Wear State Prediction of Crusher Liner Based on LMS of Improved Dustpan Tongue Function and BAS-LSSVM
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摘要: 针对破碎机衬板磨损难以预测的问题，提出一种改进箕舌线函数的LMS和BAS-LSSVM的衬板磨损状态预测方法。首先，在最小均方误差算法（LMS）的基础上，引入改进的箕舌线函数，提出改进箕舌线函数的LMS算法，将其用于衬板超声回波信号的声时（TOF）的计算中；其次，通过TOF计算出衬板厚度，并根据衬板磨损前后的厚度变化得出磨损量；最后利用天牛须算法（BAS）优化最小二乘支持向量机（LSSVM）的惩罚因子
$\gamma $ 和其核函数中的标准化参数$\sigma $ ，将磨损量作为预测模型的输入，衬板磨损阶段作为输出，建立BAS-LSSVM衬板磨损预测模型。结果表明，该方法对动锥衬板和定锥衬板的识别准确率分别达到了94.44%和95.56%，能够有效预测出衬板的磨损状态。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.-
Key words:
- wear state prediction /
- LMS /
- TOF calculation /
- LSSVM
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表 1 人工测量衬板各测点厚度
衬板 测点编号 厚度/mm 定锥衬板 A1 45.48 A2 46.07 A3 48.86 A4 48.91 动锥衬板 B1 11.09 B2 11.89 B3 13.74 B4 14.89 
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表 2 4种方法计算衬板各测点测厚的相对误差
测点 平均相对误差/% 互相关法 传统LMS
算法基于箕舌线的
LMS算法本文提出
方法A1 0.336 0.307 0.261 0.222 A2 0.268 0.256 0.246 0.242 A3 0.446 0.406 0.365 0.316 A4 0.376 0.357 0.346 0.338 B1 0.302 0.298 0.295 0.291 B2 0.296 0.287 0.278 0.272 B3 0.526 0.505 0.489 0.467 B4 0.365 0.358 0.343 0.324
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表 3 衬板各测点磨损量及相对误差
测点 人工检测
磨损量/mm超声检测
磨损量/mm相对误差/
%A1 0.93 0.88 5.38 A2 1.14 1.11 2.63 A3 1.32 1.40 5.71 A4 2.07 1.93 6.28 B1 0.86 0.81 5.81 B2 0.97 1.02 4.90 B3 1.13 1.15 2.61 B4 1.55 1.51 2.58
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表 4 3种优化算法的初始参数设置
算法名称 参数名称 对应值 GA 最大迭代次数 200 种群数量 20 交叉概率 0.9 变异概率 0.1 WOA 鲸鱼数量 20 最大迭代次数 200 BAS 步长因子c1、c2 3、10 最大迭代次数 200
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表 5 3种测试函数运行10次的平均最优解
测试函数 Sphere Griewank Rosenbrock GA 1.0426 0.2758 0.2244 WOA 0.6193 0.0623 0.0446 BAS 0.1258 0.0014 0.0715
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表 6 3种预测模型预测准确率
% 算法 衬板 磨合磨损阶段 稳定磨损阶段 破坏磨损阶段 平均预测准确率 GA-LSSVM 定锥 90 80.0 86.67 85.56 动锥 90 86.67 86.67 87.78 WOA-LSSVM 定锥 86.67 90 90 88.89 动锥 90 86.67 93.33 90 BAS-LSSVM 定锥 93.33 93.33 96.67 94.44 动锥 93.33 96.67 96.67 95.56
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