ABSTRACT

An important issue in analysing signals by the variational mode decomposition (VMD) algorithm is to confirm the number of modes and the balance parameter. In the applications of fault detection, most studies optimise parameters by the characteristics to extract fault information perfectly. Then, the results are used for subsequent operations such as fault analysis and classification. However, the optimal methods aiming at extracting fault information are not completely applicable to detect whether the fault occurs for different signal segments. To address this issue, this paper proposes a parameter optimised VMD method, and it is used to analyse acoustic emission (AE) signals from actual operating railway environment. Firstly, an optimised index is constructed based on a universally ideal decomposition result, which completely decomposes the signal without mode mixing and over-decomposition. Then, the VMD parameters are searched by the particle swarm optimisation (PSO) algorithm using the maximum index as the optimisation fitness function. Meanwhile, the permutation entropy feature of modes obtained by the optimised parameters is extracted to detect rail crack signals. After that, the proposed method is further analysed based on two different AE signals. Finally, the detection results are analysed and demonstrate the effectiveness of the proposed method.

摘要

通过变分模态分解 (VMD) 算法分析信号的一个重要问题是确定模态数量和平衡参数。在故障检测的应用中，大多数研究都是通过特征优化参数来完美地提取故障信息。然后，将结果用于后续操作，例如故障分析和分类。然而，以提取故障信息为目标的最优方法并不完全适用于检测不同信号段是否发生故障。针对这一问题，本文提出了一种参数优化的VMD方法，用于分析铁路实际运行环境中的声发射(AE)信号。首先，基于普遍理想的分解结果构建优化指标，完全分解信号，没有模式混合和过度分解。然后，使用最大指数作为优化适应度函数，通过粒子群优化 (PSO) 算法搜索 VMD 参数。同时，提取优化参数得到的模态排列熵特征来检测钢轨裂纹信号。之后，基于两个不同的AE信号进一步分析所提出的方法。最后，对检测结果进行了分析，证明了所提出方法的有效性。提取优化参数得到的模态排列熵特征，检测钢轨裂纹信号。之后，基于两个不同的AE信号进一步分析所提出的方法。最后，对检测结果进行了分析，证明了所提出方法的有效性。提取优化参数得到的模态排列熵特征，检测钢轨裂纹信号。之后，基于两个不同的AE信号进一步分析所提出的方法。最后，对检测结果进行了分析，证明了所提出方法的有效性。