A Damage Detection Method Using Neural Network Optimized by Multiple Particle Collision Algorithm

Sergio V Farias,Elcio H Shiguemori,Haroldo F Campos Velho,Osamu Saotome,Stelios M Potirakis

doi:10.1155/2021/9998187

Abstract

A critical task of structural health monitoring is damage detection and localization. Lamb wave propagation methods have been successfully applied for damage identification in plate-like structures. However, Lamb wave processing is still a challenging task due to its multimodal and dispersive characteristics. To address this issue, data-driven machine learning approaches as artificial neural network (ANN) have been proposed. However, the effectiveness of ANN can be improved based on its architecture and the learning strategy employed to train it. The present paper proposes a Multiple Particle Collision Algorithm (MPCA) to design an optimum ANN architecture to detect and locate damages in plate-like structures. For the first time in the literature, the MPCA is applied to find damages in plate-like structures. The present work uses one piezoelectric transducer to generate Lamb wave signals on an aluminum plate structure and a linear array of four transducers to capture the scattered signals. The continuous wavelet transform (CWT) processes the captured signals to estimate the time-of-flight (ToF) that is the ANN inputs. The ANN output is the damage spatial coordinates. In addition to MPCA optimization, this paper uses a quantitative entropy-based criterion to find the best mother wavelet and the scale values. The presented experimental results show that MPCA is capable of finding a simple ANN architecture with good generalization performance in the proposed damage localization application. The proposed method is compared with the 1-dimensional convolutional neural network (1D-CNN). A discussion about the advantages and limitations of the proposed method is presented.

Highlights

In safety-critical systems, failure detection and prognostic (FDP) approaches are essential to avoid catastrophic failures
The present paper proposes an optimized artificial neural network (ANN) for damage detection in a plate-like structure using the Multiple Particle Collision Algorithm (MPCA)
The experimental results of damage detection and localization in an aluminum plate validate the effectiveness of MPCA in finding a simple and optimized ANN architecture with good generalization capability

Summary

Introduction

In safety-critical systems, failure detection and prognostic (FDP) approaches are essential to avoid catastrophic failures. The two main tasks of FDP methods are incipient failure detection and remaining useful life estimation through prognostics techniques [1]. Structural health monitoring (SHM) investigates the damage detection and prognostic in structural components of critical systems such as aircraft and bridges. Continuous wavelet transform CWT is a linear transformation that decomposes the input signal xðtÞ over the scaled and translated versions of the mother wavelet ψðtÞ, as shown by the following equation [51]: CWTðs, τÞ = p1ffiffi ð∞ xðtÞψ∗ t − τ dt ð1Þ s −∞. Using a proper scale-to-frequency relationship, CWT provides a time-frequency analysis similar to the short-time Fourier transform. A usual qualitative approach is a method based on a similarity between the mother wavelet and the analyzed signal

Methods

Discussion

Conclusion