In this article statistical inference for the failure time distribution of a product from "field return data", that records the time between the product being shipped and returned for repair or replacement, is described. The problem that is addressed is that the data are not failure times because they also include the time that it took to ship and install the product and then to return it to the manufacturer for repair or replacement. The inference attempts to infer the distribution of time to failure (that is, from installation to failure) from the data when in addition there are separate data on the times from shipping to installation, and from failure to return. The method is illustrated with data from units installed in a telecommunications network.

& CONCLUSIONSAccelerated environmental stress tests (EST) are applied during the manufacturing process to improve reliability by precipitating and detecting latent defects. This test represents an in-process manufacturing screen and the objective of performing it is to avoid early field failures that reduce the customer satisfaction level and increase warranty and compensation costs.Temperature cycling during EST is one of the most commonly used test procedures. Although it is an expensive and energy intensive procedure, usually a lengthy test is initially recommended for a new product. Based on the product test performance or a possible manufacturing process modification, the test duration and regime may be changed after some period. Even if the number of test cycles is reduced, EST continues to be an expensive test and a major process bottleneck.This paper uses Generalized Linear Modeling (GLM) to investigate the effects of the production and EST test variables on the population under test. Both the number of units rejected and the time to failure can be modeled as a regression function of covariates representative of the test environment. The field reliability function is written as a product of the unconditional reliability in each segment of the test profile such as dwell, ramp, etc. The next step is to apply the result of the temperature cycle EST GLM to a mathematical cost model. This cost model includes both the test cost and the warranty and compensation costs of the early field failures. The optimum test regime and number of cycles, which minimizes the total cost is determined by combining the GLM and the cost model. In this way the production test regime can be optimized in terms of field reliability/test cost trade-off. Notations T: Initial EST duration, i.e. 480 minutes τ : Optimum EST duration p t : p -quantile of the lifetime distribution 0 t : Failure time in EST C : Fixed cost of performing EST per unit V

Estimation of the carbon footprint for telecommunications and electronics products has become a key design activity. Improvement in carbon footprint must be demonstrated consistently on all new products. Current methods of providing a useful estimate are laborious and time-consuming. A simplified approach is described, which uses basic statistical methods for estimation and promises significant improvements over existing methods.

Temperature cycling environmental stress testing (EST) • Humidity and pollutants (corrosive gases) • Mechanical, e.g., vibration (sinusoidal or random), shock, or impact • Operational, e.g., variations in rated voltage, current or power Stimuli may be applied individually, consecutively or concurrently. For PSEST, temperature cycling is most commonly employed.EST procedures are time-consuming and energyintensive, however, and there is a clear imperative for test optimization in order to reduce costs. In general, the application of comprehensive and lengthy PSEST is advisable for new products or manufacturing procedures in order to detect and correct design or process flaws. As the maturity of the product or process IntroductionAccelerated environmental stress tests (EST) are commonly applied during the design and manufacture of telecommunications equipment in order to ensure reliability [1,6]. Lucent Technologies conducts design EST (DEST) and production sampling EST (PSEST) procedures for network products. DEST is usually applied during the product development process, with the objective of correcting design flaws, and thereby "ruggedizing" the hardware [7]. PSEST is typically used as an end-of-line manufacturing screen to precipitate and detect latent defects in order to obviate so-called infant mortality failures. A range of stimuli can be applied during EST [2,7,12] improves, the duration of PSEST may be decreased, eventually yielding to a short test performed on a sampling basis. Conversely, it may be necessary to increase test duration if design or process modifications are implemented. In the case of modifications, the equipment manufacturer could be at risk of incurring the increased warranty costs associated with the release of marginal products to customers. Optimization of PSEST for contemporary telecommunications equipment is challenging, primarily because it is common for many mechanisms to cause failure during the tests. A single, dominant mechanism-or, at most, a small number of mechanisms-would facilitate optimization based on the analyses of root causes. For complex systems with multiple failure mechanisms, optimization is usually achieved by the analysis of time-to-failure data. The relationship between time-to-failure and the applied stress is the basis for the optimization of the profile and duration of the stress-for temperature cycling, the profile is described in terms of hot and cold soak temperatures, ramp rates, soak durations, and number of cycles. Furthermore, time-to-failure data from PSEST can be extrapolated in order to estimate the number of escapes-the marginal components not precipitated to failure and detected during PSEST-in order to evaluate the probability of occurrence of early-life failures. Finally, the criterion for the optimization of PSEST is a balance of costs [4,5,23]: on the one hand, the cost of screening, which include test resources, scheduling, and time; and on the other, the costs associated with field repair, warranties and damage to customer satisfaction. Th...

The problem of optimizing accelerated production testing is a pressing one in most electronic manufacturing facilities. Yet, practical models are scarce in the literature, especially for testing high volumes of electronic circuit packs in failure-accelerating environments. In this paper, we develop both a log-linear and linear model, based initially on the Weibull distribution. The models developed are suitable for modeling accelerated production testing data from a temperature-cycled environment. The model is "piecewise" in that the failures in each discrete "piece" of the temperature cycle are modeled as if the testing was in parallel rather than sequential mode. An extra covariate is introduced to indicate age at the start of each piece. The failures in a piece then depend on the stress in the piece itself and the time elapsed to the start of the piece. This last dependence captures the influence of reliability growth and has the result of providing * Centre for Telecommunications Value-Chain Research. 555 Int. J. Rel. Qual. Saf. Eng. 2008.15:555-579. Downloaded from www.worldscientific.com by RUTGERS UNIVERSITY on 04/04/15. For personal use only. 556 T. Joyce et al.an alternative linear model to the log-linear one. The paper demonstrates a simpler use of Poisson regression. An application, using actual production data, is described. Uses of the Loglogistic, Logistic, Lognormal and Normal distributions are also illustrated.