O. Arda Vanli

A minimax sensor placement approach for damage detection in composite structures

This article proposes a new method for optimal placement of sensors for detecting damages in composite structures. The problem is formulated as a minimax optimization in which the goal is to find the coordinates of a given number of sensors so that the worst (maximum) probability of nondetection of the sensor network is made as good as possible (minimized). It is shown that a minimax approach can more efficiently place the sensors on complex geometries, compared to existing placement methods that consider average probability of detection. The method allows one to account for characteristics of sensors by assuming that the effectiveness of a sensor decreases with the distance from damage via an experimentally determined sensor probability of detection function and sensor noise in sensor network optimization. The formulation also enables to account for nonuniform likelihood of damages on the structure, which often arises due to irregular loading or boundary conditions, using a damage probability density. Numerical examples and an experimental validation study involving a Lamb-wave sensing system are presented to show the effectiveness of the proposed method.

A Review of Spectral Methods for Dispersion Characterization of Carbon Nanotubes in Aqueous Suspensions

Characterization is a crucial step in the study of properties of nanomaterials to evaluate their full potential in applications. Carbon nanotube-based materials have properties that are sensitive to size, shape, concentration, and agglomeration state. It is therefore critical to quantitatively characterize these factors in situ, while the processing takes place. Traditional characterization techniques that rely on microscopy are often time consuming and in most cases provide qualitative results. Spectroscopy has been studied as an alternative tool for identifying, characterizing, and studying these materials in situ and in a quantitative way. In this paper, we provide a critical review of the spectroscopy techniques used to explore the surface properties (e.g., dispersion) characteristics of carbon nanotubes in aqueous suspensions during the sonication process.

Minimizing Carbon Dioxide Emissions Due to Container Handling at Marine Container Terminals via Hybrid Evolutionary Algorithms

Considering a rapidly increasing seaborne trade and drastic climate changes due to emissions, produced by oceangoing vessels and container handling equipment, marine container terminal operators not only have to improve effectiveness of their operations to serve the increasing demand, but also to account for the environmental impact associated with the terminal operations. This paper proposes a novel mixed integer mathematical model for the berth scheduling problem, which minimizes the total service cost of vessels, including the total carbon dioxide emission cost due to container handling. The latter pollutant is a primary greenhouse gas that causes global warming. A Hybrid Evolutionary Algorithm, which deploys a set of local search heuristics, is developed to solve the problem. Computational experiments showcase that the optimality gap of the proposed solution algorithm does not exceed 1.61%. It is further shown that the application of additional local search heuristics allows efficient discovery of promising solutions throughout the search process. Results from numerical experiments also indicate that changes in the carbon dioxide emission cost may significantly affect the design of berth schedules. The developed mathematical model and the proposed solution algorithm can thus be adopted as effective planning tools by the marine container terminal operators and improve the environmental sustainability of the terminal operations.

Closed-Loop System Identification for Small Samples With Constraints

Traditional approaches to closed-loop identification of transfer function models require a sufficiently large data set and model forms that are general enough while at the same time requiring application of some form of external excitation (a “dither signal”) to the process. In the limit, as the dither signal dominates the control actions, identification becomes easier, but the operation of the process becomes closer to that of an uncontrolled (i.e., open-loop) process, which may be unacceptable. This article proposes a closed-loop system identification procedure that aims to improve model parameter estimates by incorporating prior knowledge about the process in the form of constraints without using a dither signal. A Monte Carlo simulation study is presented to illustrate the small-sample benefits of adding various forms of constraints. It is shown how constraints based on process knowledge, which is relatively easy to gain from prior experience, result in best identified models among the class of constraints considered. In particular, prior knowledge of the input–output delay of the process is shown to be the most important for identifying a process operating in closed-loop. An example based on a real process illustrates the advantages of the proposed method over the dither signal approach.

A Bayesian approach for integration of physical and computer experiments for quality improvement in nano-composite manufacturing

This paper presents a new Bayesian predictive approach for quality improvement in nano-composite manufacturing that aims to improve the accuracy of computer model simulations by combining physical process data and computer model predictions. Such a data augmentation method is of crucial importance for capital-intensive nano-composite processes for which large experimental studies are usually very costly or impractical. A simulation example and a case study on a real vacuum-assisted resin transfer molding (VARTM) composite manufacturing process are used to illustrate the proposed approach. The results of these studies show that the proposed approach can achieve more accurate predictions of the process variability than pure empirically based or pure simulation-based methods for a given experimental data size. An important practical implication of the results of this work is that, by using the proposed Bayesian method that leverages computational methods with the experimental data, one can accomplish significant reduction in product development costs by reducing the number of physical experiments and improving the prediction accuracy of computer models. Copyright

A failure time prediction method for condition-based maintenance

ABSTRACT Due to uncertainties in material properties and use conditions, reliability predictions are often subject to considerable error. Such inaccurate predictions lead to maintenance decisions that are expensive or not able to prevent failures. This article proposes a Bayesian approach for failure time prediction of degrading components from condition data that accounts for uncertainties and incorporates prior information on degradation behavior via prior distributions. The approach is based on posterior sampling to handle more general statistical models. Simulation examples are presented to show that by incorporating condition monitoring data or including prior knowledge the effectiveness of maintenance decisions can be significantly improved. Application of the approach in a real setting is illustrated using data from the literature.

Hurricane Loss Analysis Based on the Population-Weighted Index

This paper discusses different measures for quantifying regional hurricane loss. The main measures used in the past are normalized percentage loss and dollar value loss. In this research, we show that these measures are useful but may not properly reflect the size of the population influenced by hurricanes. A new loss measure is proposed that reflects the hurricane impact on people occupying the structure. For demonstrating the differences among these metrics, regional loss analysis was conducted for Florida. The regional analysis was composed of three modules: the hazard module stochastically modeled the wind occurrence in the region; the vulnerability module utilized vulnerability functions developed in this research to calculate the loss; and the financial module quantified the hurricane loss. In the financial module, we calculated three loss metrics for certain region. The first metric is the average annual loss (AAL) which represents the expected loss per year in percentage. The second is the average annual dollar loss (AADL) which represents the expected dollar amount loss per year. The third is the average annual population-weighted loss (AAPL) — a new measure proposed in this research. Compared to the AAL, the AAPL reflects the number of people influenced by the hurricane. The advantages of the AAPL are illustrated using three different analysis examples: 1) conventional regional loss analysis, 2) mitigation potential analysis, and 3) forecasted future loss analysis due to the change in population.

An adaptive Bayesian approach for robust parameter design with observable time series noise factors

In Robust Parameter Design (RPD) the means and the covariances of noise variables, commonly assumed as known, are estimated from operating or historical data and hence can involve considerable sampling variability. In addition, for cases where there are noise factors that are measurable or with strong autocorrelation a more effective control strategy is to update the estimates of noise factor as the production takes place. This article presents a Bayesian approach to online RPD that accounts for uncertainty in noise factor and response models and allows the user to update the model estimates with production data and achieve more effective control performance. The proposed method is compared to existing dual response and certainty equivalence control approaches from the literature. Simulation examples and a case study that uses real manufacturing data from an injection molding process are used to demonstrate the proposed method.

Regularized discriminant analysis for multi-sensor decision fusion and damage detection with Lamb waves

In this study we propose a regularized linear discriminant analysis approach for damage detection which does not require an intermediate feature extraction step and therefore more efficient in handling data with high-dimensionality. A robust discriminant model is obtained by shrinking of the covariance matrix to a diagonal matrix and thresholding redundant predictors without hurting the predictive power of the model. The shrinking and threshold parameters of the discriminant function (decision boundary) are estimated to minimize the classification error. Furthermore, it is shown how the damage classification achieved by the proposed method can be extended to multiple sensors by following a Bayesian decision-fusion formulation. The detection probability of each sensor is used as a prior condition to estimate the posterior detection probability of the entire network and the posterior detection probability is used as a quantitative basis to make the final decision about the damage.

Monitoring of Proportional‐Integral Controlled Processes using a Bayesian Time Series Analysis Method

Recently, there has been interest in applying statistical process monitoring methods to processes controlled with feedback controllers in order to eliminate assignable causes and achieve reduced overall variability. In this paper, we propose a Bayesian change-point method to monitor processes regulated with proportional-integral controllers. The approach is based on fitting an exponential rise model to the control input actions in response to a step shift and employs a change-point method to detect the change. Simulation studies show that the proposed method has better run-length performance in detecting step shifts in controlled processes than Shewhart chart on individuals and special-cause chart on residuals of time series model. Copyright