Magda Carvalho Pires

Using degradation data to assess reliability: a case study on train wheel degradation

Degradation experiments are usually used to assess the lifetime distribution of highly reliable products, which are not likely to fail under the traditional life tests or accelerated life tests. In such cases, if there exist product characteristics whose degradation over time can be related to reliability, then collecting ‘degradation data’ can provide information about product reliability. In general, the degradation data are modeled by a nonlinear regression model with random coefficients. If we can obtain the estimates of parameters under the model, then the failure-time distribution can be estimated. In order to estimate those parameters, three basic methods are available, namely, the analytical, numerical and the approximate. They are chosen according to the complexity of the degradation path model used in the analysis. In this paper, the numerical and the approximate methods are compared in a simulation study, assuming a simple linear degradation path model. A comparison with traditional failure-time analysis is also performed. The mean-squared error of the estimated 100pth percentile of the lifetime distribution is evaluated for each one of the approaches. The approaches are applied to a real degradation data set. Copyright

Prognosis of glaucoma in relation to blindness at a university hospital.

PURPOSE To assess the prognosis of different types of glaucoma in relation to unilateral and bilateral blindness at a University Hospital. METHODS Charts of glaucomatous patients which presented complete data of clinical history, visual acuity, visual field, fundus examination and diagnosis were retrospectively analyzed. The patients were classified as: not blind, legally blind (best corrected visual acuity <20/200 and/or visual field <20 masculine), or totally blind (no light perception) in one or both eyes. Patients with blindness due to congenital glaucoma and other no glaucomatous causes, and incomplete charts were excluded. RESULTS 3,786 (76.3%) of 4,963 charts fulfilled the criteria. In 3,786 glaucomatous patients, 1,939 (51.2%) were not blind and 1,847 (48.8%) were blind. 1,359 patients (73.6%) were legally blind and 488 (26.4%) totally blind, 1,333 (72.2%) had unilateral blindness and 514 (27.8%), bilateral blindness. 1,564 patients (84.7%) were already blind (74.9% with legal blindness and 25.1% with total blindness) when they arrived at the Service and 283 (15.3%) became blind after their inclusion in the Service. Neovascular glaucoma presented the highest proportion (95.6%) of blindness. Postsurgical glaucoma was second causing blindness in 72.7% and thirdly, primary angle-closure glaucoma with 67.4%. Primary open-angle glaucoma presented the lowest proportion (40.5%) of blindness. CONCLUSIONS Neovascular glaucoma had the worst prognosis with the highest proportion of blindness. Primary angle-closure glaucoma caused blindness roughly 1.7 times more than primary open-angle glaucoma. Primary open-angle glaucoma presented the best prognosis. The proportion of patients that became blind after their inclusion in the Service was relatively low in relation to the proportion of patients who were blind when they arrived at the Service.

A Closer Look at Degradation Models: Classical and Bayesian Approaches

Traditionally, reliability assessment of devices has been based on (accelerated) life tests. However, for highly reliable products, little information about reliability is provided by life tests in which few or no failures are typically observed. Since most failures arise from a degradation mechanism at work for which there are characteristics that degrade over time, one alternative is to monitor the device for a period of time and assess its reliability from the changes in performance (degradation) observed during that period. The goal of this chapter is to illustrate how degradation data can be modeled and analyzed by using “classical” and Bayesian approaches. Four methods of data analysis based on classical inference are presented. Next we show how Bayesian methods can also be used to provide a natural approach to analyzing degradation data. The approaches are applied to a real data set regarding train wheels degradation.

Survival Weibull regression model for mismeasured outcomes

ABSTRACT In some survival studies, the exact time of the event of interest is unknown, but the event is known to have occurred during a particular period of time (interval-censored data). If the diagnostic tool used to detect the event of interest is not perfectly sensitive and specific, outcomes may be mismeasured; a healthy subject may be diagnosed as sick and a sick one may be diagnosed as healthy. In such cases, traditional survival analysis methods produce biased estimates for the time-to-failure distribution parameters (Paggiaro and Torelli 2004). In this context, we developed a parametric model that incorporates sensitivity and specificity into a grouped survival data analysis (a case of interval-censored data in which all subjects are tested at the same predetermined time points). Inferential aspects and properties of the methodology, such as the likelihood function and identifiability, are discussed in this article. Assuming known and non differential misclassification, Monte Carlo simulations showed that the proposed model performed well in the case of mismeasured outcomes; the estimates of the relative bias of the model were lower than those provided by the naive method that assumes perfect sensitivity and specificity. The proposed methodology is illustrated by a study related to mango tree lifetimes.