Cutting Pattern Identification for Coal Mining Shearer through a Swarm Intelligence-Based Variable Translation Wavelet Neural Network.
Cutting Pattern Identification for Coal Mining Shearer through a Swarm Intelligence-Based Variable Translation Wavelet Neural Network.
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DOI：
10.3390/s18020382
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发表时间：
2018-01-29
期刊：
影响因子：
--
通讯作者：
Liu X
中科院分区：
文献类型：
--
作者：
Xu J;Wang Z;Tan C;Si L;Liu X
由于声信号具有非接触测量、结构紧凑、功耗低等优点，在许多领域受到了广泛的关注。通过对采煤机声音信号的分析，实现采煤机截割方式的准确在线识别，保证工作面的安全质量。原始声信号首先通过工业麦克风采集，并通过自适应集成经验模式分解（EEMD）进行分解。在下一步中，提取由每个级别的归一化能量组成的13维集合作为特征向量。然后，受蝙蝠觅食行为的启发，提出了一种群智能优化算法来确定传统的可变平移小波神经网络（VTWNN）的关键参数。此外，在基本蝙蝠算法（BA）中引入扰动系数，克服了BA算法易陷入局部极值和搜索能力有限的缺点。用改进的BA优化的VTWNN（VTWNN-MBA）作为切削模式识别器。最后通过仿真算例进行了一系列比较，准确率达到95.25%，证明了该方法的有效性和优越性。
As a sound signal has the advantages of non-contacted measurement, compact structure, and low power consumption, it has resulted in much attention in many fields. In this paper, the sound signal of the coal mining shearer is analyzed to realize the accurate online cutting pattern identification and guarantee the safety quality of the working face. The original acoustic signal is first collected through an industrial microphone and decomposed by adaptive ensemble empirical mode decomposition (EEMD). A 13-dimensional set composed by the normalized energy of each level is extracted as the feature vector in the next step. Then, a swarm intelligence optimization algorithm inspired by bat foraging behavior is applied to determine key parameters of the traditional variable translation wavelet neural network (VTWNN). Moreover, a disturbance coefficient is introduced into the basic bat algorithm (BA) to overcome the disadvantage of easily falling into local extremum and limited exploration ability. The VTWNN optimized by the modified BA (VTWNN-MBA) is used as the cutting pattern recognizer. Finally, a simulation example, with an accuracy of 95.25%, and a series of comparisons are conducted to prove the effectiveness and superiority of the proposed method.
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