Kernel Principal Component Analysis for Structural Health Monitoring and Damage Detection of an Engineering Structure Under Operational Loading Variations

Sharafiz Abdul Rahim,Graeme Manson

doi:10.1007/s11668-021-01260-1

Sharafiz Abdul Rahim, Graeme Manson

Open Access

https://doi.org/10.1007/s11668-021-01260-1

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Abstract

This paper highlights kernel principal component analysis (KPCA) in distinguishing damage-sensitive features from the effects of liquid loading on frequency response. A vibration test is performed on an aircraft wing box incorporated with a liquid tank that undergoes various tank loading. Such experiment is established as a preliminary study of an aircraft wing that undergoes operational load change in a fuel tank. The operational loading effects in a mechanical system can lead to a false alarm as loading and damage effects produce a similar reduction in the vibration response. This study proposes a non-nonlinear transformation to separate loading effects from damage-sensitive features. Based on a baseline data set built from a healthy structure that undergoes systematic tank loading, the Gaussian parameter is measured based on the distance of the baseline data set to various damage states. As a result, both loading and damage features expand and are distinguished better. For novelty damage detection, Mahalanobis square distance (MSD) and Monte Carlo-based threshold are applied. The main contribution of this project is the nonlinear PCA projection to understand the dynamic behavior of the wing box under damage and loading influences and to differentiate both effects that arise from the tank loading and damage severities.

Highlights

Structural health monitoring (SHM) is a condition-based maintenance that provides an alternative strategy to traditional maintenance by utilizing sensor networks to detect irregularities through data processing, recognition algorithms and statistical methods
The pattern recognition approach successfully performs damage detection when the structure is under operational and environmental variations (OEVs) based on measurements data from various strain sensors [9].To produce a reliable and robust SHM system, the system should be able to discriminate the OEVs effects from the signal of which the process commonly referred as data normalization
It is indicated that the signal produced by the loading effects in the structure display better sensitivity compared to the effects of damage severities

Summary

Introduction

Structural health monitoring (SHM) is a condition-based maintenance that provides an alternative strategy to traditional maintenance by utilizing sensor networks to detect irregularities through data processing, recognition algorithms and statistical methods. The pattern recognition approach successfully performs damage detection when the structure is under OEVs based on measurements data from various strain sensors [9].To produce a reliable and robust SHM system, the system should be able to discriminate the OEVs effects from the signal of which the process commonly referred as data normalization. The precision value is computed from the distance between the baseline data and test data of various damage classes that undergo equivalent loading conditions.

Results

Conclusion