Damage Classification of a Bolted Connection using Guided Waves and Explainable Artificial Intelligence

With the improvements in computational power and advances in chip and sensor technology, the applications of machine learning (ML) technologies in structural health monitoring (SHM) are increasing rapidly. Compared with traditional methods, deep learning based SHM (Deep SHM) methods are more efficient and have a higher accuracy. However, due...

Cointegration strategy for damage assessment of offshore platforms subject to wind and wave forces

In structural engineering, offshore structures are undoubtedly among the most exposed to the effects of harsh environmental conditions. The external conditions of these semi-immersed systems involve complex combinations of wave and wind loads. The operating conditions are also unique because oil production platforms are subjected to repeated...

Developing health indicators for composite structures based on a two-stage semi-supervised machine learning model using acoustic emission data

Composite structures are highly valued for their strength-to-weight ratio, durability, and versatility, making them ideal for a variety of applications, including aerospace, automotive, and infrastructure. However, potential damage scenarios like impact, fatigue, and corrosion can lead to premature failure and pose a threat to safety. This...

Delamination Size Prediction for Compressive Fatigue Loaded Composite Structures Via Ultrasonic Guided Wave Based Structural Health Monitoring

Under in-plane compressive load conditions, the growth of a delamination initially induced by an impact can be followed by a fast growth after a threshold level, which leads to a catastrophic failure in composite structures. To avoid reaching this critical level, it is essential to uncover the delamination size and growth pattern in real time....

Advanced Health Monitoring of Composite Structures Through Deep Learning-Based Analysis of Lamb Wave Data for Developing Health Indicators

A health indicator (HI) serves as an intermediary link between structural health monitoring (SHM) data and prognostic models, and an efficient HI should meet prognostic criteria, i.e., monotonicity, trendability, and prognosability. However, designing a proper HI for composite structures is a challenging task due to the complex damage...

Interpretable neural network with limited weights for constructing simple and explainable HI using SHM data

Recently, companies all over the world have been focusing on the improvement of autonomous health management systems in order to enhance performance and reduce downtime costs. To achieve this, the remaining useful life predictions have been given remarkable attention. These predictions depend on the proper designing process and the quality of...

Analysis of FBG reflection spectra under uniform and non-uniform transverse loads

Loads applied transversely on the external surface of waveguides change their circular cross-sectional geometry generating birefringence. Due to this effect the reflected spectrum of a Fibre Bragg grating (FBG) undergoes a splitting of the single peak of the Bragg wavelength. In this work, we employed the Transfer Matrix Method (TMM) for x-...

DeepSHM: A deep learning approach for structural health monitoring based on guided Lamb wave technique

In our previous work, we demonstrated how to use inductive bias to infuse a convolutional neural network (CNN) with domain knowledge from fatigue analysis for aircraft visual NDE. We extend this concept to SHM and therefore in this paper, we present a novel framework called DeepSHM which involves data augmentation of captured sensor signals...

Characterisation of Transverse Matrix Cracks in Composite Materials Using Fibre Bragg Grating Sensors

In this paper, we propose a novel approach to characterise barely visible transverse matrix cracks in composite structures using fibre Bragg grating (FBG) optical sensors. Matrix cracks are one of the most prevalent types of damage in composite structures, and detecting them in the internal layers of composites has remained a challenge. In...

Mapping surface deformation and thermal dilation of arch bridges by structure-driven multi-temporal DInSAR analysis

Arch bridges are important transportation infrastructures widely distributed in China, but they are prone to structural defects due to aging without routine inspection and maintenance. Therefore, Structural Health Monitoring (SHM) of these bridges is urgently needed by civil engineers to effectively reduce the risk of bridge damage or...

Transducer Placement Option of Lamb Wave SHM System for Hotspot Damage Monitoring

In this paper, we investigated transducer placement strategies for detecting cracks in primary aircraft structures using ultrasonic Structural Health Monitoring (SHM). The approach developed is for an expected damage location based on fracture mechanics, for example fatigue crack growth in a high stress location. To assess the performance of the...

Modelling non-uniform strain distributions in aerospace composites using fibre Bragg gratings

In this paper the behaviour of fibre Bragg grating (FBG) sensors under non-uniform strain distributions was analysed. Using the fundamental matrix approach, the length of the FBG sensor was discretised, with each segment undergoing different strain values. FBG sensors that are embedded inside composites, also undergo such non-uniform strain...

Structural longevity of FPSO Hulls

The key challenge of managing Floating Production Storage and Offloading assets (FPSOs) for offshore hydrocarbon production lies in maximizing the economic value and productivity, while minimizing the Total Cost of Ownership and operational risk. This is a comprehensive task, considering the increasing demands of performance contracting, (down...