Traditional Life Tests Research Articles

Machine Learning-Guided Cycle Life Prediction for Electrochromic Devices Based on Deuterium and Water Mixing Solvent.

Electrochromic devices have demonstrated considerable potential in a range of applications, including smart windows and automotive rearview mirrors. However, traditional cycle life testing methods are time-consuming and require significant resources to process a substantial amount of generated data, which presents a significant challenge and remains an urgent issue to be addressed. To address this challenge, we proposed the use of Long Short-Term Memory (LSTM) networks to construct a prediction model of the cycle life of electrochromic devices and introduced an interpretable analysis method to further analyze the model's predictive capabilities. The original dataset used for modeling was derived from preliminary experiments conducted under 1000 cycles of six devices prepared with varying mixing ratios of heavy water (D2O). Furthermore, validation experiments confirmed the feasibility of the D2O mixing strategy, with 83% of the devices exhibiting a high initial transmittance modulation amplitude (ΔT = 43.95%), a rapid response time (tc = 7 s and tb = 8 s), and excellent cyclic stability (ΔT = 44.92% after 1000 cycles). This study is the first to use machine learning techniques to predict the cycle life of electrochromic devices while proposing performance enhancement and experimental time savings for inorganic all-liquid electrochromic devices.

Cyclic stress accelerated life test method for mechatronic products

AbstractThe reliability requirements for mechatronics continue to increase. Traditional life test methods make it difficult to obtain the data required to assess or predict the reliability of these mechatronic products within a reasonable time. Accelerated life tests can speed up the failure process of the product by using accelerated stress conditions. And then test data is obtained to predict the reliable life of the product under normal service conditions through modeling and analysis. The test efficiency of the accelerated life tests is higher than that of the life test. So, the method of accelerated life tests has become a solution for life and reliability prediction of highly reliable and long‐life mechatronic products. However, mechatronic products usually undergo cyclic stress during service. The use of cyclic stress as an accelerating stress in accelerated life tests can provide a realistic representation of service conditions and achieve higher test efficiency. Cyclic stress accelerated life test is an accurate and efficient solution for predicting the life and reliability of such mechatronic products. Current relative research is focused on constant stress, step stress and sequential stress accelerated life tests. A great challenge in engineering is modeling and analysis of cyclic stresses accelerated life tests. In this paper, we first describe the cyclic stress accelerated life test profile and its key parameters, then propose model assumptions and establish a modelling and analysis method for cyclic stress accelerated life test data, followed by an illustrative example to apply and validate the proposed method. The research results can provide technical support for the life and reliability assessment of mechatronic products under cyclic stress service conditions.

Prognostics of radiation power degradation lifetime for ultraviolet light-emitting diodes using stochastic data-driven models

With their advantages of high efficiency, long lifetime, compact size and being free of mercury, ultraviolet light-emitting diodes (UV LEDs) are widely applied in disinfection and purification, photolithography, curing and biomedical devices. However, it is challenging to assess the reliability of UV LEDs based on the traditional life test or even the accelerated life test. In this paper, radiation power degradation modeling is proposed to estimate the lifetime of UV LEDs under both constant stress and step stress degradation tests. Stochastic data-driven predictions with both Gamma process and Wiener process methods are implemented, and the degradation mechanisms occurring under different aging conditions are also analyzed. The results show that, compared to least squares regression in the IESNA TM-21 industry standard recommended by the Illuminating Engineering Society of North America (IESNA), the proposed stochastic data-driven methods can predict the lifetime with high accuracy and narrow confidence intervals, which confirms that they provide more reliable information than the IESNA TM-21 standard with greater robustness.

Time and cost constrained design of a simple step-stress accelerated life test under progressive Type-I censoring

With the engineering technology and manufacturing processes continuously improving, modern products and devices are becoming more sophisticated and reliable. On the other hand, conducting traditional life tests for these products at normal operating conditions has become almost impossible because of their extremely long lifespans. In this competing market, this is largely problematic as it could substantially delay introducing the newly developed products to the market, resulting in missed business opportunities and eventually loss of the market share. This problem is solved by accelerated life tests by subjecting the test units at higher stress levels than normal so that information on the desired lifetime parameters can be obtained more rapidly. The lifetime at the design condition is then estimated through extrapolation using an appropriate regression model. In this work, the design optimization of a simple step-stress accelerated life test under progressive Type-I censoring is investigated for assessing the reliability characteristics of a solar lighting device. Under the practical constraints that the test duration is pre-fixed for a scheduling purpose and the total experimental cost does not exceed a pre-specified budget, the design is formulated explicitly with non-uniform step durations. With the intermediate censoring taking place at the stress change time point, the existence of the optimal design is demonstrated for exponential lifetimes with a single stress variable under several design criteria including D-optimality, C-optimality, A-optimality, M-optimality, and E-optimality.

Two life prediction models for optoelectronic displays without conventional life tests.

To precisely predict the life of optoelectronic displays over a short time, two life prediction models were established based on the three-parameter Weibull right approximation method (TPWRAM). In Model I, the acceleration life under each stress was obtained using TPWRAM, the data points formed by acceleration life and acceleration stress were, respectively, fitted by three extrapolation functions and the optimal extrapolation function was determined by comparing the fitting determination coefficient and root-mean-square error. In Model II, after introducing an acceleration parameter, the luminance attenuation data under conventional stress were calculated directly by combining the ones obtained using TPWRAM under each acceleration stress. The luminance attenuation test data from the vacuum fluorescent display (VFD) were collected through four groups of constant-stress accelerated degradation tests (ADT), and the two models were applied to the life prediction of VFD. The results indicated that the designed ADT scheme was feasible, Model I revealed the changing law of life with stress and simplified the process of life prediction, and Model II made it possible to obtain the luminance attenuation formula at conventional stress without conducting a conventional life test, overcoming the shortcomings of long time-consuming traditional life tests. It was verified by comparing the life prediction values that the two models had very high precision, and both of these not only achieve the accurate estimation of optoelectronic product life without resorting to conventional life test, but also improved the method of life prediction and perfect its theoretical system.

Bayesian Methods for Planning Accelerated Repeated Measures Degradation Tests

Accelerated repeated measures degradation tests are often used to assess product or component reliability when there would be few or even no failures during a traditional life test. Such tests are used to estimate the failure-time distributions of highly reliable items in applications where it is possible to take repeated measures of some appropriate degradation measure. When engineers have valid prior information about failure mechanisms, it is important that such information be used in inference and test planning. Bayesian methods provide a vehicle for doing this. This article describes methods for selecting a Bayesian repeated measures accelerated degradation test plan when the degradation and acceleration model is linear in the parameters. A Bayesian criterion based on estimation precision of the failure-time quantile at use conditions is selected for finding optimum test plans. We use a large-sample approximation for the posterior distribution to simplify the planning criterion. The general equivalence theorem is used to check for global optimality of the optimum test plan. We also discuss how to find a compromise test plan that satisfies practical constraints while still providing good statistical properties.

Designing Degradation Experiments Using a Log-Logistic Distribution

Assessing the reliability of a product is very important to improve the product’s quality and to get the trust of customers. Degradation experiments are usually used to assess the reliability of highly reliable products,which are not expected to fail under the traditional life tests. Several decision variables, such as the sample size, the inspection frequency, and the termination time, have a direct influence on the experimental cost and the estimation precision of lifetime information. This paper deals with the optimal design of a degradation experiment where the degradation rate follows a log-logistic distribution. Under the constraint that the total experimental cost does not exceed a predetermined budget, the optimal decision variables are obtained by minimizing the mean squared error of the estimated 100pth percentile of thelifetime distribution of the product. A simulation study and a real example of drug potency data are provided to illustrate the proposed method.

Optimal Design of Life Testing for High-Brightness White LEDs Using the Six Sigma DMAIC Approach

Life testing is an essential reliability assessment procedure to qualify a new product or technology before being released to the market. High-brightness white light-emitting diodes (HBWLEDs) with high efficiency, environmental benefits, and high reliability have attracted increasing interest in the field of lighting systems. However, owing to the long lifetime of LEDs, traditional life testing methods for LEDs, which record only failure time, are time consuming and expensive. Therefore, designing an optimal life testing process for HBWLEDs is desirable for accelerating LED technology innovation and development. This paper applies the Six Sigma define–measure–analyze–improve-control (DMAIC) approach to analyze the restrictions in the traditional life testing for HBWLEDs and optimize the life testing procedure. The goal is to shorten the testing time, reduce the testing operation cost, and maintain accurate reliability estimation. In this work, a general reliability estimation method with degradation data is developed by integrating a recursive unscented Kalman filter approach to estimate HBWLED reliability. The results show that, with the help of a recursive UKF, the accuracy of reliability estimation can be improved compared with the ordinary nonlinear least squares approach. Furthermore, the operation time and cost of LED life testing can be reduced by 57.75% and 71.51%, respectively, compared with traditional life testing.

Model selection for degradation-based Bayesian reliability analysis

Traditional life testing for highly reliable products could become problematic in terms of an organization's fast product release and other competitive advantages such as market share and new technology introduction. Such issues result in the wide application of utilizing degradation data for reliability analysis and prediction for highly reliable products. The multi-unit degradation data can be either aggregated for reliability inference or modeled by mixed effect degradation models assuming given degradation path functions. This paper focuses on degradation model selection by exploring various combinations of fixed effects and random effects included in the degradation path models. The paper provides a systematic way for model selection using both statistical and empirical criteria. The model selection process is demonstrated by modeling the crack length data as a function of the number of cycles of a metal material under multiple hierarchical linear and log-linear models. These random effect models are able to capture both within-component variations due to repeated measurements over time and between-component variations due to unit-to-unit variations of the multiple sampled materials. Simulated data based on the selected hierarchical linear models are used to estimate the survival function, and the results are compared with those estimated from the non-parametric method using Kaplan–Meier estimation.

Predicting long-term lumen maintenance life of LED light sources using a particle filter-based prognostic approach

Lumen degradation is a common failure mode in LED light sources. Lumen maintenance life, defined as the time when the maintained percentages of the initial light output fall below a failure threshold, is a key characteristic for assessing the reliability of LED light sources. Owing to the long lifetime and high reliability of LED lights sources, it is challenging to estimate the lumen maintenance life for LED light sources using traditional life testing that records failure data. This paper describes a particle filter-based (PF-based) prognostic approach based on both Sequential Monte Carlo (SMC) and Bayesian techniques to predict the lumen maintenance life of LED light sources. The lumen maintenance degradation data collected from an accelerated degradation test was used to demonstrate the prediction algorithm and methodology of the proposed PF approach. Its feasibility and prediction accuracy were then validated and compared with the TM-21 standard method that was created by the Illuminating Engineering Society of North America (IESNA). Finally, a robustness study was also conducted to analyze the initialization of parameters impacting the prediction accuracy and the uncertainties of the proposed PF method. The results show that, compared to the TM-21 method, the PF approach achieves better prediction performance, with an error of less than 5% in predicting the long-term lumen maintenance life of LED light sources.

Degradation Tests Using Geometric Brownian Motion Process for Lumen Degradation Data

Running a traditional life test over an affordable time period with highly reliable products is inefficient to collect the lifetime information of products even if the products are subject to higher stress conditions. This fact makes it difficult to infer the reliability of highly reliable products. The accelerated degradation test (ADT) method has been suggested as an alternative to infer the reliability of highly reliable product based on its degradation measurements. The current study is motivated by the statistical modeling of the lumen degradation date set of transistor outline can packaged light emitting diodes (LEDs). All degradation measurements were collected from an ADT, which was conducted with two stress loadings, the ambient temperature and drive current. To study the reliability of the LEDs under the ADT, the geometric Brownian motion process and generalized Eyring model are applied to estimate the distribution parameters and percentiles of the LEDs. Planning strategies of the sample size and measurement times for the proposed ADT are established to minimize the asymptotic variance of maximum‐likelihood estimator of the lower 100pth percentile of LED lifetimes under the given budget. An algorithm is provided to reach the planning strategy. The guidelines of this study can be extended to infer the reliability of other highly reliable product besides LEDs. Copyright © 2014 John Wiley & Sons, Ltd.

Designing a Degradation Test with a Two-Parameter Exponential Lifetime Distribution

Degradation testing (DT) is a useful approach to assessing the reliability of highly reliable products which are not likely to fail under the traditional life tests or accelerated life tests. There have been a great number of excellent studies investigating the estimation of the failure time distribution and the optimal design (e.g., the optimal setting of the inspection frequency, the number of measurement, and the termination time) for DTs. However, the lifetime distributions considered in the studies mentioned above are all those without failure-free life. Here, failure-free life is characterized by a threshold parameter below which no failure is possible. The main purpose of this article is to deal with the optimal design of a DT with a two-parameter exponential lifetime distribution. More specifically, with respect to a DT where a linearized degradation model is used to model the degradation process and the lifetime is assumed to follow a two-parameter exponential distribution, under the constraint that the total experimental cost does not exceed a predetermined budget, the optimal combination of the inspection frequency, the sample size, and the termination time are determined by minimizing the mean squared error of the estimated 100p-th percentile of the lifetime distribution of the product. An example is provided to illustrate the proposed method and the corresponding sensitivity analysis is also discussed.

Optimal Design for Degradation Tests Based on Gamma Processes With Random Effects

Degradation models are usually used to provide information about the reliability of highly reliable products that are not likely to fail within a reasonable period of time under traditional life tests, or even accelerated life tests. The gamma process is a natural model for describing degradation paths, which exhibit a monotone increasing pattern, while the commonly used Wiener process is not appropriate in such a case. We discuss the problem of optimal design for degradation tests based on a gamma degradation process with random effects. To conduct a degradation experiment efficiently, several decision variables (such as the sample size, inspection frequency, and measurement numbers) need to be determined carefully. These decision variables affect not only the experimental cost, but also the precision of the estimates of lifetime parameters of interest. Under the constraint that the total experimental cost does not exceed a pre-specified budget, the optimal decision variables are found by minimizing the asymptotic variance of the estimate of the 100p-th percentile of the lifetime distribution of the product. Laser data are used to illustrate the proposed method. Moreover, we assess analytically the effects of model mis-specification that occur when the random effects are not taken into consideration in the gamma degradation model. The numerical results of these effects reveal that the impact of model mis-specification on the accuracy and precision of the prediction of percentiles of the lifetimes of products are somewhat serious for the tail probabilities. A simulation study also shows that the simulated values are quite close to the asymptotic values.

Lifetime Estimation of High-Power White LED Using Degradation-Data-Driven Method

High-power white light-emitting diodes (HPWLEDs) have attracted much attention in the lighting market. However, as one of the highly reliable electronic products which may be not likely to fail under the traditional life test or even accelerated life test, HPWLED's lifetime is difficult to estimate by using traditional reliability assessment techniques. In this paper, the degradation-data-driven method (DDDM), which is based on the general degradation path model, was used to predict the reliability of HPWLED through analyzing the lumen maintenance data collected from the IES LM-80-08 lumen maintenance test standard. The final predicted results showed that much more reliability information (e.g., mean time to failure, confidence interval, reliability function, and so on) and more accurate prediction results could be obtained by DDDM including the approximation method, the analytical method, and the two-stage method compared to the IES TM-21-11 lumen lifetime estimation method. Among all these three methods, the two-stage method produced the highest prediction accuracy.

Wear and Life Characteristics of Microwave-Sintered Copper-Graphite Composite

Copper-graphite composite is an important tribological material used in electrical sliding contact applications like electrical brushes in motors and generators. The electrical sliding contact experiences multiple stresses such as mechanical pressure and temperature. Traditional life tests under normal operating condition would be a time-consuming process due to the longer expected life of the composite. Accelerated wear testing was carried out to evaluate the life characteristics of the composite. This work focuses on evaluation of tribological performance of microwave-sintered copper-graphite composite using accelerated wear testing methodology using high temperature pin-on-disc tribometer. Microstructural studies of worn out surfaces were carried out using SEM with EDAX. Reliability and analysis on life characteristics were performed on the time-to-failure data using temperature-nonthermal-accelerated life-stress model. The obtained times-to-failure data from the accelerated wear testing was extrapolated to normal usage condition. Temperature and pressure are significantly affecting the wear performance. Self-lubricating action of graphite and improvement in wear resistance is helpful in extending the life of copper graphite composite. The life of the composite obtained through testing at mean and 99% reliability are 18,725 and 16,950 h, respectively.

Accelerated wear testing for evaluating the life characteristics of copper–graphite tribological composite

Sliding wear is a key determinant of the performance of electrical sliding contacts used in electrical machines. The behavior of the contact in sliding couple is controlled by the mutual metal transfer, friction and wear. Product life and reliability of sliding contacts are dictated by wear phenomenon. The present paper focuses on evaluation of tribological performance of copper–graphite composites using reliability theory. These composites are made up of a high electrical and thermal conductivity matrix with a solid lubricant reinforcement, making it most suitable for sliding contacts. Traditional life tests under normal operating condition would be a time consuming process due to a very long expected life of the composite. Hence, accelerated wear testing was carried out for evaluating the life characteristics. Analysis was then performed on the times-to-failure data and reliability models were developed. Life-stress relationship based on the inverse power law-Weibull model was used to make reliability predictions at normal usage level.

Predicting Long Lifetimes Using Accelerated Reliability Techniques

A continual challenge in lamp development is the need to rapidly test products that have lifetime ratings in the tens of thousands of hours. Traditional life testing, under controlled conditions, remains a mainstay test procedure. However, highly reliable statistical analysis methods allow lamp developers to estimate long lifetimes at a fraction of the testing time. These techniques usually involve the well known Weibull distribution together with historic data, multi-level accelerated stress testing, and virtual failure analysis to reduce the necessary testing times for establishing product life and reliability.

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

AbstractDegradation 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 © 2008 John Wiley & Sons, Ltd.

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.

Optimal step-stress test under type I progressive group-censoring with random removals

Some traditional life tests result in no or very few failures by the end of test. In such cases, one approach is to do life testing at higher-than-usual stress conditions in order to obtain failures quickly. This paper discusses a k-level step-stress accelerated life test under type I progressive group-censoring with random removals. An exponential failure time distribution with mean life that is a log-linear function of stress and a cumulative exposure model are considered. We derive the maximum likelihood estimators of the model parameters and establish the asymptotic properties of the estimators. We investigate four selection criteria which enable us to obtain the optimum test plans. One is to minimize the asymptotic variance of the maximum likelihood estimator of the logarithm of the mean lifetime at use-condition, and the other three criteria are to maximize the determinant, trace and the smallest eigenvalue of Fisher's information matrix. Some numerical studies are discussed to illustrate the proposed criteria.