission signals as claimed in claim 1, wherein the step S8 uses the generation of burst type signal, the generation of high frequency acoustic emission signal and the sudden increase of acoustic emission time rate as the information of the rupture precursor, and is obtained by testing the evolution characteristics and the generation principle of two types of acoustic emission signals according to the three-stage locking type landslide model.
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