Vibration-based Structural Health Monitoring Methods Research Articles

Detection and Quantification of Delamination Failures in Marine Composite Bulkheads via Vibration Energy Variations.

This paper proposes a new vibration-based structural health monitoring method for the identification of delamination defects in composite bulkheads used in small-length fiber-based ships. The core of this work is to find out if the variations of vibration energy can be efficiently used as a key performance indicator for the detection and quantification of delamination defects in marine composite bulkheads. For this purpose, the changes of vibrational energy exerted by delamination defects in sandwich and monolithic composite panel bulkheads with different types of delamination phenomenon are investigated using a non-destructive test. Experiments show that the overall vibration energy of the bulkheads is directly dependent on the damage conditions of the specimens and therefore, the variations of this parameter are a good indicator of the incorporation of delamination defects in composite bulkheads. Additionally, the overall vibration energy changes also give interesting information about the severity of the delamination defect in the panels. Hence, this methodology based on vibratory energy can be used to accurately determine delamination defects in medium-sized composite bulkheads with the advantages of being a simple and cost-effective approach. The findings of this research possess important applications for the identification of delamination failures in composite components such as bulkheads, turbine blades, and aircraft structures, among others.

An improved time-varying empirical mode decomposition for structural condition assessment using limited sensors

The safety concerns of transport infrastructure around the world have significantly increased over the past few years and pose a significant threat to the public and economic well-being. Condition assessment of transport infrastructure is currently performed using structural health monitoring (SHM). Over the last few decades, SHM of ageing infrastructure is leveraged using advancements in sensing technologies. However, vibration-based SHM methods that rely on sensor-driven data collection face several practical challenges such as traffic interruptions, availability of limited space, and a limited amount of sensors. In this study, a system identification technique is developed, which can deal with these practical challenges of SHM and provide reliable results. The proposed technique involves a time-varying filtering empirical mode decomposition algorithm that can decompose a single measured signal into its mono-component modal responses. The resulting mono-component signals are then analyzed using a wavelet-based reallocation algorithm called Multi-Synchrosqueezing Transform. The iterative nature of this algorithm is utilized to enhance the time-frequency representation of a nonstationary signal obtained from the ageing structures. The capability of the proposed method to identify the modal parameters is demonstrated using a suite of numerical and full-scale studies under various cases of measurement noise, closely-spaced and low energy modes, and damage detection using the limited sensor measurements.

Sequential projection pursuit for optimal transformation of autoregressive coefficients for damage detection in an experimental wind turbine blade

The performance, and with it, the utility of structural health monitoring systems depends strongly on the efficiency of damage sensitive features (DSFs) for describing the state of a structure. Several approaches are available for extracting DSFs from acceleration response signals, but they are often high dimensional. This affects significantly data processing and storage demands. Therefore, reducing DSF dimensions while maintaining or even improving damage detectability is desired. The present study explores the use of sequential projection pursuit for identifying low-dimensional DSF transformations optimized for structural damage detection. Here, transformation vectors are obtained sequentially using an advanced evolutionary optimization technique. A statistical objective function is employed to facilitate making decisions about the structural state with the help of statistical hypothesis testing. Optimal numbers of transformation vectors are found by fast forward selection. The approach is demonstrated using initial DSFs defined as autoregressive coefficients from acceleration response signals of an experimental wind turbine blade. Wind-like excitations were applied with the help of a pedestal fan, and damages were simulated non-destructively by adding small masses. The results demonstrate that the proposed methodology can considerably reduce DSF dimensionalities without deteriorating the damage detection performance. Conversely, the detectability of some damages could be improved in comparison to using selected original DSFs. This is promising for future developments of efficient vibration-based structural health monitoring methods.