基于改进CEEMDAN-DCNN的声发射源识别分类方法

doi:10.11872/j.issn.1005-2518.2022.02.162

Abstract

Abstract:

Accurate classification and identification of acoustic emission sources is an important basis for the study of acoustic emission ground pressure monitoring， forecasting and early warning.Aiming at the clas-sification and identification of acoustic emission event signals and mining operation noise signals of surrounding rock masses in underground mines， an intelligent recognition and classification method based on improved complete ensemble empirical mode decomposition and deep convolutional neural network（DCNN）was proposed.Firstly，the signal was decomposed by CEEMDAN， the decomposed IMF components were screened， and the components greater than the permutation entropy threshold or less than the correlation coefficient threshold were removed， and the residual IMF components were reconstructed to obtain the denoised waveform.Then， the DCNN method was used to automatically extract high-dimensional features from the denoised waveform.Finally， the features were used for classification and recognition of softmax classifier to realize intelligent multi-classification of underground signal sources.The results of this research show that：（1）Aiming at the difficulty of multi-classification of waveforms received by acoustic emission monitoring equipment，a waveform classification and recognition method based on improved CEEMDAN-DCNN is proposed.Combined with the advantages of improved CEEMDAN’s advantages of adaptive analysis，pro-cessing of nonlinear and non-stationary signals and the ability of DCNN to automatically extract high-dimensional features， the intelligent multi-classification of underground signal sources is realized.（2）In order to verify the advantages of the improved CEEMDAN algorithm， the simulation signal is constructed to simulate the acoustic emission signal of surrounding rock mass containing noise signal， and the background noise component and pseudo component are eliminated by a joint threshold.The results show that the improved CEEMDAN algorithm can eliminate noise signals and some false components， and retain the essential characteristics of the signal.（3）Through the test， the accuracy of waveform classification based on the improved CEEMDAN-DCNN method in this paper reaches 97.12%. Compared with the traditional SVM， ANN， and CNN methods， the accuracy of waveform classification is higher and the stability is better. The accuracy of DCNN classification and recognition is improved dueing to the signal preprocessed by improved CEEMDAN.（4）The waveform recognition and classification method in this paper can accurately identify the acoustic emission events of surrounding rock masses and non-surrounding rock masses， provide reliable basic research data for ground pressure monitoring and early warning models， and increase the accuracy of ground pressure monitoring and safety early warning and forecasting.

Key words: acoustic emission monitoring, waveform classification, signal classification and recognition, improved CEEMDAN, deep convolutional neural network（DCNN）, permutation entropy（PE）

CLC Number:

TD76

Xuebin XIE, Tao LIU, Huan ZHANG. Identification and Classification Method of Underground AE Source Based on Improved CEEMDAN-DCNN[J].Gold Science and Technology, 2022, 30(2): 209-221.

Figures/Tables 16

Fig.1

Table 1

Comparison of parameters and performance of CEEMD，CEEMDAN and improved CEEMDAN method"

方法	噪声个数	噪声幅值 $a i$	噪声标准差	迭代次数	嵌入维数	时间延迟	计算耗时/s	正交性指标
CEEMD	35×2	0.2	-	-	-	-	23.0798	0.0879
CEEMDAN	70	0.2	0.1	200	-	-	8.5336	0.0318
改进CEEMDAN	70	0.2	0.1	200	6	1	4.0067	3.8396e-4

Table 1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Table 2

Improved CEEMDAN decomposition parameters"

方法

噪声

个数

噪声

幅值 $a i$

噪声

标准差

迭代

次数

嵌入

维数

时间

延迟

改进CEEMDAN

0.2

0.1

200

Table 2

Fig.8

Table 3

Fig.9

Fig.10

Table 4

Table 5

Comparison the performance of proposed method with SVM，ANN and CNN"

评估指标	本文方法	DCNN	SVM	ANN	CNN
准确率/%	97.12（ $σ$ = $0.89 %$ ）	93.44（ $σ$ = $1.02 %$ ）	84.11（ $σ$ = $1.99 %$ ）	69.77（ $σ$ = $3.22 %$ ）	89.23（ $σ$ = $1.87 %$ ）
精确率/%	97.25（ $σ$ = $0.71 %$ ）	93.35（ $σ$ = $1.16 %$ ）	84.09（ $σ$ = $1.96 %$ ）	68.26（ $σ$ = $3.19 %$ ）	89.13（ $σ$ = $2.03 %$ ）
召回率/%	98.09（ $σ$ = $0.83 %$ ）	91.43（ $σ$ = $1.19 %$ ）	83.52（ $σ$ = $1.98 %$ ）	68.83（ $σ$ = $3.18 %$ ）	90.69（ $σ$ = $1.91 %$ ）
计算时间/s	5.67（ $σ$ = $0.52 %$ ）	7.72（ $σ$ = $0.52 %$ ）	20.19（ $σ$ = $0.52 %$ ）	22.10（ $σ$ = $0.52 %$ ）	10.65（ $σ$ = $0.52 %$ ）

Table 5

Fig.11

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分量	排列熵值	相关性系数
IMF1	0.91	0.14
IMF2	0.74	0.92
IMF3	0.68	0.7
IMF4	0.67	0.61
IMF5	0.58	0.58
IMF6	0.50	0.38
IMF7	0.42	0.30
IMF8	0.21	0.15

网络层	输出	卷积核尺寸/步长	padding	激活函数
输入层	1×1 024	-	-	-
卷积层C1	1×64	1×3/1	same	ReLU
DropoutD1	1×64	比率：0.2
卷积层C2	1×256	1×3/1	same	ReLU
DropoutD2	1×256	比率：0.2
卷积层C3	1×32	1×3/1	same	ReLU
DropoutD3	1×32	比率：0.2
卷积层C4	1×32	1×3/1	same	ReLU
DropoutD4	1×32	比率：0.2
卷积层C5	1×32	1×3/1	same	ReLU
DropoutD5	1×32	比率：0.2
卷积层C6	1×32	1×3/1	same	ReLU
DropoutD6	1×32	比率：0.2
全连接层F1	1×512	-	-	ReLU
dropout	1×512	比率：0.5
全连接层F2	1×4	-	-	-

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Identification and Classification Method of Underground AE Source Based on Improved CEEMDAN-DCNN

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