Abstract

Structural health monitoring (SHM) is the continuous on-board monitoring of a structure’s condition during operation by integrated systems of sensors. SHM is believed to have the potential to increase the safety of the structure while reducing its deadweight and downtime. Numerous SHM methods exist that allow the observation and assessment of different damages of different kinds of structures. Recently data fusion on different levels has been getting attention for joint damage evaluation by different SHM methods to achieve increased assessment accuracy and reliability. However, little attention is given to the question of which SHM methods are promising to combine. The current article addresses this issue by demonstrating the theoretical capabilities of a number of prominent SHM methods by comparing their fundamental physical models to the actual effects of damage on metal and composite structures. Furthermore, an overview of the state-of-the-art damage assessment concepts for different levels of SHM is given. As a result, dynamic SHM methods using ultrasonic waves and vibrations appear to be very powerful but suffer from their sensitivity to environmental influences. Combining such dynamic methods with static strain-based or conductivity-based methods and with additional sensors for environmental entities might yield a robust multi-sensor SHM approach. For demonstration, a potent system of sensors is defined and a possible joint data evaluation scheme for a multi-sensor SHM approach is presented.

Highlights

  • Economic and ecological requirements of modern industries call for highly optimized lightweight structures

  • For the pure detection of a damage (SHM Level 1), simple statistical damage metrics analyze the difference between the measured signal in the pristine state of the structure and in its actual state [70]

  • The capabilities of structural health monitoring (SHM) methods are mainly given by the structural properties to which they are sensitive and if the extracted damage features can be back-calculated to information on existence, location, type, and size (SHM Levels 1–4) of a damage

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Summary

Introduction

Economic and ecological requirements of modern industries call for highly optimized lightweight structures. Different physical effects are used to assess potential damage by numerous evaluation methods. SHM systems monitor and evaluate the physical properties of a structure based on so-called damage features and damage indicators. The assignment of damage features and indicators to a change in the structure is data- or model-based (e.g., guided wave triangulation for localization [19] and scattering pattern for identification [6]). Different SHM methods have different potentials to evaluate certain damages as it is advantageous to measure structural changes by most directly related features. Different types of damages effect different structural properties, and, the combined use of various SHM methods and their features is better for an accurate and reliable evaluation [21]. The present contribution deals with the potential advantages of heterogeneous multi-sensor data fusion for SHM, with particular emphasis on the fusion of different SHM methods

Data Fusion Overview
Fundamental SHM Methods
EIT with Conductive Surface Layers
EIT with Conductive Structural Components
Vibration Analyses with Electro-Mechanical Impedance Method
Damage Detection and Localization with UGW
Damage Identification with UGW
Summary of Damage Assessment Capabilities of SHM Methods
SHM Method dynamic static strain sensing
Multi-Sensor Approach to SHM of Metal and Composite Structures
Selection of SHM Methods and Sensors
Definition of Data Evaluation Procedure
Conclusions
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