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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.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 61771161 and No. 61601139) and the National Key R&D Program of China (No. 2019YFC0117400).
Disclosure statement
No potential conflict of interest was reported by the author(s).