Lifetime Information Research Articles

Lifetime Prediction for a Cell-on-Board (COB) Light Source Based on the Adaptive Neuro-Fuzzy Inference System (ANFIS)

Predicting the lifetime of a LED lighting system is important for the implementation of design specifications and comparative analysis of the financial competition of various illuminating systems. Most lifetime information published by LED manufacturers and standardization organizations is limited to certain temperature and current values. However, as a result of different working and ambient conditions throughout the whole operating period, significant differences in lifetimes can be observed. In this article, an advanced method of lifetime prediction is proposed considering the initial task areas and the statistical characteristics of the study values obtained in the accelerated fragmentation test. This study proposes a new method to predict the lifetime of COB LED using an artificial intelligence approach and LM-80 data. Accordingly, a database with 6000 hours of LM-80 data was created using the Neuro-Fuzzy (ANFIS) algorithm, and a highly accurate lifetime prediction method was developed. This method reveals an approximate similarity of 99.8506% with the benchmark lifetime. The proposed methodology may provide a useful guideline to lifetime predictions of LED-related products which can also be adapted to different operating conditions in a shorter time compared to conventional methods. At the same time, this method can be used in the life prediction of nanosensors and can be produced with the 3D technique.

Evaluation of mean-time-to-failure based on nonlinear degradation data with applications

In reliability engineering, obtaining lifetime information for highly reliable products is a challenging problem. When a product quality characteristic whose degradation over time can be related to lifetime, then the degradation data can be used to estimate the first-passage (failure) time distribution and the Mean-Time-To-Failure (MTTF) for a given threshold level. To model the degradation data, the commonly used Lévy process modeling approach assumes that the degradation measurements are linearly related to time throughout the lifetime of the product. However, the degradation data may not be linearly related to time in practice. For this reason, trend-renewal-process-type models can be considered for degradation modeling in which a proper trend function is used to transform the degradation data so that the Lévy process approach can be applied. In this article, we study several parametric and semiparametric models and approaches to estimate the first-passage time distribution and MTTF for degradation data that may be not linearly related to time. A Monte Carlo simulation study is used to demonstrate the performance of the proposed methods. In addition, a model selection procedure is proposed to select among different models. Two numerical examples of lithium-ion battery degradation data are applied to illustrate the proposed methodologies.

Rapid and lean multifactorial screening methods for robust product lifetime improvement

Reliability enhancement is indispensable in modern operations. It aims to ensure the viability of complex functionalities in competitive products. We propose a full-robust screening/optimization method that promotes the rapid multi-factorial profiling of censored highly-fractionated lifetime datasets. The method intends to support operational conditions that demand quick, practical and economical experimentation. The innovative part of this proposal includes the robust split and quantification of structured lifetime information in terms of location and dispersion tendencies. To accomplish the robust data-reduction of lifetimes, maximum breakdown-point estimators are introduced to stabilize potential external-noise intrusions, which might be manifested as outliers or extremities. The novel solver provides resilience by robustifying the location (median) and dispersion (Rousseeuw-Croux Qn) estimations. The proposed profiler fuses dichotomized and homogenized lifetime information in a distribution-free manner. The converted and consolidated lifetime dataset is non-parametrically pre-screened to ensure error balances across effects. Consequently, any strong effects that maximize the lifetime response are diagnosed as long as the error symmetry has been previously established. We discuss problems that may be encountered in comparison to other multi-factorial profilers/optimizers upon application to densely-fractionated-and-saturated experimental schemes. We comment on the lean and agile advantages of the proposed technique with respect to several traditional treatments for the difficult case that implicates small and censored survival datasets. The robust screening procedure is illustrated on an industrial-level paradigm that concerns the multi-factorial reliability improvement of a thermostat; the trial units have been subjected to conditions of censoring and use-rate acceleration.

Lifetime performance-qualified sampling system under a Weibull distribution with failure-censoring

Reducing the warranty cost to increase profit is a major focus for most businesses. To achieve this goal, the acceptance sampling plan (ASP) can be an efficient tool to validate whether the product lifetimes meet their requirements. In the ASP practice, the single sampling plan (SSP) is widely used due to its administrative simplicity. Unfortunately, the SSP ignores the valuable sample information from preceding lots, which may reduce its efficiency and quality-discrimination power. Especially, the collection of lifetime information often consumes a significant amount of time and is expensive in today’s high product-yield and long-life environment. To address the drawbacks of the SSP, we propose a lifetime performance-qualified quick switch sampling (QSS) system under a Weibull distribution with failure-censoring. This system nimbly switches between the normal SSP and tightened SSP based on the previous lot-disposition result. Compared to the ordinary SSP and the recently proposed multiple-lot dependent sampling plan, the QSS system not only significantly reduces the required number of failures that need to be observed in the life testing but also exhibits a better discriminatory operating characteristic curve for the lot disposition. On the other hand, the QSS system helps the consumer to communicate to the supplier important messages that (i) only with reliable submissions the latter can persistently enjoy the benefits of the normal lot-sentencing standard; (ii) once the submitted lot is rejected, the supplier will have to spend more efforts to gain back the consumer trust. Moreover, we developed a web-based tool to help practitioners obtain the system criteria easily and quickly without the need for the traditional time-consuming table lookup. By operating the interactive interface of the web app, practitioners can quickly obtain the system criteria conforming to the requirements. Finally, the proposed QSS system is illustrated by a real-life application.

Profile optimum planning for degradation analysis

AbstractDegradation tests are widely used to assess the lifetime information of highly reliable products. Before conducting a degradation test, the fundamental issues in regard to decision variables are to determine how many sample sizes are needed, how long samples need to be tested, and how many measurements are taken, particularly in early experiments with limited budgets. By minimizing the approximate variance of the estimated qth quantile of the product's lifetime distribution with a cost constraint, profile optimum planning (POP) is proposed to provide a systematic solution to these decision variables under mild conditions. Based on the derived theoretical results, the use of flow charts efficiently simplifies complex optimization problems in practical applications. In addition, sensitivity analysis is studied to elucidate the effect of how the uncertainty in POP can be divided and allocated to the experimental costs and model parameters. Technical derivations, flow charts, and analysis results are available online as Supporting Information.

Single Cell Physiological Characterization in Living Tissue

The omics approach can provide single cell genomics, proteomic and lipidomics and other biological important parameters and at the level of single cell for some of these methods. However the information obtained is at one time and on a fixed tissue. Even if we had this information it is unclear what we can learn about the interactions of the different compartments in a single cell and even less in a living tissue which involves the interaction among cells. Fluorescence and tissue fluorescence has the potential to offer a comprehensive understanding of the relationship between cellular compartments in the context of a tissue. In this abstract we show new fluorescence technologies that have the potential to reach our goals. To meet this goal, we have constructed a scanning fluorescence microscope that con provide gene expression, proteomics and other parameters in living cell on selected genes and proteins. More important, on the same cells using spectral and lifetime information we can build indexes that correlates with metabolism, with protein interactions, water contentment, molecular crowding, cell to cell contacts and protein aggregation with very high spatial resolution using a STED technique for imaging.

NIR-II Excitation and NIR-I Emission Based Two-photon Fluorescence Lifetime Microscopic Imaging Using Aggregation-induced Emission Dots

Near-infrared(NIR) lights are powerful tools to conduct deep-tissue imaging since NIR-I wavelengths hold less photon absorption and NIR-II wavelengths serve low photon scattering in the biological tissues compared with visible lights. Two-photon fluorescence lifetime microscopy(2PFLM) can utilize NIR-II excitation and NIR-I emission at the same time with the assistance of a well-designed fluorescent agent. Aggregation induced emission(AIE) dyes are famous for unique optical properties and could serve a large two-photon absorption(2PA) cross-section as aggregated dots. Herein, we report two-photon fluorescence lifetime microscopic imaging with NIR-II excitation and NIR-I emission using a novel deep-red AIE dye. The AIE-gens held a 2PA cross-section as large as 1.61×104 GM at 1040 nm. Prepared AIE dots had a two-photon fluorescence peak at 790 nm and a stable lifetime of 2.2 ns under the excitation of 1040 nm femtosecond laser. The brain vessels of a living mouse were vividly reconstructed with the two-photon fluorescence lifetime information obtained by our home-made 2PFLM system. Abundant vessels as small as 3.17 µm were still observed with a nice signal-background ratio at the depth of 750 µm. Our work will inspire more insight into the improvement of the working wavelength of fluorescent agents and traditional 2PFLM.

Lifetime Estimation of Grid-Connected Battery Storage and Power Electronics Inverter Providing Primary Frequency Regulation

Battery Energy Storage Systems (BESSs) are a new asset for Primary Frequency Regulation (PFR), an ancillary service for improving the grid stability. The system operators determine the implementation and remuneration of PFR. However, assessing the revenue stream is not enough to define the business case, as also the components' lifetime has to be estimated. Previous studies of lifetime estimation for BESSs performing PFR considered only the electrochemical storage, disregarding the power electronics (PE). Nonetheless, researchers have shown the importance of estimating PE wear due to the operation when applied in renewable energy generation and microgrids. This paper presents a lifetime analysis of BESSs providing PFR considering IGBT modules, electrolytic capacitors and electrochemical storage degradation. The lifetime information is used to estimate BESS's Net-Present-Value (NPV), evaluating the benefits of deploying PE-based BESS in the European grid. A comparison between different countries, Germany, the Netherlands, and the U.K., is performed, considering the PFR implementation and remuneration differences. The analysis shows that the BESS management strategy can extend its lifetime and that the component that exhibits the shortest lifetime is the electrochemical storage. The PE components are subject to low wear due to the low power utilization and, therefore, small thermal swings while performing PFR. In conclusion, the provision of PFR by means of BESS has been found to be profitable in all three countries. However, in the Netherlands, the potential NPV has been estimated to be 47% and 76% higher than in Germany and the U.K., respectively.

A Fusion Prognostic Method for Remaining Useful Life Prediction Based on an Extended Belief Rule Base and Particle Filters

As a critical part of prognostics and health management (PHM), remaining useful life (RUL) prediction can provide manufacturers and users with system lifetime information and improve the reliability of maintainable systems. Particle filters (PFs) are powerful tools for RUL prediction because they can represent the uncertainty of results well. However, due to the lack of measurement data, the parameters of the measurement model cannot be updated during the long-term prediction process. Additionally, for complex systems, the measurement model of a system often cannot be obtained in an analytical form. In this paper, a fusion prognostic method based on an extended belief rule base (EBRB) and a PF is designed to solve these problems. In the proposed framework, a double-layer maximum mean discrepancy-extended belief rule base (DMMD-EBRB) model with time delay is adopted to estimate and predict the hidden behavior of a degrading system. The unknown parameters of the degradation model are identified by the PF using the output of the EBRB. Afterwards, the system state is further predicted by the PF. The effectiveness of the proposed method is validated with the NASA-PCoE and CALCE lithium-ion battery degradation experiment datasets. In addition, several other related fusion methods are investigated for comparison with the proposed method. The experiments show that the proposed method yields better performance than the existing methods.

Bayesian Inference for General Half-Normally Distributed Lifetime Products

The generalized half-normal (GHN) distribution has earned widely concerned in reliability analysis. Type-I hybrid censoring (HC-I) is one efficient scheme for saving test time and cost to collect lifetime information of reliable products. However, the HC-I scheme also induces a complicated likelihood function and makes the searching of maximum likelihood estimates of the model parameters difficulty. In this study, the maximum likelihood estimation and Bayesian estimation procedure are studied for the GHN distribution based on HC-I samples. The Markov chain Monte Carlo approach using the Metropolis-Hastings algorithm via Gibbs sampling is proposed to implement the Bayesian estimation procedure for obtaining the Bayes estimators of the model parameters. The Bayesian estimation procedure is more reliable than the ML estimation method due to the Bayesian estimation procedure does not use gradient methods to search the estimates of model parameters. Simulation results show that the proposed Bayesian estimation procedure perform well in terms of the bias and mean square error.

Pulsed Interleaved MINFLUX.

We introduce p-MINFLUX, a new implementation of the highly photon-efficient single-molecule localization method with a simplified experimental setup and additional fluorescence lifetime information. In contrast to the original MINFLUX implementation, p-MINFLUX uses interleaved laser pulses to deliver the doughnut-shaped excitation foci at a maximum repetition rate. Using both static and dynamic DNA origami model systems, we demonstrate the performance of p-MINFLUX for single-molecule localization nanoscopy and tracking, respectively. p-MINFLUX delivers 1-2 nm localization precision with 2000-1000 photon counts. In addition, p-MINFLUX gives access to the fluorescence lifetime enabling multiplexing and super-resolved lifetime imaging. p-MINFLUX should help to unlock the full potential of innovative single-molecule localization schemes.

Confocal Fluorescence-Lifetime Single-Molecule Localization Microscopy.

Fluorescence lifetime imaging microscopy is an important technique that adds another dimension to intensity and color acquired by conventional microscopy. In particular, it allows for multiplexing fluorescent labels that have otherwise similar spectral properties. Currently, the only super-resolution technique that is capable of recording super-resolved images with lifetime information is stimulated emission depletion microscopy. In contrast, all single-molecule localization microscopy (SMLM) techniques that employ wide-field cameras completely lack the lifetime dimension. Here, we combine fluorescence-lifetime confocal laser-scanning microscopy with SMLM for realizing single-molecule localization-based fluorescence-lifetime super-resolution imaging. Besides yielding images with a spatial resolution much beyond the diffraction limit, it determines the fluorescence lifetime of all localized molecules. We validate our technique by applying it to direct stochastic optical reconstruction microscopy and points accumulation for imaging in nanoscale topography imaging of fixed cells, and we demonstrate its multiplexing capability on samples with two different labels that differ only by fluorescence lifetime but not by their spectral properties.

Chronic Low-Dose Exposure to Xenoestrogen Ambient Air Pollutants and Breast Cancer Risk: XENAIR Protocol for a Case-Control Study Nested Within the French E3N Cohort.

BackgroundBreast cancer is the most frequent cancer in women in industrialized countries. Lifestyle and environmental factors, particularly endocrine-disrupting pollutants, have been suggested to play a role in breast cancer risk. Current epidemiological studies, although not fully consistent, suggest a positive association of breast cancer risk with exposure to several International Agency for Research on Cancer Group 1 air-pollutant carcinogens, such as particulate matter, polychlorinated biphenyls (PCB), dioxins, Benzo[a]pyrene (BaP), and cadmium. However, epidemiological studies remain scarce and inconsistent. It has been proposed that the menopausal status could modify the relationship between pollutants and breast cancer and that the association varies with hormone receptor status.ObjectiveThe XENAIR project will investigate the association of breast cancer risk (overall and by hormone receptor status) with chronic exposure to selected air pollutants, including particulate matter, nitrogen dioxide (NO2), ozone (O3), BaP, dioxins, PCB-153, and cadmium.MethodsOur research is based on a case-control study nested within the French national E3N cohort of 5222 invasive breast cancer cases identified during follow-up from 1990 to 2011, and 5222 matched controls. A questionnaire was sent to all participants to collect their lifetime residential addresses and information on indoor pollution. We will assess these exposures using complementary models of land-use regression, atmospheric dispersion, and regional chemistry-transport (CHIMERE) models, via a Geographic Information System. Associations with breast cancer risk will be modeled using conditional logistic regression models. We will also study the impact of exposure on DNA methylation and interactions with genetic polymorphisms. Appropriate statistical methods, including Bayesian modeling, principal component analysis, and cluster analysis, will be used to assess the impact of multipollutant exposure. The fraction of breast cancer cases attributable to air pollution will be estimated.ResultsThe XENAIR project will contribute to current knowledge on the health effects of air pollution and identify and understand environmental modifiable risk factors related to breast cancer risk.ConclusionsThe results will provide relevant evidence to governments and policy-makers to improve effective public health prevention strategies on air pollution. The XENAIR dataset can be used in future efforts to study the effects of exposure to air pollution associated with other chronic conditions.International Registered Report Identifier (IRRID)DERR1-10.2196/15167

Large Facet-Specific Built-in Potential Differences Affecting Trap State Densities and Carrier Lifetimes of GaAs Wafers

Previously, intrinsic GaAs {111} wafers showed appreciably better electrical conductivity than the {100} wafer and the least conductive {110} wafer. Such facet dependence can be understood from subtle variation in the bond length, bond geometry, and frontier orbital electron energy distribution for different surface planes. Attempting to gain more insights about the emergence of electrical facet effects, temperature- and voltage-varying impedance measurements were performed on these GaAs wafers using silver as the contacting electrode. Parameters from the impedance and Hall effect measurements allow the presentation of the facet-specific density and energy distribution of trap states located within the GaAs band gap. Charge carrier lifetime information can also be extracted from the impedance data. Unexpectedly, the {100} surface has the largest trap state density, while the {110} surface shows the smallest density of trap states. The {111} surface also gives the longest carrier lifetime. By contrast, the most electrically conductive {111} surface in the undoped Si and Ge wafers exhibits the lowest density of trap states and shortest carrier lifetime, which are considered as characteristics of a surface with a high-conductivity property. Facet-specific GaAs–Ag band diagrams reveal both upward and downward interfacial conduction band bending cases with positive and negative built-in potentials, which resemble n- and p-type semiconductor–metal junction situations. These irregular band diagrams may partly explain the unexpected state trap density and carrier lifetime behaviors seen in the GaAs wafers, making these parameters unsuitable to relate to their electrical conductivity properties.

Comparative evaluation of reliability assessment methods of power modules in motor drive inverter

The complete percentile lifetime models are essentially required for a mission profile based reliability evaluation of power modules. However, the information on the complete lifetime models or lifetime data of power modules is rarely fully provided by manufacturers. The Monte Carlo method is a good solution to obtain the time-dependent reliability function when only limited lifetime information such as a certain Bx lifetime is given. However, there is a lack of research on the accuracy of the reliability function obtained by the Monte Carlo analysis. In this paper, a time-dependent reliability function of the IGBT module which is obtained by Monte Carlo method from a certain Bx lifetime is compared with that estimated by the complete percentile lifetime model in the case study of motor drive inverter. The complete percentile lifetime model is developed by the use of power cycling test results of 30 IGBT modules.

Higgs portal to dark matter and Brightarrow K^{(*)} decays

We consider a Higgs portal model in which the 125-GeV Higgs boson mixes with a light singlet mediator h_2 coupling to particles of a Dark Sector and study potential brightarrow s h_2 decays in the Belle II experiment. Multiplying the gauge-dependent off-shell Standard-Model b-s-Higgs vertex with the sine of the Higgs mixing angle does not give the correct b-s-h_2 vertex. We clarify this issue by calculating the b-s-h_2 vertex in an arbitrary R_xi gauge and demonstrate how the xi dependence cancels from physical decay rates involving an on-shell or off-shell h_2. Then we revisit the brightarrow s h_2 phenomenology and point out that a simultaneous study of Brightarrow K^* h_2 and Brightarrow K h_2 helps to discriminate between the Higgs portal and alternative models of the Dark Sector. We further advocate for the use of the h_2 lifetime information contained in displaced-vertex data with h_2 decaying back to Standard-Model particles to better constrain the h_2 mass or to reveal additional h_2 decay modes into long-lived particles.

Global planning of accelerated degradation tests based on exponential dispersion degradation models

AbstractThe accelerated degradation test (ADT) is an efficient tool for assessing the lifetime information of highly reliable products. However, conducting an ADT is very expensive. Therefore, how to conduct a cost‐constrained ADT plan is a great challenging issue for reliability analysts. By taking the experimental cost into consideration, this paper proposes a semi‐analytical procedure to determine the total sample size, testing stress levels, the measurement frequencies, and the number of measurements (within a degradation path) globally under a class of exponential dispersion degradation models. The proposed method is also extended to determine the global planning of a three‐level compromise plan. The advantage of the proposed method not only provides better design insights for conducting an ADT plan, but also provides an efficient algorithm to obtain a cost‐constrained ADT plan, compared with conventional optimal plans by grid search algorithms.

Wide-Field Fluorescence Lifetime Imaging of Single Molecules.

Fluorescence lifetime imaging (FLIM) has become an important microscopy technique in bioimaging. The two most important of its applications are lifetime-multiplexing for imaging many different structures in parallel, and lifetime-based measurements of Förster resonance energy transfer. There are two principal FLIM techniques, one based on confocal-laser scanning microscopy (CLSM) and time-correlated single-photon counting (TCSPC) and the other based on wide-field microscopy and phase fluorometry. Although the first approach (CLSM-TCSPC) assures high sensitivity and allows one to detect single molecules, it is slow and has a small photon yield. The second allows, in principal, high frame rates (by 2-3 orders of magnitude faster than CLSM), but it suffers from low sensitivity, which precludes its application for single-molecule imaging. Here, we demonstrate that a novel wide-field TCSPC camera (LINCam25, Photonscore GmbH) can be successfully used for single-molecule FLIM, although its quantum yield of detection in the red spectral region is only ∼5%. This is due to the virtually absent background and readout noise of the camera, assuring high signal-to-noise ratio even at low detection efficiency. We performed single-molecule FLIM of different red fluorophores, and we use the lifetime information for successfully distinguishing between different molecular species. Finally, we demonstrate single-molecule metal-induced energy transfer (MIET) imaging which is a first step for three-dimensional single-molecule localization microscopy (SMLM) with nanometer resolution.

Scanning FCS and Superresolution Microscopy on 2D Lipid Membranes

Fluorescence Correlation Spectroscopy (FCS) is an established tool for understanding the dynamics of complex cellular processes. By applying the approach scanning FCS (sFCS), significant improvements can be obtained when studying slowly diffusing species, as is often the case in cell membranes. In sFCS, the excitation volume is scanned rapidly through the sample allowing for the simultaneous measurement of the diffusion of species at multiple locations within the sample. This significantly increases the statistical accuracy. In addition, shorter residence times of the fluorophores lead to lower photon doses experienced by each detected molecule, reducing the risk of photobleaching. This is especially important for sensitive fluorophores or when performing combined Stimulated Emission Depletion (STED) and FCS measurements. An added advantage of the scanning process is the ability to determine the observation volume without prior calibration. Here, we show results obtained with a confocal time-resolved fluorescence microscope (MicroTime 200 STED equipped with a FLIMbee galvo scanner, PicoQuant). We performed sFCS measurements on supported lipid bilayers (SLBs) which are commonly used as a simple model system for biological membranes. As the thickness of a bilayer is just a few nanometers, the diffusion properties determined with FCS or sFCS usually correspond to an average over both leaflets. We utilize the fluorescence lifetime information in order to achieve an axial nanometric localization based on Metal Induced Energy Transfer (MIET) [Karedla et al. (2014).ChemPhysChem 15(4):705-711]. The measured lifetime values enable us to separate the different molecular diffusion properties within the upper and the lower leaflet of the SLB measured on graphene.

A Prognostic Framework for Wheel Treads Integrating Parameter Correlation and Multiple Uncertainties

As crucial rotary components of high-speed trains, wheel treads in realistic operation environment usually suffer severe cyclic shocks, which damage the health status and ultimately cause safety risks. Timely and precise health prognosis based on vibration signals is an effective technology to mitigate such risks. In this work, a new parameter-related Wiener process model is proposed to capture multiple uncertainties existed in on-site prognosis of wheel treads. The proposed model establishes a quantitative relationship between degradation rate and variations, and integrates uncertainties via heterogeneity analysis of both criterions. A maximum-likelihood-based method is presented to initialize the unknown model parameters, followed by a recursive update algorithm with fully utilization of historical lifetime information. An investigation of real-world wheel tread signals demonstrates the superiority of the proposed model in accuracy improvement.