Advanced methodologies for reliability-based design optimization and structural health prognostics

Failures of engineered systems can lead to significant economic and societal losses. To minimize the losses, reliability must be ensured throughout the system's lifecycle in the presence of manufacturing variability and uncertain operational conditions. Many reliability-based design optimization (RBDO) techniques have been developed to ensure high reliability of engineered system design under manufacturing variability. Schedule-based maintenance, although expensive, has been a popular method to maintain highly reliable engineered systems under uncertain operational conditions. However, so far there is no cost-effective and systematic approach to ensure high reliability of engineered systems throughout their lifecycles while accounting for both the manufacturing variability and uncertain operational conditions. Inspired by an intrinsic ability of systems in ecology, economics, and other fields that is able to proactively adjust their functioning to avoid potential system failures, this dissertation attempts to adaptively manage engineered system reliability during its lifecycle by advancing two essential and co-related research areas: system RBDO and prognostics and health management (PHM). System RBDO ensures high reliability of an engineered system in the early design stage, whereas capitalizing on PHM technology enables the system to proactively avoid failures in its operation stage. Extensive literature reviews in these areas have identified four key research issues: (1) how system failure modes and their interactions can be analyzed in a statistical sense; (2) how limited data for input manufacturing variability can be used for RBDO; (3) how sensor networks can be designed to effectively monitor system health degradation under highly uncertain operational conditions; and (4) how accurate and timely remaining useful lives of systems can be predicted under highly uncertain operational conditions. To properly address these key research issues, this dissertation lays out four research thrusts in the following chapters: Chapter 3 - Complementary Intersection Method for System Reliability Analysis, Chapter 4 - Bayesian Approach to RBDO, Chapter 5 - Sensing Function Design for Structural Health Prognostics, and Chapter 6 - A Generic Framework for Structural Health Prognostics. Multiple engineering case studies are presented to demonstrate the feasibility and effectiveness of the proposed RBDO and PHM techniques for ensuring and improving the reliability of engineered systems within their lifecycles

Risk-Averse Optimization for Resilience Enhancement of Complex Engineering Systems under Uncertainties

With the growth of complexity and extent, large scale interconnected network systems, e.g. transportation networks or infrastructure networks, become more vulnerable towards external disruptions. Hence, managing potential disruptive events during the design, operating, and recovery phase of an engineered system therefore improving the system's resilience is an important yet challenging task. In order to ensure system resilience after the occurrence of failure events, this study proposes a mixed-integer linear programming (MILP) based restoration framework using heterogeneous dispatchable agents. Scenario-based stochastic optimization (SO) technique is adopted to deal with the inherent uncertainties imposed on the recovery process from nature. Moreover, different from conventional SO using deterministic equivalent formulations, additional risk measure is implemented for this study because of the temporal sparsity of the decision making in applications such as the recovery from extreme events. The resulting restoration framework involves a large-scale MILP problem and thus an adequate decomposition technique, i.e. modified Lagrangian dual decomposition, is also employed in order to achieve tractable computational complexity. Case study results based on the IEEE 37-bus test feeder demonstrate the benefits of using the proposed framework for resilience improvement as well as the advantages of adopting SO formulations

Driving While Reading Using Google Glass Versus Using a Smartphone: Which is More Distracting to Driving Performance?

Using a phone while driving leads to distraction and impaired driving performance. When reading text on a phone, the act of looking away from the road could cause driving impairment. Wearable displays like Google Glass might reduce the visual impairment caused by looking away, even if they do not overcome other factors contributing to impaired driving. However, such devices could also increase impairment by giving drivers the mistaken impression that they can pay attention to both the display and the road simultaneously or impair visual processing by superimposing visual information in the driving scenes. We compared driving performance in a simulated naturalistic driving task while drivers read text on Google Glass or on a smartphone. As expected, reading on Google Glass and the smartphone both impaired driving performance by increasing lane variations, but drivers using Google Glass showed less lane variation compared to smartphone users. To the extent that these metrics reflect better driving performance, Google Glass might somewhat reduce the costs of reading text while driving. Keywords: Driver distraction; Tactical vehicle control; Google Glass; Cellphon

Dichloridobis[3-methyl-4-phenyl-5-(2-pyrid­yl)-4H-1,2,4-triazole-κ2 N 1,N 5]copper(II) 3.33-hydrate

In the title compound, [CuCl2(C14H12N4)2]·3.33H2O, the Cu(II) atom is coordinated by two chelating 3-methyl-4-phenyl-5-(2-pyrid­yl)-1,2,4-triazole ligands and two chloride anions in a distorted octa­hedral geometry with a CuN2N′ 2Cl2 chromophore. The Cu atom is located on an inversion center. Two uncoordinated water mol­ecules lie on threefold rotation axes with disordered H atoms. Two hydrogen bonds are formed between the water mol­ecules, and another between water and a chlorido ligand

A new response surface approach for time-variant reliability analysis

Click on the DOI link to access the article (may not be free).This paper presents a new approach, referred to as the nested extreme response surface (NERS), to efficiently carry out time-dependent reliability analysis and determine the optimal designs. The key of the NERS is to convert the time-dependent reliability analysis to time-independent one through constructing a kriging based nested time prediction model. The efficient global optimization technique is integrated with NERS to extract the extreme time responses of limit state functions and an adaptive response prediction and model maturation mechanism is developed for an optimal balancing of the model accuracy and the computational efficiency. With the nested response surface of time, existing advanced reliability analysis and design methods can be used. The NERS approach is integrated with RBDO for the design of engineered systems with time-dependent probabilistic constraints. The case study results demonstrate the accuracy and the effectiveness of the proposed approach

Deep Belief Network based state classification for structural health diagnosis

Click on the DOI link to access the article (may not be free).Effective health diagnosis provides multifarious benefits such as improved safety, improved reliability and reduced costs for the operation and maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using Deep Belief Networks (DBN). The DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures. The DBN employs a hierarchical structure with multiple stacked Restricted Boltzmann Machines and works through a layer by layer successive learning process. The proposed multi-sensor health diagnosis methodology using the DBN based state classification can be structured in three consecutive stages: first, defining health states and preprocessing the sensory data for DBN training and testing; second, developing DBN based classification models for the diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. The performance of health diagnosis using DBN based health state classification is compared with support vector machine technique and demonstrated with aircraft wing structure health diagnostics and aircraft engine health diagnosis using 2008 PHM challenge data