An improved near-field weighted subspace fitting algorithm based on niche particle swarm optimization for ultrasonic guided wave multi-damage localization

• Published in: Mechanical systems and signal processing 2024-06, Vol.215, p.111403, Article 111403
• Main Authors: Fang, Xin, Liu, Guijie, Wang, Honghui, Mu, Weilei, Xie, Yingchun, Tian, Xiaojie, Li, Gongbo, Li, Guanghao
• Format: Article
• Language: English
• Subjects: Multi-damage location; Near-field subspace fitting; Niche particle swarm optimization; Ultrasonic guided wave
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2024.111403

•A near-field weighted subspace fitting algorithm is proposed for ultrasonic guided wave multi-damage localization.•A near-field multi-dimensional solution search algorithm based on a niche particle swarm optimization is proposed, which effectively improves the efficiency of damage localization.•Algorithm performance comparison, finite element simulation, real point damage and slot damage experiments verify the effectiveness of the proposed method. The ultrasonic guided wave-based method for multi-damage localization has been widely proposed. However, the precision of this method is directly correlated with both the quantity of sensors employed and the intricacy of the implementation process. This relationship poses a challenge in striking a balance between the accuracy and efficiency. To improve the computational efficiency under the premise of ensuring the accuracy of multi-damage localization, this paper proposes a near-field weighted subspace fitting algorithm based on niche-particle swarm optimization. Firstly, the fitting relationship between the signal subspace of the diffraction wave and the array steering vector is established under the uniform linear array. Secondly, a multi-dimensional solution space search algorithm based on niche-particle swarm optimization is proposed to improve the search efficiency of damage. Finally, the algorithm is verified by performance comparison, finite element simulation and experiment. The results show that compared with the same type of method, the algorithm improves the computational efficiency by nearly threefold under the identifiable multi-damage conditions. Additionally, the angle error is 1 ∼ 6°, and the distance error is 1 ∼ 20 mm.