Investigation on the use of artificial neural networks to overcome the effects of environmental and operational changes on guided waves monitoring

11th International Conference on Damage Assessment of Structures

物理学;材料科学

Mountassir, M. El^1 ; Yaacoubi, S.^1 ; Dahmene, F.^1

Institut de Soudure, Plateforme RDI CND, 4 Bvd Henri Becquerel, Yutz

57970, France^1

关键词: Advanced signal processing;    Dispersive components;    False indication;    Number of hidden neurons;    Operational changes;    Operational conditions;    Structural health monitoring (SHM);    Ultrasonic guided wave;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/628/1/012127/pdf
DOI  :  10.1088/1742-6596/628/1/012127

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Intelligent feature extraction and advanced signal processing techniques are necessary for a better interpretation of ultrasonic guided waves signals either in structural health monitoring (SHM) or in nondestructive testing (NDT). Such signals are characterized by at least multi-modal and dispersive components. In addition, in SHM, these signals are closely vulnerable to environmental and operational conditions (EOCs), and can be severely affected. In this paper we investigate the use of Artificial Neural Network (ANN) to overcome these effects and to provide a reliable damage detection method with a minimal of false indications. An experimental case of study (full scale pipe) is presented. Damages sizes have been increased and their shapes modified in different steps. Various parameters such as the number of inputs and the number of hidden neurons were studied to find the optimal configuration of the neural network.