Td., Shenzhen 518031, China Correspondence: [email protected]; Tel.: 86-411-
Td., Shenzhen 518031, China Correspondence: [email protected]; Tel.: 86-411-847-Citation: Zhang, Q.; Xu, D.; Hou, J.; Jankowski, L.; Wang, H. Damage Identification Strategy Making use of Additional Virtual Mass Primarily based on Harm Sparsity. Appl. Sci. 2021, 11, 10152. https://doi.org/10.3390/ app112110152 Academic Editor: Mohammad Noori Received: 21 September 2021 Accepted: 27 October 2021 Published: 29 OctoberAbstract: Harm identification solutions primarily based on structural modal parameters are influenced by the structure kind, RP101988 Epigenetic Reader Domain variety of measuring sensors and noise, resulting in insufficient modal data and low harm identification accuracy. The added virtual mass method introduced within this study is primarily based around the virtual deformation technique for deriving the frequency-domain response equation with the virtual structure and determine its mode to expand the modal details of the original structure. Primarily based on the initial condition assumption that the structural harm was sparse, the damage identification technique primarily based on sparsity with l1 and l2 norm of your damage-factor variation along with the orthogonal matching pursuit (OMP) system based on the l0 norm had been introduced. Based on the traits from the added virtual mass process, an improved OMP process (IOMP) was created to enhance the localization of optimal solution determined using the OMP strategy and also the damage substructure choice method, analyze the harm in the entire structure globally, and boost harm identification accuracy. The accuracy and robustness of each and every damage identification system for multi-damage scenario had been analyzed and verified via simulation and experiment. Key phrases: structural overall health monitoring (SHM); harm identification; virtual mass; sparse constraint; IOMP method1. Icosabutate web Introduction With all the rapid development of modern science and technology, there has been an growing number of significant and complex engineering structures [1,2]. When these structures turn into damaged, the consequences are catastrophic, major to a significant loss of human lives and property [3,4]. Consequently, it is essential to adopt effective health-monitoring procedures for such structures [5], and damage identification is actually a crucial aspect of structural wellness monitoring (SHM) [6,7]. Dependable and efficient damage identification approaches are particularly needed to achieve the security and integrity of structures [8]. Probably the most broadly applied vibration theory in structural harm identification diagnoses damages by measuring the dynamic response and modal parameters of structures [9,10]. As the standard characteristics of structures, modal parameters usually do not change with all the excitation form [11]; hence, the damage identification approach based on modal parameters is trusted [12,13]. Rao et al. [14] analyzed the experimental and analytical modes of a cantilever beam utilizing an artificial neural network primarily based around the vibration theory to recognize structural damages. Ali et al. [15]. assessed structural damage by comparing the dynamic response parameters of the finite element model in damaged and undamaged states primarily based around the experimental organic frequency and vibration mode from the structure and verified the model using the cantilever beam model. Wu et al. [16]. identified the crack place and extension depth ofPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.Copyright: 2021 by the authors. Licensee MDPI, Basel, Switzerland. This short article is an.