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Research on Identification Technology of Explosive Vibration Based on EEMD Energy Entropy and Multiclassification SVM.

1. Introduction

The blasting vibration signal is a typical short-time nonstationary random signal. The characteristics of the blasting vibration signal can reflect the blasting type, blasting parameters, and site media information. The feature extraction of blasting vibration signal is the basis of studying blasting vibration prediction, structural response under blasting vibration excitation, and reducing blasting vibration. At present, the characteristics of blasting vibration signals mainly include peak characteristics, spectral features, time-holding characteristics, and energy characteristics. A large number of scholars have carried out in-depth research on the influence of characteristics and blasting parameters on signal characteristics. Min et al. [1] studied the strain law of buried pipeline under collapse vibration load. Through range analysis and variance significance analysis, it was found that the dynamic strain of buried pipeline under impact was more significant than that of collapse height and self-weight. Lu [2] obtained the energy distribution of each frequency band after multiscale lifting wavelet packet decomposition of the measured blasting vibration signal and summarized the energy distribution characteristics of the frequency band of the blasting vibration signal. Based on the dimension analysis theory, Zhong [3] discussed the influence factors of blasting vibration holding time and deduced the prediction formula of blasting vibration holding time.

With the deepening of research, a new branch of blasting vibration characteristics has appeared: energy entropy. Shannon referred to the concept of thermodynamics and called the average amount of information excluding redundancy "energy entropy" [4]. Energy entropy is an important concept in information theory and a description of the degree of system uncertainty. It can reflect the degree of random change of signals. In recent years, it has been widely used in mechanical fault diagnosis [5-7], speech signal processing [8], mine micro-earthquake signal recognition [9, 10], and other fields [11-13]. In this paper, EEMD algorithm was used to decompose the energy entropy of vibration signal, and the energy characteristics of each IMF are obtained as the input vector of SVM. Through the analysis and training of many kinds of vibration signals, the accurate prediction of vibration types was realized. The research results can provide technical reserves for urban vibration monitoring system and quick and accurate information of explosion source types for decision-makers of disaster prevention and mitigation command departments.

2. EEMD Energy Entropy

The EEMD (ensemble empirical mode decomposition) algorithm is a mature nonstationary random signal decomposition algorithm based on the EMD (empirical mode decomposition) algorithm. The original intention is to decompose any nonstationary random signal into multiple IMFs (intrinsic mode function), so as to provide feasibility for further Hilbert transform. EMD algorithm is different from the traditional wavelet and Fourier transform, its decomposition process has no fixed base, it has adaptive characteristics, and so it is famous for its efficiency and accuracy. But, the frequency aliasing effect of EMD algorithm has been perplexing scholars. The improved idea of EEMD is to add white noise at the beginning of EMD decomposition cycle, so as to solve the problem of frequency aliasing in traditional EMD algorithm.

The signal was decomposed by the EEMD algorithm to obtain a plurality of IMFs, which represent the characteristic distribution of the signal energy. Due to the difference of explosion source, initiation mode, and propagation medium of different types of explosion vibration signals, the energy distribution characteristics of IMFs are different.

Here we introduce the concept of EEMD energy entropy:

[H.sub.En] = -[n.summation over (i=1)] [E.sub.i]/E log [E.sub.i]/E, (1)

where E is the total energy of the signal and E; is the energy of each IMF. In this paper, - [E.sub.i]/Elog[E.sub.i]/E is called CEE (components of energy entropy).

Several vibration signals from different sample groups are selected for decomposition, and their respective energy entropies are calculated, as shown in Table 1. The signal acquisition parameters are shown in Table 2. A simple statistical analysis of the energy entropy data of four kinds of vibration shows that their statistical characteristics are significantly different. Therefore, the vibration energy entropy can be studied in depth as a characteristic parameter of vibration signal classification.

3. Multiclassification SVM

Support vector machine is a binary linear classifier with supervised learning mode [14]. As shown in the Figure 1, for a given input, the sample set S is linearly separable. Each sample contains multiple features, thus forming a feature space (2-dimensional space shown in the figure). The goal of the algorithm is to find the hyper plane with the largest margin [omega]X + b - 0 [15]. The decision boundary satisfying the condition actually constructs two parallel hyperplanes as the interval boundary to distinguish the classification of samples [16]:

[omega]X + b [greater than or equal to] 1, [??] y = 1, [omega]X + b [less than or equal to] 1, [??] = 1. (2)

All samples above the upper interval boundary belong to a positive class and those below the lower interval boundary belong to a negative class. The distance 2/[omega] between two interval boundaries is defined as the margin [17]. The samples of positive and negative classes on the boundary of interval are support vectors.

Support vector machine uses the hinge loss function to calculate the empirical risk. It adds the regularization module to optimize the structural risk and has good robustness [18]. For the nonlinear classification problem, SVM can learn through kernel function method, and the classification effect is still excellent [19].

As mentioned above, SVM is initially a linear classifier. With the in-depth study of scholars, SVM algorithm has been successfully extended to the field of nonlinear separability [20]. The principle is to map the nonlinear separable problem from the original feature space to a higher dimensional Hilbert space by using nonlinear functions [21]. There are hyperplanes [22] in space that can separate positive classes from negative classes as shown in Figure 2.

The standard SVM computing process was used to construct multiple decision boundaries in order to achieve multiclassification of samples [23]. The commonly used structures are "one-to-all (OAA-SVM)" and "one-to-one (OAO-SVM)". OAA-SVM establishes M decision boundaries for M classifications, and each decision boundary determines the attribution of one classification to all other classifications. OAO-SVM used in this paper is a voting method [24]. The calculation process is to establish decision boundaries for any two of the M classifications, that is, there are M(M - 1)/2 decision boundaries in total. The final sample category is determined by the score order of decision boundary.

4. Application of Experimental Data Analysis

In this paper, vibration data under four typical explosion scenarios are selected.

Scene 1: pipeline explosion vibration: at present, the global natural gas pipeline construction mileage has exceeded 370000 km [25]. The pressure of pipeline operation keeps increasing. Due to various reasons, natural gas pipeline explosion accidents occur frequently, which brings hidden dangers to the production safety of human society. Therefore, the author has carried out many full-scale high-pressure natural gas pipeline explosion tests, as shown in Figure 3, and recorded the pipeline explosion vibration data as a kind of sample for this study.

Scene 2: building collapse vibration: with the expansion of the city scale and the exhaustion of the design life of the building itself, the blasting demolition engineering of the high-rise building in the city is increasing day by day. The high-rise building disintegrates in the process of collapse, which causes violent vibration when hitting the ground, a serious threat to surrounding buildings. The research team collected a number of groups of building collapse vibration data as a kind of sample for this study, as shown in Figure 4.

Scene 3: surface rock blast vibration: rock blasting is a key method for the rapid and high-quality construction of large-scale infrastructure projects such as nuclear power plants, airports, tunnels, and ports. The high standard, high difficulty, and high strength of large-scale engineering construction put forward new challenges to the application of blasting technology. The impact of blasting vibration of subsequent projects on the projects that have been put into operation cannot be ignored. The research team obtained a large number of surface rock blast vibration data from the foundation pit excavation project of Fujian Fuqing Nuclear Power Plant for this study, as shown in Figure 5. The blast parameters are shown in Table 3.

Scene 4: blasting vibration in tunneling: because of its strong adaptability to geological conditions and low excavation cost, DBM (drilling and blasting method) is especially suitable for the construction of hard rock tunnel, broken rock tunnel, and a large number of short tunnels [27]. The vibration effect of DBM on the surrounding buildings, especially the subway lines which have been built and in operation, cannot be ignored. The research team had been stationed at the construction site of Nanjing Metro Tunnel for a long time, as shown in Figure 6, and collected the blasting vibration data of metro tunnel as a kind of sample for this study. The blast parameters are shown in Table 4.

The above data are collected by TC-4850 and Blast-UM vibration recorders, and the specific sampling parameters are shown in the table.

As mentioned above, the data samples in this study were divided into four categories, each of which takes 10 groups of data, 40 samples in total. In each training-prediction cycle, 30 groups of data were randomly selected as training sets, and the remaining 10 groups were detection sets. EEMD decomposition was carried out for each data. According to statistics, the number of IMF of 40 sample data was between 10 and 13. Considering that the main energy components were concentrated in the first several IMF layers, the CEE of the first nine IMF layers was taken and normalized to obtain the input matrix as shown in Table 5.

The feature matrix of 40 x 9 was input into OAO-SVM to train and test. The results are shown in Figure 7.

It can be seen from the figure that the prediction accuracy can be maintained above 80% in multiple training and tests. The prediction accuracy statistics of the four types of signals is shown in Table 6.

It can be seen from the statistical results that this method has high prediction accuracy for underground tunnel blast vibration and surface rock blast vibration. On the other hand, this algorithm often misjudges the pipeline explosion vibration. The cause of this phenomenon may be that the sample size was too small.

The EEMD-SVM algorithm provides us with a new means for vibration classification. Compared with commonly used BP neural network algorithms, the new method has the characteristics of high accuracy and short training time. The comparison between the two methods is shown in Table 7.

It can be seen from the table that SVM algorithm has certain advantages in prediction accuracy compared with neural network algorithm, while in training efficiency, it was much better than neural network algorithm.

Furthermore, the approximate entropy and permutation entropy are calculated on the basis of the inherent modal components as input eigenvectors, which are compared with the results of the algorithm in this paper, as shown in Table 8. It can be seen that, in terms of prediction accuracy and learning efficiency, the three algorithms are basically the same. However, due to the increase of approximate entropy and permutation entropy in the process of entropy calculation, the CPU time used in the construction of input eigenvectors is significantly increased, and it is highly related to the embedding dimension (permutation entropy) and reconstruction dimension (approximate entropy) parameters, so the comprehensive efficiency is at a disadvantage.

5. Discussion and Conclusion

In this study, through the cross-research of EEMD algorithm and SVM algorithm, combined with the training and prediction of 4 types of typical explosion vibration signals, the relevant conclusions are obtained as follows:

(1) EEMD energy entropy can be used as a significant feature for explosion vibration signal recognition

(2) Compared with the traditional BP neural network algorithm, the method in this paper has greater advantages in training efficiency and prediction accuracy

(3) Compared with permutation entropy and approximate entropy, the method in this paper has no obvious advantages in training efficiency and prediction accuracy, but the comprehensive efficiency of the algorithm has been improved.

The new algorithm can provide reference for urban vibration monitoring and help managers to know the type of accidents in a short time to make timely decisions.

Considering that the prediction accuracy of pipeline explosion vibration signal is insufficient, the author will further collect and sort out the data and expand the sample capacity, in order to achieve higher prediction accuracy.

https://doi.org/10.1155/2020/7893925

Data Availability

The data used in this paper can be obtained in the following link: hyperlink https://figshare.com/s/185b350ad6803157536c.

Conflicts of Interest

The authors declare that they have no conflicts of interest regarding this work.

Acknowledgments

This research was supported by the National Natural Science Foundation of China (nos. 11672331 and 51608530). The authors would like to gratefully acknowledge this support.

References

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Huayuan Ma, Xinghua Li [ID], Qiang Liu [ID], Xie Xingbo, Chong Ji, and Changxiao Zhao

Army Engineering University of PLA, Nanjing, China

Correspondence should be addressed to Xinghua Li; 379523589@qq.com and Qiang Liu; 3453815448@qq.com

Received 5 January 2020; Revised 16 March 2020; Accepted 20 July 2020; Published 8 August 2020

Academic Editor: Jiawei Xiang

Caption: Figure 1: Schematic diagram of SVM.

Caption: Figure 2: Nonlinear separable case.

Caption: Figure 3: Pipeline explosion vibration. (a) OD1422-12Mpa explosion experiment of natural gas pipeline [26]. (b) Vibration signal.

Caption: Figure 4: Demolition collapse vibration. (a) Blasting project of investment Plaza building in Jingzhou, Hubei Province. (b) Vibration signal.

Caption: Figure 5: Surface rock blast vibration. (a) "Hualong No. 1" foundation pit blasting project. (b) Vibration signal.

Caption: Figure 6: Underground tunnel blast vibration. (a) Blasting site of Nanjing metro line 3 tunnel. (b) Vibration signal.

Caption: Figure 7: Comparison between the prediction results of SVM and the real situation. (a) First operation. (b) Second operation. (c) Third operation. (d) Fourth operation.
Table 1: Comparison of energy entropy of typical vibration
signals.

Signal type                 Pipeline    Underground    Demolition
                           explosion    tunnel blast    collapse
                           vibration     vibration     vibration

Energyentropy  Signal 1      2.1656        1.9290        1.5578
               Signal 2      2.2454        2.3216        1.0854
               Signal 3      2.0622        2.2722        1.6836
               Signal 4      2.4368        2.3813        1.6146
    Expectation              2.2275        2.2260        1.4854
     Variance                0.1372        0.1758        0.2352

Signal type                 Surface rock
                           blast vibration

Energyentropy  Signal 1        1.5580
               Signal 2        1.9108
               Signal 3        1.7539
               Signal 4        2.0222
    Expectation                1.8112
     Variance                  0.1745

Table 2: Parameters of recorder.

Type of     Type of sensor   Number of       Frequency       Sampling
recorder                     channels    response range/Hz   rate/SPS

TC-4850     Piezoelectric        3             5~500           8000
Blast-UM    Piezoelectric        3             5~300          10000

Table 3: Parameters of rock blasting.

Hole spacing (m)   Row spacing   Hole diameter   Hole depth
                       (m)           (mm)           (m)

5.5                    3.2            115            13

Hole spacing (m)   Explosive mass   Resistance line
                        (kg)              (m)

5.5                      85               2.2

Hole spacing (m)     Explosive type     Delay time (ms)

5.5                Emulsion explosive         50
                        [phi]90

Table 4: Parameters of tunnel blasting.

Blasting method    Hole       Hole     Hole depth       Specific
                  spacing   diameter      (m)          consumption
                   (cm)       (mm)                  (kg * [m.sup.-3])

Cut blasting        50         40         1.2             0.6~1

Blasting method         Explosive type         Delay time (ms)

Cut blasting      Emulsion explosive [phi]32         50

Table 5: Input matrix.

                                           Eigenvectors

Signal            Serial     [CEE.sub.1]   [CEE.sub.2]   [CEE.sub.3]
                  number

Pipeline            1          0.0279        0.0075        0.0819
explosion           2          0.0303        0.0163        0.0059
vibration           3          0.0828        0.0027        0.0420
                    4          0.0013        0.0282        0.2993
                    5          0.0011        0.0162        0.0989
                    6          0.0022        0.0221        0.0175
                    7          0.0006        0.0047        0.0493
                    8          0.0239        0.0036        0.0242
                    9          0.0236        0.0907        0.0412
                    10         0.0049        0.0172        0.1697

Underground         11         0.5307        0.5300        0.3088
tunnel blast        12         0.3478        0.3438        0.5261
vibration           13         0.3844        0.2399        0.4719
                    14         0.3990        0.4214        0.5307
                    15         0.2209        0.2740        0.5307
                    16         0.4189        0.4115        0.5188
                    17         0.5304        0.5026        0.2967
                    18         0.3332        0.3404        0.5173
                    19         0.3610        0.5030        0.4422
                    20         0.4228        0.5306        0.4153

Demolition          21         0.0031        0.0040        0.0016
collapse            22         0.0006        0.0001        0.0012
vibration           23         0.0016        0.0004        0.0001
                    24         0.0010        0.0003        0.0002
                    25         0.0001        0.0001        0.0000
                    26         0.0007        0.0003        0.0001
                    27         0.0006        0.0002        0.0001
                    28         0.0004        0.0003        0.0034
                    29         0.0006        0.0002        0.0001
                    30         0.0010        0.0002        0.0001

Surface rock        31         0.0008        0.0003        0.0002
blast vibration     32         0.0010        0.0003        0.0002
                    33         0.0003        0.0001        0.0835
                    34         0.0003        0.0001        0.0125
                    35         0.0009        0.0004        0.0002
                    36         0.0006        0.0002        0.0085
                    37         0.0007        0.0005        0.0002
                    38         0.0026        0.0018        0.0008
                    39         0.0021        0.0011        0.0004
                    40         0.0054        0.0018        0.0011

                                            Eigenvectors

Signal            Serial     [CEE.sub.4]    [CEE.sub.5]   [CEE.sub.6]
                  number

Pipeline            1           0.2489        0.0217        0.2653
explosion           2           0.0062        0.0125        0.0290
vibration           3           0.0961        0.4683        0.3196
                    4           0.5303        0.4825        0.3942
                    5           0.4106        0.5162        0.5019
                    6           0.4069        0.5110        0.4941
                    7           0.0700        0.2878        0.3746
                    8           0.0733        0.4083        0.4934
                    9           0.0705        0.4006        0.5306
                    10          0.3350        0.3698        0.4949

Underground         11          0.3942        0.0873        0.0439
tunnel blast        12          0.5194        0.3942        0.1373
vibration           13          0.5265        0.5042        0.0853
                    14          0.4871        0.2307        0.1177
                    15          0.5300        0.3747        0.1071
                    16          0.5222        0.2383        0.0962
                    17          0.3365        0.3651        0.2369
                    18          0.4970        0.4597        0.2048
                    19          0.5287        0.2982        0.1074
                    20          0.3757        0.3952        0.1826

Demolition          21          0.0004        0.0016        0.1478
collapse            22          0.0024        0.1070        0.4057
vibration           23          0.0003        0.4343        0.3062
                    24          0.0001        0.0014        0.4967
                    25          0.0058        0.3794        0.4942
                    26          0.0001        0.0002        0.3554
                    27          0.0001        0.4003        0.3324
                    28          0.0204        0.0582        0.0786
                    29          0.0001        0.0069        0.5259
                    30          0.0003        0.1572        0.5283

Surface rock        31          0.1390        0.4707        0.5269
blast vibration     32          0.5307        0.5108        0.5145
                    33          0.4339        0.5137        0.5259
                    34          0.1499        0.5250        0.5054
                    35          0.1870        0.5188        0.5298
                    36          0.1743        0.2947        0.3846
                    37          0.4827        0.5277        0.5270
                    38          0.0878        0.5184        0.5011
                    39          0.1693        0.4668        0.5307
                    40          0.0007        0.5202        0.5290

                                           Eigenvectors

Signal            Serial     [CEE.sub.7]   [CEE.sub.8]   [CEE.sub.9]
                  number

Pipeline            1          0.4851        0.5305        0.4968
explosion           2          0.0262        0.5166        0.4858
vibration           3          0.5111        0.4434        0.0963
                    4          0.3509        0.2898        0.0603
                    5          0.3201        0.1050        0.0597
                    6          0.3428        0.0698        0.1088
                    7          0.4724        0.5305        0.4554
                    8          0.4662        0.5040        0.3915
                    9          0.4053        0.4942        0.2492
                    10         0.4725        0.0911        0.0287

Underground         11         0.0170        0.0097        0.0076
tunnel blast        12         0.0281        0.0133        0.0115
vibration           13         0.0337        0.0118        0.0143
                    14         0.1023        0.0685        0.0239
                    15         0.0561        0.0226        0.0148
                    16         0.0981        0.0332        0.0099
                    17         0.1103        0.0315        0.0452
                    18         0.0867        0.0247        0.0322
                    19         0.0377        0.0463        0.0178
                    20         0.0860        0.0401        0.0257

Demolition          21         0.4680        0.4896        0.4417
collapse            22         0.2847        0.2110        0.0726
vibration           23         0.4134        0.1687        0.3587
                    24         0.2037        0.5247        0.3866
                    25         0.4950        0.3346        0.0972
                    26         0.4591        0.4919        0.4404
                    27         0.3792        0.2881        0.1832
                    28         0.4714        0.5305        0.2545
                    29         0.5142        0.2997        0.1634
                    30         0.5307        0.4387        0.3204

Surface rock        31         0.2577        0.1172        0.0453
blast vibration     32         0.2442        0.0731        0.0359
                    33         0.1329        0.0407        0.0230
                    34         0.4730        0.1849        0.1713
                    35         0.3780        0.1559        0.0485
                    36         0.2294        0.0591        0.0183
                    37         0.1483        0.0348        0.0167
                    38         0.5090        0.0774        0.0280
                    39         0.1530        0.0472        0.0094
                    40         0.4917        0.2858        0.2168

Table 6: Prediction result statistics.

Signal type        Pipeline       Underground     Demolition
                   explosion     tunnel blast      collapse
                 vibration (%)   vibration (%)   vibration (%)

Prediction            50              100            88.9
accuracy

Signal type       Surface rock
                 blast vibration
                       (%)

Prediction            92.3
accuracy

Table 7: Algorithm effect comparison.

                      SVM algorithm              BP neural network
                                                    algorithm

               Prediction      Training      Prediction      Training
                accuracy       time (s)       accuracy       time (s)

Example 1          80            127             70            478
Example 2         131             90            502             70
Example 3          90            152             60            615

Table 8: Algorithm effect comparison.

               EEMD energy entropy        Approximate entropy

              Prediction   Training     Prediction   Training
               accuracy    time (s)      accuracy    time (s)

Example 1         80         135            80         142
Example 2         90         124            80         136
Example 3         90         156            70         161
Example 4        100         119            80         121

                Permutation entropy

              Prediction   Training
               accuracy    time (s)

Example 1         90         129
Example 2         90         131
Example 3         90         167
Example 4         80         118
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Title Annotation:Research Article
Author:Ma, Huayuan; Li, Xinghua; Liu, Qiang; Xingbo, Xie; Ji, Chong; Zhao, Changxiao
Publication:Shock and Vibration
Geographic Code:9CHIN
Date:Aug 31, 2020
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