Error analysis and correction of multi-sensor cluster methods for acoustic emission source localization

Xue, Chuanrong; Xu, Gang; Wang, Xinke; Gao, Jiacheng; Gao, Dejun

doi:10.1016/j.ultras.2021.106438

Cited by 10 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This technique helps to localize the acoustic source with only a few sensors, which is heuristic for the scenario that limited sensors can be used for the monitoring of structures. Xue et al focused on the analysis of the positioning error of multi-sensor cluster methods with sensor arrangements of isosceles right-angled triangle and triangular pyramid and used the correction algorithm to improve the locating results [10]. The above studies provide extremely valuable value for largescale damage research, but, for large structures and multiple monitoring areas, it is still difficult to identify, orientate, and characterize structural damage accurately and timely.…”

Section: Introductionmentioning

confidence: 99%

A sparse, triangle-shaped sensor array for damage orientation and characterization of composite structures

Qiu

Liao

et al. 2023

Smart Mater. Struct.

View full text Add to dashboard Cite

Since numerous sensors are needed to create a sensor array for the structural health monitoring of large-scale structures, the equipment quantity and cost considerably increase. This study proposes a sparse, triangle-shaped sensor array to identify, orient, and assess the degree of structural damage in composite constructions in order to overcome this shortcoming. The damage-scattered Lamb waves are recorded by the sparse sensor array with a variety of features that are then extracted and fed into the Support Vector Machine (SVM) classification method. The location and severity of the damage in composite constructions can be determined by training the SVM model. The principle component analysis (PCA) technique is used to compress the wave feature vectors while maintaining the majority of the damage information because the high dimension of the wave feature vectors required a significant amount of calculation during the training phase. Proof-of-concept tests show that the trained model, by utilizing the many properties of Lamb wave signals, can orient and define the degree of damage with excellent accuracy. Multiple lamb wave properties can be used to make up for the triangle sensor array's loss of damage information. In conjunction with the SVM, the triangle-shaped sensor array that was proposed in this study can efficiently make it easier to identify and characterize damage to large-scale structures while using fewer sensors.

show abstract

Section: Introductionmentioning

confidence: 99%