Intelligent recognition of cutting load of coal mining equipment based on vibration wavelet packet feature

Author(s):  
Yang Jian-jian ◽  
Wang Zi-rui ◽  
Tang Zhi-wei ◽  
Wang Feng-dong ◽  
Zhang Zhi-hua ◽  
...  
2021 ◽  
Vol 25 (1) ◽  
pp. 115-122
Author(s):  
Shuilin Wang ◽  
SongYong Liu ◽  
Fanping Meng

The traditional research method of fault diagnosis mechanism has poor stability, which leads to the difference of fault diagnosis and location results. Therefore, under the complex geological environment, a new research method of fault diagnosis mechanism of gear and bearing for coal mining equipment is proposed. This method calculates gears and bearings’ yield strength by analyzing coal mining equipment’s bearing capacity elasticity. According to the fitting degree, the equipment sample’s projection space is confirmed, the fault features of gear and bearing are extracted by segmentation algorithm, the optimal fitness is set by positioning algorithm, the location of fault center is obtained, and the fault mechanism diagnosis is studied. Experimental results show that compared with the traditional method, the proposed method is more stable, and the difference in fault diagnosis results is minimal. It can be seen that this method is more suitable for fault diagnosis of coal mining equipment.


2020 ◽  
Vol 29 ◽  
pp. 2633366X2097468
Author(s):  
Qiufeng Li ◽  
Tiantian Qi ◽  
Lihua Shi ◽  
Yao Chen ◽  
Lixia Huang ◽  
...  

Glass fiber-reinforced plastics (GFRP) is widely used in many industrial fields. When acoustic emission (AE) technology is applied for dynamic monitoring, the interfering signals often affect the damage evaluation results, which significantly influences industrial production safety. In this work, an effective intelligent recognition method for AE signals from the GFRP damage is proposed. Firstly, the wavelet packet analysis method is used to study the characteristic difference in frequency domain between the interfering and AE signals, which can be characterized by feature vector. Then, the model of back-propagation neural network (BPNN) is constructed. The number of nodes in the input layer is determined according to the feature vector, and the feature vectors from different types of signals are input into the BPNN for training. Finally, the wavelet packet feature vectors of the signals collected from the experiment are input into the trained BPNN for intelligent recognition. The accuracy rate of the proposed method reaches to 97.5%, which implies that the proposed method can be used for dynamic and accurate monitoring of GFRP structures.


Robotica ◽  
2001 ◽  
Vol 19 (5) ◽  
pp. 513-526 ◽  
Author(s):  
J. C. Ralston ◽  
D. W. Hainsworth ◽  
D. C. Reid ◽  
D. L. Anderson ◽  
R. J. McPhee

This paper presents some recent applications of sensing, guidance and telerobotic technology in the coal mining industry. Of special interest is the development of semi or fully autonomous systems to provide remote guidance and communications for coal mining equipment. We consider the use of radar and inertial based sensors in an attempt to solve the horizontal and lateral guidance problems associated with mining equipment automation. We also describe a novel teleoperated robot vehicle with unique communications capabilities, called the Numbat, which is used in underground mine safety and reconnaissance missions.


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