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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Elimination of End effects in LMD Based on LSTM Network and Applications for Rolling Bearing Fault Feature Extraction
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Elimination of End effects in LMD Based on LSTM Network and Applications for Rolling Bearing Fault Feature Extraction

机译：基于LSTM网络的LMD在LMD中消除终止效应及滚动轴承故障特征提取

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Local mean decomposition (LMD) is widely used in the area of multicomponents signal processing and fault diagnosis. One of the major problems is end effects, which distort the decomposed waveform at each end of the analyzed signal and influence feature frequency. In order to solve this problem, this paper proposes a novel self-adaptive waveform point extended method based on long short-term memory (LSTM) network. First, based on existing signal points, the LSTM network parameters of right and left ends are trained; then, these parameters are used to extend the waveform point at each end-side of signal; furthermore, the corresponding parameters are adaptively updated. The proposed method is compared with the characteristic segment extension and the traditional neural network extension methods through a simulated signal to verify the effectiveness. By combing the proposed method with LMD, an improved LMD algorithm is obtained. Finally, application of rolling bearing fault signal is carried out by the improved LMD algorithm, and the results show that the feature frequencies of the rolling bearing’s ball and inner and outer rings are successfully extracted.
机译：局部均值分解（LMD）广泛用于多组分信号处理和故障诊断领域。其中一个主要问题是结束效果，其在分析的信号的每一端处扭曲分解波形并影响特征频率。为了解决这个问题，本文提出了一种基于长短期存储器（LSTM）网络的新型自适应波形点扩展方法。首先，基于现有信号点，训练右端和左端的LSTM网络参数;然后，这些参数用于在信号的每个端侧扩展波形点;此外，相应的参数被自适应地更新。将所提出的方法与特征段扩展和传统的神经网络扩展方法进行比较，通过模拟信号来验证有效性。通过用LMD梳理所提出的方法，获得了一种改进的LMD算法。最后，通过改进的LMD算法进行滚动轴承故障信号的应用，结果表明，成功提取了滚动轴承球和内环的特征频率。

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