Wear State Prediction of Crusher Liner Based on LMS of   Improved Dustpan Tongue Function and BAS-LSSVM

CAI Gaipin; ZHAO Xin; LI Bobo; YU Hui

doi:10.13433/j.cnki.1003-8728.20220049

Volume 42 Issue 6

Jun. 2023

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Article Navigation > Mechanical Science and Technology for Aerospace Engineering > 2023 > 42(6): 923-933

CAI Gaipin, ZHAO Xin, LI Bobo, YU Hui. Wear State Prediction of Crusher Liner Based on LMS of Improved Dustpan Tongue Function and BAS-LSSVM[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(6): 923-933. doi: 10.13433/j.cnki.1003-8728.20220049

Citation:

CAI Gaipin, ZHAO Xin, LI Bobo, YU Hui. Wear State Prediction of Crusher Liner Based on LMS of Improved Dustpan Tongue Function and BAS-LSSVM[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(6): 923-933. doi: 10.13433/j.cnki.1003-8728.20220049

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Wear State Prediction of Crusher Liner Based on LMS of Improved Dustpan Tongue Function and BAS-LSSVM

doi: 10.13433/j.cnki.1003-8728.20220049

CAI Gaipin^{1, 2
,},
ZHAO Xin¹,
LI Bobo¹,
YU Hui¹

1.
School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, Jiangxi, China
2.
Jiangxi Province Engineering Research Center for Mechanical and Electrical of Mining and Metallurgy, Ganzhou 341000, Jiangxi, China

Received Date: 2021-06-25
Publish Date: 2023-06-25

Abstract

Abstract

In order to solve the problem that the wear of crusher liner is difficult to predict, a new method for predicting the wear state of crusher liner based on LMS and BAS-LSSVM with improved half-pan tongue function is proposed. Firstly, based on the least mean square error (LMS) algorithm, an improved dustpan tongue function LMS was introduced to calculate the acoustic time (TOF) of the ultrasonic echo signal of the lining board. Secondly, the thickness of liner was calculated by TOF, and the wear amount was obtained according to the thickness change of liner before and after wear. Finally, a BAS-LSSVM liner wear prediction model was established by optimizing the penalty factor of least squares support vector machine (LSSVM) and the standardized parameters in its kernel function by using the Beetle Antennae Search algorithm (BAS). The wear amount was taken as the input of the prediction model, and the lining wear stage was taken as the output. The experimental results show that the identification accuracy of moving cone liner and fixed cone liner can reach 94.44% and 95.56% respectively, which can effectively predict the wear state of the crusher liner.
- wear state prediction,
- LMS,
- TOF calculation,
- LSSVM

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References(15)

References

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