Suk Joo Bae

Degradation models and implied lifetime distributions

In experiments where failure times are sparse, degradation analysis is useful for the analysis of failure time distributions in reliability studies. This research investigates the link between a practitioners selected degradation model and the resulting lifetime model. Simple additive and multiplicative models with single random effects are featured. Results show that seemingly innocuous assumptions of the degradation path create surprising restrictions on the lifetime distribution. These constraints are described in terms of failure rate and distribution classes.

A Nonlinear Random-Coefficients Model for Degradation Testing

As an alternative to traditional life testing, degradation tests can be effective in assessing product reliability when measurements of degradation leading to failure can be observed. This article presents a degradation model for highly reliable light displays, such as plasma display panels and vacuum fluorescent displays (VFDs). Standard degradation models fail to capture the burn-in characteristics of VFDs, when emitted light actually increases up to a certain point in time before it decreases (or degrades) continuously. Random coefficients are used to model this phenomenon in a nonlinear way, which allows for a nonmonotonic degradation path. In many situations, the relative efficiency of the lifetime estimate is improved over the standard estimators based on transformed linear models.

Direct Prediction Methods on Lifetime Distribution of Organic Light-Emitting Diodes From Accelerated Degradation Tests

Accelerated degradation testing (ADT) expedites product degradation by stressing the product beyond its normal use. To extrapolate the products reliability at use condition, the ADT requires a known functional link relating the harsh testing environment to the usual use environment. Practitioners are often faced with a great challenge to designate an explicit form of the stress-degradation relationship a priori in accelerated degradation models. In this paper, we propose three methods to make direct inference on the lifetime distribution itself without invoking arbitrary assumptions on the degradation model: delta approximation, multiple imputation of failure-times, and the lifetime distribution-based (LDB) method. The methods are easy to implement without computational difficulty, hence they have potential in a wide range of applications for estimating lifetime distributions from ADT data. We applied the methods to two ADT data sets including a real application of commercial organic light-emitting diodes (OLED). The analysis of the examples and simulation results suggests parametric LDB and multiple imputation method as more potential alternatives to traditional failure-time approaches, especially for the case where there is neither enough physical background, nor historical evidence supporting presumed relationships between stress and the parameters of the degradation model.

Dual Features Functional Support Vector Machines for Fault Detection of Rechargeable Batteries

The early detection of faulty batteries is a critical work in the manufacturing processes of a secondary rechargeable battery. Conventional approaches use original performance degradation profiles of remaining capacity after recharge in order to detect faulty batteries. However, original degradation profiles with right-truncated test duration may not be effective in detecting faulty batteries. In this correspondence, we propose dual features functional support vector machine approach that uses both first and second derivatives of degradation profiles for early detection of faulty batteries with the reduced error rate. The modified floating search algorithm for the repeated feature selection with newly added degradation path points is presented to find a few good features for the enhanced detection while reducing the computation time for online implementation. After that, an attribute sampling plan considering time-varying classification errors is presented to determine the optimal number of test cycles and sample sizes by minimizing our proposed cost function. The real-life case study is presented to illustrate the proposed methodology and show its improved performance compared to existing approaches. The proposed method can be applied in a wide range of manufacturing processes to assess time-dependent quality characteristics.

A change-point analysis for modeling incomplete burn-in for light displays

In testing display devices such as Plasma Display Panels (PDPs), the observed degradation in luminosity can exhibit an unstable period due to incomplete burn-in during the manufacturing process. We introduce a log-linear model with random coefficients and a change point to describe the nonlinear degradation path. The change point represents the time at which the burn-in period has finished and the degradation in the luminosity changes to a slower and more stable rate. The inference procedure for the lifetime distribution is based on maximum likelihood estimators and results indicate that reliability estimation can be improved substantially by using the change-point model to account for product burn-in effects. An example based on laboratory tests of PDPs helps to illustrate the procedure.

Bi-objective scheduling for reentrant hybrid flow shop using Pareto genetic algorithm

This paper deals with a scheduling problem for reentrant hybrid flowshop with serial stages where each stage consists of identical parallel machines. In a reentrant flowshop, a job may revisit any stage several times. Local-search based Pareto genetic algorithms with Minkowski distance-based crossover operator is proposed to approximate the Pareto optimal solutions for the minimization of makespan and total tardiness in a reentrant hybrid flowshop. The Pareto genetic algorithms are compared with existing multi-objective genetic algorithm, NSGA-II in terms of the convergence to optimal solution, the diversity of solution and the dominance of solution. Experimental results show that the proposed crossover operator and local search are effective and the proposed algorithm outperforms NSGA-II by statistical analysis.

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Spherical Cubature Particle Filter

Lithium-ion batteries are critical components to provide power sources for commercial products. To ensure a high reliability of lithium-ion batteries, prognostic actions for lithium-ion batteries should be prepared. In this paper, a prognostic method is proposed to predict the remaining useful life (RUL) of lithium-ion batteries. A state-space model for the lithium-ion battery capacity is first constructed to assess capacity degradation. Then, a spherical cubature particle filter (SCPF) is introduced to solve the state-space model. The major idea of the SCPF is to adapt a spherical cubature integration-based Kalman filter to provide an importance function of a standard particle filter (PF). Once the state-space model is determined, the extrapolations of the state-space model to a specified failure threshold are performed to infer the RUL of the lithium-ion batteries. Degradation data of 26 lithium-ion battery capacities were analyzed to validate the effectiveness of the proposed prognostic method. The analytical results show that the proposed prognostic method is more effective in the prediction of RUL of lithium-ion batteries, compared with an existing PF-based prognostic method.

Degradation Analysis of Nano-Contamination in Plasma Display Panels

As an alternative to traditional life testing, degradation tests can be effective in assessing product reliability when measurements of degradation leading to failure can be observed. This article proposes a new model to describe the nonlinear degradation paths caused by nano-contamination in plasma display panels (PDP): a bi-exponential model with random coefficients. A likelihood ratio test was sequentially executed to select random effects in the nonlinear model. Analysis results indicate that the reliability estimation can be improved substantially by using the nonlinear random-coefficients model to incorporate both inherent degradation characteristics, and contamination effects of impurities for PDP degradation paths.

A Bayesian approach to modeling two-phase degradation using change-point regression

Influenced by defects or contaminants remaining after a series of manufacturing processes, the degradation paths of some products exhibit two-phase patterns over the testing period. This paper proposes a hierarchical Bayesian change-point regression model to fit the two-phase degradation patterns, and derives the failure-time distribution of a unit that is randomly selected from its population. A Gibbs sampling algorithm is developed for the inference of the parameters in the change-point degradation model, as well as for the prediction of the failure-time distribution of the randomly selected unit. The proposed approach is applied to the degradation paths of plasma display panels (PDPs) presenting the two-phase pattern.

Statistical Models for Hot Electron Degradation in Nano-Scaled MOSFET Devices

In a MOS structure, the generation of hot carrier interface states is a critical feature of the items reliability. On the nano-scale, there are problems with degradation in transconductance, shift in threshold voltage, and decrease in drain current capability. Quantum mechanics has been used to relate this decrease to degradation, and device failure. Although the lifetime, and degradation of a device are typically used to characterize its reliability, in this paper we model the distribution of hot-electron activation energies, which has appeal because it exhibits a two-point discrete mixture of logistic distributions. The logistic mixture presents computational problems that are addressed in simulation.