Sound Recognition Method of Coal Mine Gas and Coal Dust Explosion Based on GoogLeNet

Yu, Xingchen; Li, Xiaowei

doi:10.3390/e25030412

Cited by 6 publications

(9 citation statements)

References 9 publications

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“…To verify the advantages of the coal mine gas and coal dust explosion sound recognition method proposed in this paper, the recognition results of the algorithm in this paper are compared with the coal mine gas and coal dust explosion sound recognition results proposed in the literature [ 5 , 6 , 7 ], and the specific comparison results are shown in Table 3 . As can be seen from Table 3 , the algorithm proposed in this paper has a recognition rate of 95%, which is 10% and 2% higher than that in [ 5 , 6 ], respectively and is the same as that in [ 7 ]; the recall rate in [ 6 ] is up to 100%, which is 5% higher than that of the algorithm proposed in this paper and 16.7% and 25% higher than that in [ 5 , 7 ], respectively. The algorithm proposed in this paper has an accuracy rate of 95.8%, which is higher than that in [ 5 , 6 ], 24.4% and 14.7% higher than in [ 5 ] and 4.2% lower than in [ 7 ], respectively.…”

Section: Resultsmentioning

confidence: 99%

“…The authors of [ 6 ] propose the decomposition method of DTWCT to realize the decomposition and reconstruction of sound signals, extract the energy entropy ratio of its high-frequency components, which is used to characterize the sound signals, and input them into ELM to construct the sound recognition model. The authors of [ 7 ] propose the wavelet packet decomposition method to realize the decomposition of the sound signal, extract the energy ratio of its decomposition components, which is used to characterize the sound signal, and input it into the BP neural network to construct the sound recognition model.…”

Section: Introductionmentioning

confidence: 99%

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Sound Identification Method for Gas and Coal Dust Explosions Based on MLP

2023

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To solve the problems of backward gas and coal dust explosion alarm technology and single monitoring means in coal mines, and to improve the accuracy of gas and coal dust explosion identification in coal mines, a sound identification method for gas and coal dust explosions based on MLP in coal mines is proposed, and the distributions of the mean value of the short-time energy, zero crossing rate, spectral centroid, spectral spread, roll-off, 16-dimensional time-frequency features, MFCC, GFCC, short-time Fourier coefficients of gas explosion sound, coal dust sound, and other underground sounds were analyzed. In order to select the most suitable feature vector to characterize the sound signal, the best feature extraction model of the Relief algorithm was established, and the cross-entropy distribution of the MLP model trained with the different numbers of feature values was analyzed. In order to further optimize the feature value selection, the recognition results of the recognition models trained with the different numbers of sound feature values were compared, and the first 35-dimensional feature values were finally determined as the feature vector to characterize the sound signal. The feature vectors are input into the MLP to establish the sound recognition model of coal mine gas and coal dust explosion. An analysis of the feature extraction, optimal feature extraction, model training, and time consumption for model recognition during the model establishment process shows that the proposed algorithm has high computational efficiency and meets the requirement of the real-time coal mine safety monitoring and alarm system. From the results of recognition experiments, the sound recognition algorithm can distinguish each kind of sound involved in the experiments more accurately. The average recognition rate, recall rate, and accuracy rate of the model can reach 95%, 95%, and 95.8%, respectively, which is obviously better than the comparison algorithm and can meet the requirements of coal mine gas and coal dust explosion sensing and alarming.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sound Identification Method for Gas and Coal Dust Explosions Based on MLP

2023

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“…Then each output result is merged. In the literature, GoogLeNet confirms high efficiency for image classification[25][26][27][28].…”

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confidence: 85%

Fault diagnosis of electrical faults of three phase induction motors using acoustic analysis

Lucas,

Lui,

Brumercik

et al. 2024

Bulletin of the Polish Academy of Sciences Technical Sciences

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Fault diagnosis techniques of electrical motors can prevent unplanned downtime and loss of money, production, and health. Various parts of the induction motor can be diagnosed: Rotor, stator, rolling bearings, fan, insulation damage, and shaft. Acoustic analysis is non-invasive. Acoustic sensors are low-cost. Changes in the acoustic signal are often observed for faults in induction motors. In this paper, the authors present a fault diagnosis technique for three-phase induction motors (TPIM) using acoustic analysis. The authors analyzed acoustic signals for three conditions of the TPIM: healthy TPIM, TPIM with twobroken bars, and TPIM with a faulty ring of the squirrel cage. Acoustic analysis was performed using Fast Fourier Transform(FFT), a new feature extraction method called MoD-7 (Maxima of differences between the conditions), and deep neural networks: GoogLeNet, and ResNet-50. The results of the analysis of acoustic signals were equal to 100% for the three analyzed conditions. The proposed technique is excellent for acoustic signals. The described technique can be used for electric motor fault diagnosis applications.

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“…Traditional methods of manual observation and monitoring are inadequate to meet the demands of modern coal mining. Therefore, there is a need to rely on advanced image recognition and computer vision technologies to achieve real-time monitoring of the working face’s status [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

CM-YOLOv8: Lightweight YOLO for Coal Mine Fully Mechanized Mining Face

Fan,

Mao,

et al. 2024

Sensors

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With the continuous development of deep learning, the application of object detection based on deep neural networks in the coal mine has been expanding. Simultaneously, as the production applications demand higher recognition accuracy, most research chooses to enlarge the depth and parameters of the network to improve accuracy. However, due to the limited computing resources in the coal mining face, it is challenging to meet the computation demands of a large number of hardware resources. Therefore, this paper proposes a lightweight object detection algorithm designed specifically for the coal mining face, referred to as CM-YOLOv8. The algorithm introduces adaptive predefined anchor boxes tailored to the coal mining face dataset to enhance the detection performance of various targets. Simultaneously, a pruning method based on the L1 norm is designed, significantly compressing the model’s computation and parameter volume without compromising accuracy. The proposed algorithm is validated on the coal mining dataset DsLMF+, achieving a compression rate of 40% on the model volume with less than a 1% drop in accuracy. Comparative analysis with other existing algorithms demonstrates its efficiency and practicality in coal mining scenarios. The experiments confirm that CM-YOLOv8 significantly reduces the model’s computational requirements and volume while maintaining high accuracy.

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Sound Recognition Method of Coal Mine Gas and Coal Dust Explosion Based on GoogLeNet

Cited by 6 publications

References 9 publications

Sound Identification Method for Gas and Coal Dust Explosions Based on MLP

Sound Identification Method for Gas and Coal Dust Explosions Based on MLP

Fault diagnosis of electrical faults of three phase induction motors using acoustic analysis

CM-YOLOv8: Lightweight YOLO for Coal Mine Fully Mechanized Mining Face

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