Reliability analysis for systems with outsourced components

The current business model for many industrial firms is to function as system integrators, depending on numerous outsourced components from outside component suppliers. This practice has resulted in tremendous cost savings; it makes system reliability analysis, however, more challenging due to the limited component information available to system designers. The component information is often proprietary to component suppliers. Motivated by the need of system reliability prediction with outsourced components, this work aims to explore feasible ways to accurately predict the system reliability during the system design stage. Four methods are proposed. The first method reconstructs component reliability functions using limited reliability data with respect to component loads, and the system reliability is then estimated statistically. The second method applies two-class support vector machines (SVM) to approximate limit-state functions of outsourced components based on the categorical reliability dataset. With the integration of the obtained limit-state functions and those of in-house components, the joint probability density function of all the components is estimated, thereby leading to accurate system reliability prediction. The third method is an extension of the second one, and a one-class SVM is proposed to rebuild limit-state functions for outsourced components given only the failure dataset. The last method deals with the case where no reliability dataset is available. A partial safety factor method is developed, which enables component suppliers to provide sufficient information to system designers for accurate reliability analysis without revealing the proprietary design details. Both numerical examples and engineering applications demonstrate the accuracy and effectiveness of the proposed methods --Abstract, page iv

Reliability prediction in early design stages

In the past, reliability is usually quantified with sufficient information available. This is not only time-consuming and cost-expensive, but also too late for occurred failures and losses. For solving this problem, the objective of this dissertation is to predict product reliability in early design stages with limited information. The current research of early reliability prediction is far from mature. Inspired by methodologies for the detail design stage, this research uses statistics-based and physics-based methodologies by providing general models with quantitative results, which could help design for reliability and decision making during the early design stage. New methodologies which accommodate component dependence, time dependence, and limited information are developed in this research to help early accurate reliability assessment. The component dependence is considered implicitly and automatically without knowing component design details by constructing a strength-stress interference model. The time-dependent reliability analysis is converted into its time-independent counterpart with the use of the extreme value of the system load by simulation. The effect of dependent interval distribution parameters estimated from limited point and interval samples are also considered to obtain more accurate system reliability. Optimization is used to obtain narrower system reliability bounds compared to those from the traditional method with independent component assumption or independent distribution parameter assumption. With new methodologies, it is possible to obtain narrower time-dependent system reliability bounds with limited information during early design stages by considering component dependence and distribution parameter dependence. Examples are provided to demonstrate the proposed methodologies --Abstract, page iv

Experimental analysis of computer system dependability

This paper reviews an area which has evolved over the past 15 years: experimental analysis of computer system dependability. Methodologies and advances are discussed for three basic approaches used in the area: simulated fault injection, physical fault injection, and measurement-based analysis. The three approaches are suited, respectively, to dependability evaluation in the three phases of a system's life: design phase, prototype phase, and operational phase. Before the discussion of these phases, several statistical techniques used in the area are introduced. For each phase, a classification of research methods or study topics is outlined, followed by discussion of these methods or topics as well as representative studies. The statistical techniques introduced include the estimation of parameters and confidence intervals, probability distribution characterization, and several multivariate analysis methods. Importance sampling, a statistical technique used to accelerate Monte Carlo simulation, is also introduced. The discussion of simulated fault injection covers electrical-level, logic-level, and function-level fault injection methods as well as representative simulation environments such as FOCUS and DEPEND. The discussion of physical fault injection covers hardware, software, and radiation fault injection methods as well as several software and hybrid tools including FIAT, FERARI, HYBRID, and FINE. The discussion of measurement-based analysis covers measurement and data processing techniques, basic error characterization, dependency analysis, Markov reward modeling, software-dependability, and fault diagnosis. The discussion involves several important issues studies in the area, including fault models, fast simulation techniques, workload/failure dependency, correlated failures, and software fault tolerance

Prediction of ball and roller bearing thermal and kinematic performance by computer analysis

Characteristics of good computerized analysis software are suggested. These general remarks and an overview of representative software precede a more detailed discussion of load support system analysis program structure. Particular attention is directed at a recent cylindrical roller bearing analysis as an example of the available design tools. Selected software modules are then examined to reveal the detail inherent in contemporary analysis. This leads to a brief section on current design computation which seeks to suggest when and why computerized analysis is warranted. An example concludes the argument offered for such design methodology. Finally, remarks are made concerning needs for model development to address effects which are now considered to be secondary but are anticipated to emerge to primary status in the near future

Icebergs in the Clouds: the Other Risks of Cloud Computing

Cloud computing is appealing from management and efficiency perspectives, but brings risks both known and unknown. Well-known and hotly-debated information security risks, due to software vulnerabilities, insider attacks, and side-channels for example, may be only the "tip of the iceberg." As diverse, independently developed cloud services share ever more fluidly and aggressively multiplexed hardware resource pools, unpredictable interactions between load-balancing and other reactive mechanisms could lead to dynamic instabilities or "meltdowns." Non-transparent layering structures, where alternative cloud services may appear independent but share deep, hidden resource dependencies, may create unexpected and potentially catastrophic failure correlations, reminiscent of financial industry crashes. Finally, cloud computing exacerbates already-difficult digital preservation challenges, because only the provider of a cloud-based application or service can archive a "live," functional copy of a cloud artifact and its data for long-term cultural preservation. This paper explores these largely unrecognized risks, making the case that we should study them before our socioeconomic fabric becomes inextricably dependent on a convenient but potentially unstable computing model.Comment: 6 pages, 3 figure

Advanced Rotorcraft Transmission (ART) program-Boeing helicopters status report

The Advanced Rotorcraft Transmission (ART) program is structured to incorporate key emerging material and component technologies into an advanced rotorcraft transmission with the intention of making significant improvements in the state of the art (SOA). Specific objectives of ART are: (1) Reduce transmission weight by 25 pct.; (2) Reduce transmission noise by 10 dB; and (3) Improve transmission life and reliability, while extending Mean Time Between Removal to 5000 hr. Boeing selected a transmission sized for the Tactical Tilt Rotor (TTR) aircraft which meets the Future Air Attack Vehicle (FAVV) requirements. Component development testing will be conducted to evaluate the high risk concepts prior to finalizing the advanced transmission configuration. The results of tradeoff studies and development test which were completed are summarized