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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Identification of Shearer Cutting Patterns Using Vibration Signals Based on a Least Squares Support Vector Machine with an Improved Fruit Fly Optimization Algorithm
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Identification of Shearer Cutting Patterns Using Vibration Signals Based on a Least Squares Support Vector Machine with an Improved Fruit Fly Optimization Algorithm

机译：基于最小二乘支持向量机和改进果蝇优化算法的振动信号识别采煤机切割模式

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摘要

Shearers play an important role in fully mechanized coal mining face and accurately identifying their cutting pattern is very helpful for improving the automation level of shearers and ensuring the safety of coal mining. The least squares support vector machine (LSSVM) has been proven to offer strong potential in prediction and classification issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. In this paper, an improved fly optimization algorithm (IFOA) to optimize the parameters of LSSVM was presented and the LSSVM coupled with IFOA (IFOA-LSSVM) was used to identify the shearer cutting pattern. The vibration acceleration signals of five cutting patterns were collected and the special state features were extracted based on the ensemble empirical mode decomposition (EEMD) and the kernel function. Some examples on the IFOA-LSSVM model were further presented and the results were compared with LSSVM, PSO-LSSVM, GA-LSSVM and FOA-LSSVM models in detail. The comparison results indicate that the proposed approach was feasible, efficient and outperformed the others. Finally, an industrial application example at the coal mining face was demonstrated to specify the effect of the proposed system.
机译：采煤机在综采工作面中起着重要的作用，准确识别其采煤方式对提高采煤机的自动化水平，确保采煤安全非常有帮助。最小二乘支持向量机（LSSVM）已被证明在预测和分类问题上具有强大的潜力，特别是通过采用适当的元启发式算法来确定其两个参数的值。然而，这些元启发式算法的缺点是难以理解并且缓慢地达到全局最优解。本文提出了一种用于优化LSSVM参数的改进的飞行优化算法（IFOA），并结合IFOA（IFOA-LSSVM）将LSSVM用于识别采煤机的切割模式。基于整体经验模态分解（EEMD）和核函数，收集了五个切削模式的振动加速度信号，并提取了特殊的状态特征。进一步介绍了有关IFOA-LSSVM模型的一些例子，并将结果与​​LSSVM，PSO-LSSVM，GA-LSSVM和FOA-LSSVM模型进行了比较。比较结果表明，该方法可行，高效，性能优于其他方法。最后，通过一个在煤矿工作面的工业应用实例来说明所提出系统的效果。

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