ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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The improvements in wind energy infrastructure have been a constant process throughout many decades. There are new advancements in technology that can further contribute toward the prognostics and health management (PHM) in this industry. These advancements are driven by the need to fully explore the impact of uncertainty, quality and quantity of data, physics-based machine learning (PBML), and digital twin (DT). All these aspects need to be taken into consideration to perform an effective PHM of wind energy infrastructure. To address these aspects, four research questions were formulated. What is the role of uncertainty in machine learning (ML) in diagnostics and prognostics? What is the role of data augmentation and quality of data for ML? What is the role of PBML? What is the role of the DT in diagnostics and prognostics? The methodology used was Preferred Reporting Items for Systematic Review and Meta-Analysis. A total of 143 records, from the last five years, were analyzed. Each of the four questions was answered by discussion of literature, definitions, critical aspects, benefits and challenges, the role of aspect in PHM of wind energy infrastructure systems, and conclusion. This article is available in the ASME Digital Collection at .

In recent years, machine learning (ML) techniques have gained popularity in structural health monitoring (SHM). These have been particularly used for damage detection in a wide range of engineering applications such as wind turbine blades. The outcomes of previous research studies in this area have demonstrated the capabilities of ML for robust damage detection. However, the primary challenge facing ML in SHM is the lack of interpretability of the prediction models hindering the broader implementation of these techniques. For this purpose, this study integrates the novel Shapley Additive exPlanations (SHAP) method into a ML-based damage detection process as a tool for introducing interpretability and, thus, build evidence for reliable decision-making in SHM applications. The SHAP method is based on coalitional game theory and adds global and local interpretability to ML-based models by computing the marginal contribution of each feature. The contribution is used to understand the nature of damage indices (DIs). The applicability of the SHAP method is first demonstrated on a simple lumped mass-spring-damper system with simulated temperature variabilities. Later, the SHAP method has been evaluated on data from an in-operation V27 wind turbine with artificially introduced damage in one of its blades. The results show the relationship between the environmental and operational variabilities (EOVs) and their direct influence on the damage indices. This ultimately helps to understand the difference between false positives caused by EOVs and true positives resulting from damage in the structure. This article is available in the ASME Digital Collection at .

Data-driven wind turbine performance predictions, such as power and loads, are important for planning and operation. Current methods do not take site-specific conditions such as turbulence intensity and shear into account, which could result in errors of up to 10%. In this work, four different machine learning models (k-nearest neighbors regression, random forest regression, extreme gradient boosting regression and artificial neural networks (ANN)) are trained and tested, first on a simulation dataset and then on a real dataset. It is found that machine learning methods that take site-specific conditions into account can improve prediction accuracy by a factor of two to three, depending on the error indicator chosen. Similar results are observed for multi-output ANNs for simulated in- and out-of-plane rotor blade tip deflection and root loads. Future work focuses on understanding transferability of results between different turbines within a wind farm and between different wind turbine types. This article is available in the ASME Digital Collection at .

Structural failures of offshore wind substructures might be less likely than failures of other equipments of the offshore wind turbines, but they pose a high risk due to the possibility of catastrophic consequences. Significant costs are linked to offshore operations, like inspections and maintenance activities, thus remote monitoring shows promise for a cost-efficient structural integrity management. This work aims to investigate the feasibility of a two-level detection, in terms of anomaly identification and location, in the jacket support structure of an offshore wind turbine. A monitoring scheme is suggested by basing the detection on a database of simulated modal properties of the structure for different failure scenarios. The detection model identifies the correct anomaly based on three types of modal indicators, namely, natural frequency, the modal assurance criterion between mode shapes, and the modal flexibility variation. The supervised Fisher's linear discriminant analysis is applied to transform the modal indicators to maximize the separability of several scenarios. A fuzzy clustering algorithm is then trained to predict the membership of new data to each of the scenarios in the database. In a case study, extreme scour phenomena and jacket members' integrity loss are simulated, together with variations of the structural dynamics for environmental and operating conditions. Cross-validation is used to select the best hyperparameters, and the effectiveness of the clustering is validated with slight variations of the environmental conditions. The results prove that it is feasible to detect and locate the simulated scenarios via the global monitoring of an offshore wind jacket structure. This article is available in the ASME Digital Collection at .

The probabilistic safety analysis evaluates system reliability and failure probability by using statistics and probability theory. However, it cannot estimate the system safety uncertainties due to variabilities of probabilities in different system states. The article first summarizes how the information entropy expresses the probabilistic uncertainties due to nonuniform probability distributions of system states. Next, it argues that the conditional entropy of system operational and failure states appropriately describes both system redundancy and robustness. Finally, the article concludes that the probabilistic uncertainties of reliability, redundancy, and robustness jointly define the integral system safety. The concept of integral system safety allows for more precise definitions of efficient system functional properties, system configuration evaluation, optimal component selection, optimization, and decision making in engineering. This article is available in the ASME Digital Collection at .

An essential step in the safe design of systems is choosing the system configuration that will maximize the overall availability of the system and minimize its overall cost. The main objective of this paper is to propose an optimization method of multistate system availability in the presence of both aleatory and epistemic uncertainties, to choose the best configuration for the system in terms of availability, cost, and imprecision. The problem is formulated as follows: let us consider several configurations of a system, with each configuration consisting of components with different working states, and imprecise failure and repair rates provided in the form of intervals. The aim is to find the best configuration regarding the system's imprecise availability, cost, and imprecision. First, the imprecise steady availability of each configuration is computed by using an original method based on Markovian approaches combined with interval contraction techniques. Then an objective function incorporating cost, the lower and upper bounds of availability, and imprecision is defined and computed to provide the best configuration. To illustrate the proposed method, a use case is discussed. This article is available in the ASME Digital Collection at .

Machine guards provide protection against the ejection of parts during operation, such as chips or workpiece fragments. They are considered safe if the impact resistance is at least as high as the resulting projectile energy in the worst case of damage. To protect the machine operator, the impact resistance of machine guards is determined according to ISO standards. The bisection method can be used to determine the impact resistance through impact tests. However, this method is inaccurate for a small number of impact tests and does not indicate uncertainties in the determination. Moreover, the result of testing is validated in different ways depending on the standard utilized for testing. Relevant uncertainties affecting impact testing and a new probabilistic approach for assessing the impact resistance using the Recht and Ipson equation are presented. With multiple impact tests at different initial velocities a Recht and Ipson best-fit curve and a confidence interval for a ballistic limit can be obtained, which is used to determine the impact resistance by defining a velocity reduction coefficient. This method can be applied to any machine guard made of ductile material. This paper validates the Recht and Ipson method by performing impact tests with a standardized 2.5 kg projectile on polycarbonate sheets of different thicknesses. Determination of the ballistic limit showed good agreement with experimental results. With the ballistic limits, the velocity reduction coefficients have been found to determine the impact resistances. Therefore, an alternative method for standardized tests to determine the impact resistance was found. This article is available in the ASME Digital Collection at .

The purpose of this work is to enable the use of the Dempster–Shafer evidence theory (ET) for uncertainty propagation on computationally expensive automotive crash simulations. This is necessary as the results of these simulations are influenced by multiple possibly uncertain aspects. To avoid negative effects, it is important to detect these factors and their consequences. The challenge when pursuing this effort is the prohibitively high computational cost of the ET. To this end, we present a framework of existing methods that is specifically designed to reduce the necessary number of full model evaluations and parameters. An initial screening removes clearly irrelevant parameters to mitigate the curse of dimensionality. Next, we approximate the full-scale simulation using metamodels to accelerate output generation and thus enable the calculation of global sensitivity indices. These indicate effects of the parameters on the considered output and more profoundly sort out irrelevant parameters. After these steps, the ET can be performed rapidly and feasibly due to fast-responding metamodel and reduced input dimension. It yields bounds for the cumulative distribution function of the considered quantity of interest. We apply the proposed framework to a simplified crash test dummy model. The elementary effects method is used for screening, a kriging metamodel emulates the finite element simulation, and Sobol' sensitivity indices are determined before the ET is applied. The outcome of the framework provides engineers with information about the uncertainties they may face in hardware testing and that should be addressed in future vehicle design. This article is available in the ASME Digital Collection at .

A methodology to account for the effect of epistemic uncertainty (regarding model parameters) on the strength prediction of carbon fiber reinforced polymer (CFRP) composite laminates is presented. A three-dimensional concurrent multiscale physics modeling framework is considered. A continuum damage mechanics-based constitutive relation is used for multiscale analysis. The parameters for the constitutive model are unknown and need to be calibrated. A least squares-based approach is employed for the calibration of model parameters and a model discrepancy term. The calibrated constitutive model is validated quantitatively using experimental data for both unnotched and open-hole specimens with different composite layups. The quantitative validation results are used to indicate further steps for model improvement. This article is available in the ASME Digital Collection at .

Government agencies, globally, strive to minimize the likelihood and frequency of human death and severe injury on road transport systems. From an engineering design standpoint, the minimization of these road accident effects on occupants becomes a critical design goal. This necessitates the quantification and management of injury risks on the human body in response to several vehicular impact variables and their associated uncertainties for different crash scenarios. In this paper, we present a decision-based, robust design (RD) framework to quantify and manage the impact-based injury risks on occupants for different computational model-based car crash scenarios. The key functionality offered is the designer's capability to conduct robust concept exploration focused on managing the selected impact variables and associated uncertainties, such that injury risks are controlled within acceptable levels. The framework's efficacy is tested for near-side impact scenarios with impact velocity and angle of impact as the critical variables of interest. Two injury criteria, namely, head injury criterion (HIC) and lateral neck injury criteria (Lateral Nij) are selected to quantitatively measure the head and neck injury risks in each crash simulation. Using the framework, a RD problem is formulated to determine the combination of impact variables that best satisfies the injury goals defined. The framework and associated design constructs are generic and support the formulation and decision-based robust concept exploration of similar problems involving models under uncertainty. Our focus in this paper is on the framework rather than the results per se. This article is available in the ASME Digital Collection at .

Surveys and the resulting data provide powerful insights to an item or concept being assessed. Not only do they garner feedback that can be used to enhance the item, but, in the instance of this article, they can enhance the validity of positive change via novel concepts prior to spending the time and money in incorporating these concepts and running the risk of negligible meaningful return. In an effort to ensure the validity of proposed novel methods in a design engineering context, a survey was developed and administered to engineering subject matter experts. This was done to not only ensure that the proposed methods would be viable if implemented but gave confidence in the results of the research that drove the method development. This assessment activity served as a risk reduction activity to ensure smooth implementation of the methods from both an efficiency standpoint and, most importantly, as part of maximizing system safety. This paper discusses the composition and considerations of the survey administered in the research study, in addition to the survey results with the intention of providing a format for others in a similar context to glean from and, if practical, replicate the method. This article is available in the ASME Digital Collection at .