Vibration signal-based defect detection method for railway signal relay using parameter-optimized VMD and ensemble feature selection

Zhang, Minhao; Cao, Yuan; Sun, Yongkui; Su, Shuai

doi:10.1016/j.conengprac.2023.105630

Cited by 9 publications

(1 citation statement)

References 40 publications

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“…In 2014, Dragomiretskiy proposed VMD [6], a method with both a strong theoretical basis and notable robustness in extracting fault features in complex environments, such as strong noise. This algorithm has found application across diverse fields and has shown excellent performance [7][8][9]. However, the VMD algorithm itself has limitations, and the effectiveness of its decomposition may be affected by both the quantity of intrinsic mode functions (IMFs), denoted as K, and the parameter α for the penalty factor.…”

Section: Introductionmentioning

confidence: 99%

An Improved Sparrow Search Algorithm for the Optimization of Variational Modal Decomposition Parameters

Du,

Wang,

Qian

et al. 2024

Applied Sciences

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Variational modal decomposition (VMD) is frequently employed for both signal decomposition and extracting features; however, the decomposition outcome is influenced by the quantity of intrinsic modal functions (IMFs) and the specific parameter values of penalty factors. To tackle this issue, we propose an algorithm based on the Halton sequence and the Laplace crossover operator for the sparrow search algorithm–VMD (HLSSA-VMD) to fine-tune the parameters of VMD. First, the population initialization by the Halton sequence yields higher-quality initial solutions, which effectively addresses the issue of the algorithm’s sluggish convergence due to overlapping and the lack of diversity of the initial solutions. Second, the introduction of the Laplace crossover operator (LX) to perturb the position of the best individual in each iteration helps to prevent the algorithm from becoming ensnared in a local optimum and improves the convergence speed of the algorithm. Finally, from the simulation of 17 benchmark test functions, we found that the HLSSA exhibited superior convergence accuracy and accelerated convergence pace, as well as better robustness than the particle swarm optimization (PSO) algorithm, the whale optimization algorithm (WOA), the multiverse optimization (MVO) algorithm, and the traditional sparrow search algorithm (SSA). In addition, we verified the effectiveness of the HLSSA-VMD algorithm on two simulated signals and compared it with PSO-VMD, WOA-VMD, MVO-VMD, and SSA-VMD. The experimental findings indicate that the HLSSA-VMD obtains better parameters, confirming the superiority of the algorithm.

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