Statistical Reliability with Applications

This chapter reviews fundamental ideas in reliability theory and inference. The first part of the chapter accounts for lifetime distributions that are used in engineering reliability analyis, including general properties of reliability distributions that pertain to lifetime for manufactured products. Certain distributions are formulated on the basis of simple physical properties, and other are more or less empirical. The first part of the chapter ends with a description of graphical and analytical methods to find appropriate lifetime distributions for a set of failure data. The second part of the chapter describes statistical methods for analyzing reliability data, including maximum likelihood estimation and likelihood ratio testing. Degradation data are more prevalent in experiments in which failure is rare and test time is limited. Special regression techniques for degradation data can be used to draw inference on the underlying lifetime distribution, even if failures are rarely observed. The last part of the chapter discusses reliability for systems. Along with the components that comprise the system, reliability analysis must take account of the system configuration and (stochastic) component dependencies. System reliability is illustrated with an analysis of logistics systems (e.g., moving goods in a system of product sources and retail outlets). Robust reliability design can be used to construct a supply chain that runs with maximum efficiency or minimum cost

A Stochastic Approach to Measurement-Driven Damage Detection And Prognosis in Structural Health Monitoring

Damage detection and prognosis are integral to asset management of critical mechanical and civil engineering infrastructure. In practice, these two aspects are often decoupled, where the former is carried out independently using sensor data (e.g., vibrations), while the latter is undertaken based on reliability principles using life time failure data of the system or the component of interest. Only in a few studies damage detection results are extended to remaining useful life estimation, which is achieved by modeling the underlying degradation process using a surrogate measure of degradation. However, an integrated framework which undertakes damage detection, prognosis, and maintenance planning in a systematic way is lacking in the literature. Furthermore, the parameters of degradation model which are utilized for prognosis are often solely estimated using the degradation data obtained from the monitored unit, which represents the degradation of a specific unit, but ignores the general population trend. The main objectives of this thesis are three-fold: first, a mathematical framework using surrogate measure of degradation is developed to undertake the damage detection and prognosis in a single framework; next, the prior knowledge obtained from the historical failed units are integrated in model parameter estimation and residual useful life (RUL) updating of a monitored unit using a Bayesian approach; finally, the proposed degradation modeling framework is applied for maintenance planning of civil and industrial systems, specifically, for reinforced concrete beams and rolling element bearings. The initiation of a fault in these applications is often followed by a sudden change in the degradation path. The location of a change-point can be associated with a sudden loss of stiffness in the case of structural members, or fault initiation in the case of bearings. Hence, in this thesis, the task of change point location identification is thought of as being synonymous with damage or fault detection in the context of structural health monitoring. Furthermore, the change point results are used for two-phase degradation modeling, future degradation level prediction and subsequent RUL estimation. The model parameters are updated using a Bayesian approach, which systematically integrates the prior knowledge obtained from historical failure-time data with monitored data obtained from an in-situ unit. Once such a model is established, it is projected to a failure threshold, thereby allowing for RUL estimation and maintenance planning. Results from the numerical as well as actual field data shows that the proposed degradation modeling framework is good in performing these two tasks. It was also found that as more degradation data is utilized from the monitoring unit, the progressing fault is detected in a timely manner and the model parameters estimates and the end life predictions become more accurate

Landscape genetic connectivity in a riparian foundation tree is jointly driven by climatic gradients and river networks

Fremont cottonwood (Populus fremonti) is a foundation riparian tree species that drives community structure and ecosystem processes in southwestern U.S. ecosystems. Despite its ecological importance, little is known about the ecological and environmental processes that shape its genetic diversity, structure, and landscape connectivity. Here, we combined molecular analyses of 82 populations including 1312 individual trees dispersed over the species’ geographical distribution. We reduced the data set to 40 populations and 743 individuals to eliminate admixture with a sibling species, and used multivariate restricted optimization and reciprocal causal modeling to evaluate the effects of river network connectivity and climatic gradients on gene flow. Our results confirmed the following: First, gene flow of Fremont cottonwood is jointly controlled by the connectivity of the river network and gradients of seasonal precipitation. Second, gene flow is facilitated by mid-sized to large rivers, and is resisted by small streams and terrestrial uplands, with resistance to gene flow decreasing with river size. Third, genetic differentiation increases with cumulative differences in winter and spring precipitation. Our results suggest that ongoing fragmentation of riparian habitats will lead to a loss of landscape-level genetic connectivity, leading to increased inbreeding and the concomitant loss of genetic diversity in a foundation species. These genetic effects will cascade to a much larger community of organisms, some of which are threatened and endangered

The global tree carrying capacity (keynote)

editorial reviewe

ALIPHATIC SILICA‐EPOXY SYSTEMS CONTAINING DOPO‐BASED FLAME RETARDANTS, BIO‐WASTES, AND OTHER SYNERGISTS

Most industrial applications require polymer‐based materials showing excellent fire performances to satisfy stringent requirements. No‐dripping and self‐extinguishing hybrid silica‐epoxy composites can be prepared by combining tailored sol‐gel synthesis strategies with DOPO‐based flame retardants, bio‐wastes, and other synergists. This approach allows for achieving V‐0 rating in UL‐94 vertical flame spread tests, even using a sustainable route, aliphatic amine as hardener, and low P loadings

Gas Turbines

This book is intended to provide valuable information for the analysis and design of various gas turbine engines for different applications. The target audience for this book is design, maintenance, materials, aerospace and mechanical engineers. The design and maintenance engineers in the gas turbine and aircraft industry will benefit immensely from the integration and system discussions in the book. The chapters are of high relevance and interest to manufacturers, researchers and academicians as well

Applying genomic data in wildlife monitoring: Development guidelines for genotyping degraded samples with reduced single nucleotide polymorphism (SNP) panels

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Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen