On A Truncated Accelerated Plan for Two Component Parallel Systems under Ramp-Stress Testing Using Masked Data for Weibull Distribution

Several studies on design of Acceptance Life Test (ALT) focused on a subsystem (single system) totally ignoring its internal design. In most cases, it is not always possible to identify the components that cause the system failure or the cause can only be identified by a subset of its component resulting in a masked observation. This paper therefore investigates into the development of ramp-stress accelerated life testing for a high reliability parallel system that consist of two dependent components using masked failure data. This type of testing may be very useful in a twin-engine plane or jet. A ramp-stress results when stress applied on the system increases linearly with time. A parallel system with two dependent components is taken with dependency modeled by G umbel-Hougaard copula. The stress-life relationship is modeled using inverse power law and cumulative exposure model is assumed to model the effect of changing stress. The method of maximum likelihood is thereafter used for estimating design parameters. This optimal plan consists in finding the optimal stress rate using D-optimality criterion by minimizing the reciprocal of the determinant of Fisher information matrix. The projected plan is also explained using a real life example and sensitivity analysis carried out. This formulated model can help guide and assist engineers to obtain reliability estimates quickly with high reliability products that are sustainable

Trends in the Statistical Assessment of Reliability

Changes in technology have had and will continue to have a strong effect on changes in the area of statistical assessment of reliability data. These changes include higher levels of integration in electronics, improvements in measurement technology and the deployment of sensors and smart chips into more products, dramatically improved computing power and storage technology, and the development of new, powerful statistical methods for graphics, inference, and experimental design and reliability test planning. This paper traces some of the history of the development of statistical methods for reliability assessment and makes some predictions about the future

Effect of carbide distribution on rolling-element fatigue life of AMS 5749

Endurance tests with ball bearings made of corrosion resistant bearing steel which resulted in fatigue lives much lower than were predicted are discussed. Metallurgical analysis revealed an undesirable carbide distribution in the races. It was shown in accelerated fatigue tests in the RC rig that large, banded carbides can reduce rolling element fatigue life by a factor of approximately four. The early spalling failures on the bearing raceways are attributed to the large carbide size and banded distribution

Component Reliability Estimation From Partially Masked and Censored System Life Data Under Competing Risks.

This research presents new approaches to the estimation of component reliability distribution parameters from partially masked and/or censored system life data. Such data are common in continuous production environments. The methods were tested on Monte Carlo simulated data and compared to the only alternative suggested in literature. This alternative did not converge on many masked datasets. The new methods produce accurate parameter estimates, particularly at low masking levels. They show little bias. One method ignores masked data and treats them as censored observations. It works well if at least 2 known-cause failures of each component type have been observed and is particularly useful for analysis of any size datasets with a small fraction of masked observations. It provides quick and accurate estimates. A second method performs well when the number of masked observations is small but forms a significant portion of the dataset and/or when the assumption of independent masking does not hold. The third method provides accurate estimates when the dataset is small but contains a large fraction of masked observations and when independent masking is assumed. The latter two methods provide an indication which component most likely caused each masked system failure, albeit at the price of much computation time. The methods were implemented in user-friendly software that can be used to apply the method on simulated or real-life data. An application of the methods to real-life industrial data is presented. This research shows that masked system life data can be used effectively to estimate component life distribution parameters in a situation where such data form a large portion of the dataset and few known failures exist. It also demonstrates that a small fraction of masked data in a dataset can safely be treated as censored observations without much effect on the accuracy of the resulting estimates. These results are important as masked system life data are becoming more prevalent in industrial production environments. The research results are gauged to be useful in continuous manufacturing environments, e.g. in the petrochemical industry. They will also likely interest the electronics and automotive industry where masked observations are common

Fatigue of friction stir welded lap joints with sealants

A lack of understanding of corrosion fatigue in friction stir welded aluminum joints prevents friction stir welding from being implemented in aerospace applications. Fatigue testing reveals a 60-75% reduction in the fatigue life of friction stir welded aluminum lap joints immersed in 3.5% NaCl solution (corrosion fatigue) compared with that of lap joints tested in ambient air. The loss in fatigue life is attributed to accelerated fatigue cracking due to hydrogen environment embrittlement. Two polymer sealant candidates are investigated: silicone rubber and nylon-11. Both sealant candidates can be applied prior to welding and seal the faying surface gaps in lap joints upon welding. The rubber sealant cures at room temperature after welding and can be welded with the same parameters as without the sealant. The 50% sample population corrosion fatigue life is increased by 22% with the use of the rubber sealant, but the effectiveness of the rubber sealant is limited by its cohesive mechanical properties, e.g. elongation to failure. In ambient fatigue, the nylon sealed welds exhibit twice the 50% sample population fatigue life of other welds. Finite element modeling predicts a reduction in the stresses in the weld due the stiffness contribution of the nylon sealant. The effectiveness of the nylon sealant is limited by its adhesive bond strength. When immersed in water, as in corrosion fatigue, the adhesive bond strength is reduced, the sealant bond fails within 500 fatigue cycles, and the mechanical benefits of the nylon sealant are negated. The corrosion fatigue life of nylon sealed welds is 26% less than that of welds without sealant because of the more severe hook defect associated with hotter welding conditions required to melt the nylon. Finite element modeling results indicate an increase in stress intensity factors of about 10% in welds with more severe hook defects--Abstract, page iii

Hazard rate models for early warranty issue detection using upstream supply chain information

This research presents a statistical methodology to construct an early automotive warranty issue detection model based on upstream supply chain information. This is contrary to extant methods that are mostly reactive and only rely on data available from the OEMs (original equipment manufacturers). For any upstream supply chain information with direct history from warranty claims, the research proposes hazard rate models to link upstream supply chain information as explanatory covariates for early detection of warranty issues. For any upstream supply chain information without direct warranty claims history, we introduce Bayesian hazard rate models to account for uncertainties of the explanatory covariates. In doing so, it improves both the accuracy of warranty issue detection as well as the lead time for detection. The proposed methodology is illustrated and validated using real-world data from a leading global Tier-one automotive supplier

Evaluation of solder-joint reliability for a 10mm Quad Flat Leadless package with top-side paddle using classical models for a leadless device and accelerated life testing

The standard QFN package consists of a leadless perimeter array and a bottom solderable thermal paddle. The thermal performance of the package can be improved by moving the paddle to the topside. The soldered surface area of the package reduces by about 80% with a top-side paddle. The soldered-joint life will also reduce due to the significant thermal coefficient of expansion mismatch between the QFN package and the circuit board. The solder-joint reliability of a large QFN package with top-side paddle is not well understood. This thesis evaluates the solder-joint reliability of a 10mm square leadless QFN package with top-side paddle. The analysis includes several classical models for a leadless package and compares modeling results to accelerated reliability testing. The accelerated tests include the influence mold compound and lead finish play on solder-joint life and ways to improve solder-joint reliability

Knowledge Discovery from Complex Event Time Data with Covariates

In particular engineering applications, such as reliability engineering, complex types of data are encountered which require novel methods of statistical analysis. Handling covariates properly while managing the missing values is a challenging task. These type of issues happen frequently in reliability data analysis. Specifically, accelerated life testing (ALT) data are usually conducted by exposing test units of a product to severer-than-normal conditions to expedite the failure process. The resulting lifetime and/or censoring data are often modeled by a probability distribution along with a life-stress relationship. However, if the probability distribution and life-stress relationship selected cannot adequately describe the underlying failure process, the resulting reliability prediction will be misleading. To seek new mathematical and statistical tools to facilitate the modeling of such data, a critical question to be asked is: Can we find a family of versatile probability distributions along with a general life-stress relationship to model complex lifetime data with covariates? In this dissertation, a more general method is proposed for modeling lifetime data with covariates. Reliability estimation based on complete failure-time data or failure-time data with certain types of censoring has been extensively studied in statistics and engineering. However, the actual failure times of individual components are usually unavailable in many applications. Instead, only aggregate failure-time data are collected by actual users due to technical and/or economic reasons. When dealing with such data for reliability estimation, practitioners often face challenges of selecting the underlying failure-time distributions and the corresponding statistical inference methods. So far, only the Exponential, Normal, Gamma and Inverse Gaussian (IG) distributions have been used in analyzing aggregate failure-time data because these distributions have closed-form expressions for such data. However, the limited choices of probability distributions cannot satisfy extensive needs in a variety of engineering applications. Phase-type (PH) distributions are robust and flexible in modeling failure-time data as they can mimic a large collection of probability distributions of nonnegative random variables arbitrarily closely by adjusting the model structures. In this paper, PH distributions are utilized, for the first time, in reliability estimation based on aggregate failure-time data. To this end, a maximum likelihood estimation (MLE) method and a Bayesian alternative are developed. For the MLE method, an expectation-maximization (EM) algorithm is developed to estimate the model parameters, and the corresponding Fisher information is used to construct the confidence intervals for the quantities of interest. For the Bayesian method, a procedure for performing point and interval estimation is also introduced. Several numerical examples show that the proposed PH-based reliability estimation methods are quite flexible and alleviate the burden of selecting a probability distribution when the underlying failure-time distribution is general or even unknown