Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE)

Development of a Damage Quantification Model for Composite Skin-Stiffener Structures

The development of a model-based approach for a damage severity assessment applied on a complex composite skin structure with stiffeners is presented in this paper. Earlier investigations on composite structures with stiffeners revealed that a vibration based structural health monitoring approach, employing the Modal Strain Energy Damage Index (MSE-DI) algorithm can detect and localise delaminations. The next step, performed in the presented part of the research, is to assess the severity of the damage. It is shown that combining results from a fre-quency based analysis and from a modal strain energy based analysis can enhance the quantifica-tion of the severity estimation. This conclusion was drawn by analysing the effect of small masses that were added at a specific location in to mimic a damage, but maintain reversibility of the dam-age. The use of a numerical model to create a virtual test space was found to be valuable for the interpretation of experimental dat

Triple correlation for detection of damage-related nonlinearities in composite structures

Nonlinear effects in vibration responses are investigated for the undamaged composite plate and the composite plate with a delamination. The analysis is focused on higher harmonic generation in vibration responses for various excitation amplitude levels. This effect is investigated using the triple correlation technique. The dynamics of composite plate was modelled using two-dimensional finite elements and the classical lamination theory. The doubled-node approach was used to model delamination area. Mode shapes and natural frequencies were estimated based on numerical models. Next, the delamination divergence analysis was used to obtain relative displacements for delaminated plies. Experimental modal analysis test was carried out to verify the numerical models. The two strongest vibration modes as well as two vibration modes with the smallest and largest motion level of delaminated plies were selected for nonlinear vibration test. The Fisher criterion was employed to verify the effectiveness and confidence level of the proposed technique. The results show that the method can be used not only to reveal nonlinearities, but also to reliably detect impact damage in composites. These results are confirmed using the statistical analysis

Depth estimation of inner wall defects by means of infrared thermography

There two common methods dealing with interpreting data from infrared thermography: qualitatively and quantitatively. On a certain condition, the first method would be sufficient, but for an accurate interpretation, one should undergo the second one. This report proposes a method to estimate the defect depth quantitatively at an inner wall of petrochemical furnace wall. Finite element method (FEM) is used to model multilayer walls and to simulate temperature distribution due to the existence of the defect. Five informative parameters are proposed for depth estimation purpose. These parameters are the maximum temperature over the defect area (Tmax-def), the average temperature at the right edge of the defect (Tavg-right), the average temperature at the left edge of the defect (Tavg-left), the average temperature at the top edge of the defect (Tavg-top), and the average temperature over the sound area (Tavg-so). Artificial Neural Network (ANN) was trained with these parameters for estimating the defect depth. Two ANN architectures, Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) network were trained for various defect depths. ANNs were used to estimate the controlled and testing data. The result shows that 100% accuracy of depth estimation was achieved for the controlled data. For the testing data, the accuracy was above 90% for the MLP network and above 80% for the RBF network. The results showed that the proposed informative parameters are useful for the estimation of defect depth and it is also clear that ANN can be used for quantitative interpretation of thermography data

Key success factors in establishing end-of-life vehicle management system: a primer for Malaysia

In Malaysia, end-of-life vehicles are not properly managed. Their number keeps growing, as reflected by the increasing number of produced and registered vehicles every year. Improper management of end-of-life vehicles endangers environment and social life in Malaysia. Excessive water, air and soil pollution are among the primary effects of improper end-of-life vehicle management. These negative impacts have led to an initiative to develop a framework for establishing end-of-life vehicle management system in Malaysia. A set of preliminary factors and underlying items have been identified from previous research efforts. Then, a large survey, with 300 respondents comprising of vehicle manufacturers and distributors, part dealers, and end-of-life vehicle collectors; and high response rate, was conducted. Responses of the survey were factor-analysed using IBM SPSS Statistics version 20. As a result, the eight success factors namely management responsibility, performance management, capacity management, resource management, stakeholder responsibility, education and awareness, improvement and awareness, and cost management in implementing end-of-life vehicle management system in Malaysia and 33 underlying items are identified and thoroughly discussed. By conducting reliability analysis, all eight success factors are determined to be reliable. Linear relationship among the factors is then confirmed using structural equation modeling. Upon confirmation, the proposed framework containing factors and items, can be valuable for supporting authorities in establishing end-of-life vehicle management system not only in Malaysia, but also in other countries without proper end-of-life vehicle management system

Integrating Multiscale Numerical Simulations with Machine Learning to Predict the Strain Sensing Efficiency of Nano-Engineered Smart Cementitious Composites

Prediction of in-situ strain sensing efficiency of self-sensing cementitious composites using machine learning (ML) requires a large, representative, consistent, and accurate dataset. However, such large experimental dataset is not readily available. Moreover, the success of the ML approach depends on its ability to abide by the fundamental laws of physics. To address these challenges this paper synergistically integrates a validated finite element analysis (FEA)-based multiscale simulation framework with ML to predict the strain-sensing ability of self-sensing cementitious composites enabled by incorporating nano-engineered interfaces. The multiscale simulation framework is leveraged to develop a balanced, representative, complete, and consistent dataset containing 3000 combinations of strain-dependent electromechanical responses. This large dataset is used to predict the strain-sensing ability of the nanoengineered cementitious composites using a feed-forward multilayer perceptron-based neural network (NN) approach which shows excellent prediction efficacy. This paper also applies a Shapley Additive Explanations (SHAP) algorithm to interpret the NN predictions in light of the relative importance of different design parameters on the strain-sensing ability of the composite. Overall, the synergistic and comprehensive approach presented here can be used as a starting point toward the development of reliable performance standards to accelerate the acceptance of these self-sensing cementitious composites for large-scale applications

Novel 2D strain-rate-dependent lamina-based and RVE/phase-based progressive fatigue damage criteria for randomly loaded multi-layer fiber-reinforced composites

Two implicit progressive fatigue damage models that rely on new equivalent-damage and equivalent-stress criteria are presented for the prediction of various failure modes of the composites. The criteria are coupled with lamina-based and representative-volume-element-based damage progression approaches. The common concepts of residual strength and residual stiffness are revisited and modified. A fatigue life assessment algorithm that incorporates the strain-rate-dependence of the fatigue strengths and stiffnesses, and random and asynchronous changes of the stress components, distinct mean values, and phase shifts of the stress components is employed. New ideas and new post-processing procedures are employed in the current research. It is the first time that the significant impacts of the strain-rate-dependence of the properties of the composites on stress and fatigue life analyses are investigated. Results of the proposed fatigue criteria are first implemented to a composite plate with a complex lamination scheme under a random transverse load and the predicted fatigue lives are verified by the experimental results. Then, these criteria are implemented to a composite chassis frame of an SUV car under realistic random road inputs and the theoretical results are verified by the experimental results. Results confirm the significant role of the strain-rate-dependence effects on the fatigue lives

Energy Analysis Method for Hidden Damage Detection

A method of detecting internal defects in composites or other multilayer materials includes generating a wavefield on a surface of the material. Wavefield data is collected from the wavefield on the surface, and the measured wavefield data is processed to provide measured energy data. The method may include generating simulated or predicted energy data for the multilayer material that is compared to the simulated energy data to determine if the multilayer material has internal defects or damage below the surface. The method can be utilized to detect and/or quantify damage or other defects that are "hidden" by damage that is closer to the surface of the material

Data mining using intelligent systems : an optimized weighted fuzzy decision tree approach

Data mining can be said to have the aim to analyze the observational datasets to find relationships and to present the data in ways that are both understandable and useful. In this thesis, some existing intelligent systems techniques such as Self-Organizing Map, Fuzzy C-means and decision tree are used to analyze several datasets. The techniques are used to provide flexible information processing capability for handling real-life situations. This thesis is concerned with the design, implementation, testing and application of these techniques to those datasets. The thesis also introduces a hybrid intelligent systems technique: Optimized Weighted Fuzzy Decision Tree (OWFDT) with the aim of improving Fuzzy Decision Trees (FDT) and solving practical problems. This thesis first proposes an optimized weighted fuzzy decision tree, incorporating the introduction of Fuzzy C-Means to fuzzify the input instances but keeping the expected labels crisp. This leads to a different output layer activation function and weight connection in the neural network (NN) structure obtained by mapping the FDT to the NN. A momentum term was also introduced into the learning process to train the weight connections to avoid oscillation or divergence. A new reasoning mechanism has been also proposed to combine the constructed tree with those weights which had been optimized in the learning process. This thesis also makes a comparison between the OWFDT and two benchmark algorithms, Fuzzy ID3 and weighted FDT. SIx datasets ranging from material science to medical and civil engineering were introduced as case study applications. These datasets involve classification of composite material failure mechanism, classification of electrocorticography (ECoG)/Electroencephalogram (EEG) signals, eye bacteria prediction and wave overtopping prediction. Different intelligent systems techniques were used to cluster the patterns and predict the classes although OWFDT was used to design classifiers for all the datasets. In the material dataset, Self-Organizing Map and Fuzzy C-Means were used to cluster the acoustic event signals and classify those events to different failure mechanism, after the classification, OWFDT was introduced to design a classifier in an attempt to classify acoustic event signals. For the eye bacteria dataset, we use the bagging technique to improve the classification accuracy of Multilayer Perceptrons and Decision Trees. Bootstrap aggregating (bagging) to Decision Tree also helped to select those most important sensors (features) so that the dimension of the data could be reduced. Those features which were most important were used to grow the OWFDT and the curse of dimensionality problem could be solved using this approach. The last dataset, which is concerned with wave overtopping, was used to benchmark OWFDT with some other Intelligent Systems techniques, such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Evolving Fuzzy Neural Network (EFuNN), Genetic Neural Mathematical Method (GNMM) and Fuzzy ARTMAP. Through analyzing these datasets using these Intelligent Systems Techniques, it has been shown that patterns and classes can be found or can be classified through combining those techniques together. OWFDT has also demonstrated its efficiency and effectiveness as compared with a conventional fuzzy Decision Tree and weighted fuzzy Decision Tree