Lifetime Distribution Model Research Articles

Lifetime Distribution for a Mixed Redundant System with Imperfect Switch and Components Having Phase–Type Time-to-Failure Distribution

Recently, a mixed redundancy was introduced among the redundant design strategies to achieve a more reliable system within the equivalent resources. This study deals with a lifetime distribution for a mixed redundant system with an imperfect fault detector/switch. The lifetime distribution model was formulated using a structured continuous Markov chain (CTMC) and considers the time-to-failure (TTF) distribution of a component as a phase-type distribution (PHD). The model’s versatility and practicality are enhanced because the PHD can represent diverse degradation patterns of the components exposed to varied operating environments. The model provides accurate reliability for a mixed redundant system by advancing the approximate reliability function suggested in previous studies. Furthermore, the model would be useful in system design and management because it provides information such as the nth moment of the system’s lifetime distribution. In numerical experiments on some examples, the mixed redundancy was confirmed to devise a more reliable system than the existing active and standby redundancies, and the improvement effect increased as the number of redundant components decreased. The optimal structure for maximizing the expected lifetime of the system changes depends on the reliability of the components and fault detector/switch.

FMECA and Failure Data Analysis of Domestic Wheeled Walking-Aid

The gradual acceleration of the aging population in China has led to an increased demand for mobility aids, and the reliability of domestic wheeled walking-aid is one of their important attributes, but there is little research on the reliability of mobility aids. This paper conducts the failure mode, effects and criticality analysis on domestic wheeled walking-aid. By collecting, collating and analyzing the 26 failure modes, the key modules are the chassis and the lifting system. The key modules are obtained, and the failure data is analyzed to find out that the lifetime distribution model is log-normal and the 0.95 lower confidence limit for reliability life of product t 0.9 is 2489.4 hours. The study aims to provide ideas for the reliability analysis of other active medical devices and calls for the formation of a reliability study review point for the industry that meets the requirements of Chinese medical device regulations.

COMPOUND RAYLEIGH LIFETIME DISTRIBUTION-I

This paper provides a new lifetime distribution model named as Compound Rayleigh Lifetime Distribution -I which is derived from Rayleigh distribution by imposing the restriction on the distribution of its parameter towards obtaining the properties of fine tuning manoeuvres and shift process in mean which is useful in reliability and statistical quality control analysis. The theoretical results obtained are illustrated with appropriate practical example.

Reliability Estimation for Stress-Strength Model Based on Unit-Half-Normal Distribution

Many lifetime distribution models have successfully served as population models for risk analysis and reliability mechanisms. We propose a novel estimation procedure of stress–strength reliability in the case of two independent unit-half-normal distributions can fit asymmetrical data with either positive or negative skew, with different shape parameters. We obtain the maximum likelihood estimator of the reliability, its asymptotic distribution, and exact and asymptotic confidence intervals. In addition, confidence intervals of model parameters are constructed by using bootstrap techniques. We study the performance of the estimators based on Monte Carlo simulations, the mean squared error, average bias and length, and coverage probabilities. Finally, we apply the proposed reliability model in data analysis of burr measurements on the iron sheets.

A new class of lifetime distribution with decreasing failure rate: Properties and applications

The exponential model is the simplest among all lifetime distribution models and it possesses a constant failure rate. Here we propose a new class of lifetime distribution having decreasing failure rate which we developed through compounding exponential distribution with the positive hyper-Poisson distribution. We investigate some of its statistical properties and employed various methods of estimation for estimating the parameters of the distribution along with certain test procedures. All the procedures discussed in the paper are illustrated with the help of real-life data sets. Further, a brief simulation study is conducted for examining the performance of the estimators of the parameters of the distribution.

A Reliability Evaluation of Marine Power Electronic Devices Based on Performance Degradation Data and a Competitive Failure Model

Pan, G.; Li, X.; Luo, Q.; Li, D., and Zhang, Z., 2020. A reliability evaluation of marine power electronic devices based on performance degradation data and a competitive failure model. In: Hu, C. and Cai, M. (eds.), Geo-informatics and Oceanography. Journal of Coastal Research, Special Issue No. 105, pp. 67–70. Coconut Creek (Florida), ISSN 0749-0208.This paper proposes a reliability evaluation method for marine power electronic (MPE) devices that considers the characteristics of high reliability, long life, and multi–failure-mode competition. Performance degradation data are used to construct performance degradation and lifetime distribution models of MPE devices, and methods of parameter estimation, goodness-of-fit testing, and optimization are proposed to solve them. By considering the coupled competitive relationships of multiple performance parameters, a competitive failure model is proposed to rapidly evaluate the reliability of MPE devices. The Copula function and Monte Carlo method are used to solve the model. As an example, reliability evaluation is carried out for a marine insulated-gate bipolar transistor. The error between the evaluation result of the proposed method and an actual statistical case is demonstrated to be small, which verifies that the proposed method is accurate and effective.

BDS B1I multipath channel statistical model comparison between static and dynamic scenarios in dense urban canyon environment

Global Navigation Satellite System (GNSS) multipath channel models are fundamental and critical for signal simulation and receiver performance evaluation. They also aid the designing of suitable multipath error mitigation algorithms when the properties of multipath channel are available. However, there is insufficient existing research on BeiDou Navigation Satellite System (BDS) signal multipath channel models. In this study, multipath channel statistical models are established on the basis of extensive datasets of the BDS B1I signal. A multipath parameter estimation algorithm is designed to extract information of multipath rays from the intermediate frequency data. The delay, power loss, Doppler fading frequency, and lifetime distribution models for static and dynamic vehicle platforms are established and compared, and the effects of the satellite orbit type and platform speed on the models are analyzed. The results reveal the detailed distribution and variation characteristics of the multipath parameters and are valuable for the development of accurate urban navigation systems.

Priori information analysis of optocoupler accelerated degradation experiment based on failure mechanism verification test

The optocoupler is a weak link in the inertial navigation platform of a kind of guided munitions. It is necessary to use accelerated storage test to verify the storage life of long storage products. Especially for small sample products, it is very important to obtain prior information for the design and implementation of accelerated degradation test. In this paper, the optocoupler failure mechanism verification test is designed and the experimental results are analyzed and the prior information is obtained. The results show that optocouplers have two failure modes, one is sudden failure and the other is degradation failure; the maximum temperature stress of optocoupler can't exceed 140 °C; the increase of leakage current of optocoupler is caused by movable ions contaminating the LED chip. The surface leakage current is proportional to the adsorption amount. The increase of leakage current makes p-n junction tunneling effect occur which LEDs the failure of the optocoupler. The lifetime distribution model of the optocoupler is determined by the failure physics. The lifetime of the optocoupler is subject to the lognormal distribution. The degeneracy orbit of the optocoupler leakage current is described by a power law model. The estimated values of the orbital parameters are initially calculated and the parameters of its life distribution function are deduced. The above information lays a good foundation for the optimization design and data processing of the accelerated degradation experiment.

Survival Analysis of Plants Grown in Long‐Term Field Experiments

Core Ideas The probability distribution of plant survival was presented. A new HS‐II Mielniczuk lifetime distribution model was presented for the first time. Smith–Bain distribution was the optimal model for willow density of 24,000 plants ha−1. The HS‐II Mielniczuk was the optimal model for willow densities of 48,000 and 96,000 ha−1. Plant survival was evaluated in a statistical analysis. The analysis was conducted based on data from a long‐term field experiment where five willow (Salix L.) genotypes were grown at four densities (12,000, 24,000, 48,000, and 96,000 ha−1) in a plantation with an annual harvesting cycle. Four exponential power distributions that best fit plant survival data were selected, including an alternative parametric distribution, which is presented for the first time in this study. The developed models supported predictions of the future distribution of willow plants in groups characterized by different planting density, willow cultivars, and clones. At the recommended density of 24,000 plants ha−1, the exponential power distribution proposed by Smith and Bain best fit willow survival time and the plantation’s productive lifespan. The new hyperbolic secant distribution was characterized by the highest explanatory power measured by Akaike information criterion, Schwarz’s Bayesian information criterion, and a heavy‐tailed distribution. This model was optimal for higher planting densities (48,000 and 96,000 ha−1) with significantly lower plant survival rates.

Reliability and Lifetime Distribution Analysis of Belt Drive Systems considering Time‐Dependent Stiffness of Belts

Time‐dependent reliability models, failure rate models, and lifetime distribution models of belt drive systems are developed in this paper, which take geometric parameters, material parameters, and motion parameters as the input and consider the dynamic properties of the belt drive systems. Most of the reliability models of belt drive systems are static models. The proposed models can take into account the time‐dependent statistical properties of dynamic stress process. Moreover, the stiffness degradation is considered in the established models, whose influences on reliability indices are analyzed in case study. The results show that stiffness degradation has significant impacts on reliability, failure rate, and lifetime distribution. In addition, sensitivity models of reliability with respect to input parameters are constructed, which can be used for the guidance of safe design of belt drive systems.

Forecasting time-to-failure of machine using hybrid Neuro-genetic algorithm – a case study in mining machinery

Forecasting of time-to-failure is an important aspect of a mining machine for the performance assessment, fault detection and schedule maintenance. The knowledge of failure time allows more defined arrangement of preventive maintenance. Traditional methods, including lifetime distribution models, fault tree analysis and Markov models, have a limitation of assuming a specific statistical distribution function to fit the failure time data. In this study, a hybrid data-driven method using neural network and genetic algorithm is proposed to forecast failure time. The forecasting model was developed using neural network algorithm and all the neural network parameters, i.e. input nodes, hidden nodes and the learning algorithms, are selected automatically using the genetic algorithm. The developed model was validated using the failure data of a mining machine. A case study was conducted investigating a load-haul-dump machine (LHD) in the mining industry. Failure historical data for the LHD machine were collected, and cumulative failure time was calculated for time-to-failure forecasting. Study results demonstrate that the developed model performs satisfactory in the prediction of next failure time. A comparative study reveals that the proposed method performs better than existing methods.

Inference on the Kumaraswamy distribution

Many lifetime distribution models have successfully served as population models for risk analysis and reliability mechanisms. The Kumaraswamy distribution is one of these distributions which is particularly useful to many natural phenomena whose outcomes have lower and upper bounds or bounded outcomes in the biomedical and epidemiological research. This article studies point estimation and interval estimation for the Kumaraswamy distribution. The inverse estimators (IEs) for the parameters of the Kumaraswamy distribution are derived. Numerical comparisons with maximum likelihood estimation and biased-corrected methods clearly indicate the proposed IEs are promising. Confidence intervals for the parameters and reliability characteristics of interest are constructed using pivotal or generalized pivotal quantities. Then, the results are extended to the stress–strength model involving two Kumaraswamy populations with different parameter values. Construction of confidence intervals for the stress–strength reliability is derived. Extensive simulations are used to demonstrate the performance of confidence intervals constructed using generalized pivotal quantities.

Remaining useful life prediction of rotating equipment using covariate-based hazard models – Industry applications

The ability to estimate the expected remaining useful life (RUL) is critical to reduce maintenance costs, operational downtime and safety hazards. In most industries, reliability analysis is based on the reliability-centred maintenance and lifetime distribution models. In these models, the lifetime of an asset is estimated using failure time data; however, statistically sufficient failure time data are often difficult to attain in practice due to the fixed time-based replacement and the small population of identical assets. When condition indicator data are available in addition to failure time data, one of the alternate approaches to the traditional reliability models is the condition-based maintenance (CBM). The covariate-based hazard modelling is one of CBM approaches. There are a number of covariate-based hazard models; however, little study has been conducted to evaluate the performance of these models in asset life prediction using various condition indicators and data availability. This paper reviews two covariate-based hazard models, proportional hazard model (PHM) and proportional covariate model (PCM). To assess these models’ performance, the expected RUL is compared to the actual RUL. Outcomes demonstrate that both models achieve convincingly good results in RUL prediction; however, PCM has smaller absolute prediction error. In addition, PHM shows over-smoothing tendency compared to PCM in sudden changes of condition data. Moreover, the case studies show PCM is not being biased in the case of small sample size.

Performance comparison of box-cox transformation and weighted variance methods with weibull distribution

AbstractA standard process capability index is calculated based on the assumption that the quality characteristic of the process follows the normal distribution. But there are many cases in which the quality characteristic comes from a non-normal distribution. This paper studies Box-Cox transformation method and Weighted Variance method to calculate process capability indices for Weibull distributed quality characteristic and compares performances of these methods. Weibull distribution is extensively used as a lifetime distribution model because of its flexible shape. The data sets used in performance comparison are randomly generated from Weibull distribution for two different shape and scale parameters through a simulation study. The results indicate that Box-Cox transformation method produces better estimates for process capability than Weighted Variance method.

A New Finite Interval Lifetime Distribution Model for Fitting Bathtub-Shaped Failure Rate Curve

This paper raised a new four-parameter fitting model to describe bathtub curve, which is widely used in research on components’ life analysis, then gave explanation of model parameters, and provided parameter estimation method as well as application examples utilizing some well-known lifetime data. By comparative analysis between the new model and some existing bathtub curve fitting model, we can find that the new fitting model is very convenient and its parameters are clear; moreover, this model is of universal applicability which is not only suitable for bathtub-shaped failure rate curves but also applicable for the constant, increasing, and decreasing failure rate curves.

Finite weakest-link model of lifetime distribution of quasibrittle structures under fatigue loading

The design of various engineering structures, such as buildings, infrastructure, aircraft, ships, as well as microelectronic components and medical implants, must ensure an extremely low probability of failure during their service lifetime. Since such a low probability is beyond the means of histogram testing, we must rely on some physically based probabilistic model for the statistics of structural lifetime. Attention is focused on structures consisting of quasibrittle materials. These are brittle materials with inhomogeneities that are not negligible compared with the structure size, as exemplified by concrete, fiber composites, tough ceramics, rocks, sea ice, bone, wood, and many more at the micro- or nano-scale. This paper presents a finite weakest-link model of the fatigue lifetime of quasibrittle structures that fail at the fracture of one representative volume element (RVE). In this model, the probability distribution of critical stress amplitude is first derived by assuming a prescribed number of loading cycles and a fixed stress ratio. The probability distribution of fatigue lifetime is then deduced from the probability distribution of critical stress amplitude through the Paris law for fatigue crack growth. It is shown that the present theory matches well with the experimentally measured lifetime histograms of various engineering and dental ceramics, which systematically deviate from the two-parameter Weibull distribution. The theory indicates that the mean fatigue lifetime of quasibrittle structures must strongly depend on the structure size and geometry. Finally, the present model indicates that the probability distribution of fatigue lifetime can be determined from the mean size effect analysis.

Lifetime Estimation of a Photovoltaic Module Subjected to Corrosion Due to Damp Heat Testing

In this paper, a methodology is presented for estimating the lifetime of a photovoltaic (PV) module. Designers guarantee an acceptable level of power (80% of the initial power) up to 25 yr for solar panels without having sufficient feedback to validate this lifetime. Accelerated life testing (ALT) can be carried out in order to determine the lifetime of the equipment. Severe conditions are used to accelerate the ageing of components and the reliability is then deduced in normal conditions, which are considered to be stochastic rather than constant. Environmental conditions at normal operations are simulated using IEC 61725 standard and meteorological data. The mean lifetime of a crystalline-silicon photovoltaic module that meets the minimum power requirement is estimated. The main results show the influence of lifetime distribution and Peck model parameters on the estimation of the lifetime of a photovoltaic module.

Distance-Dependent Metal-Enhanced Intrinsic Fluorescence of Proteins Using Polyelectrolyte Layer-by-Layer Assembly and Aluminum Nanoparticles.

Previously reported studies indicate that aluminum nanostructured substrates can potentially find widespread use in metal-enhanced fluorescence (MEF) applications particularly in the UV or near-UV spectral region toward label-free detection of biomolecules. MEF largely depends on several factors, such as chemical nature, size, shape of the nanostructure and its distance from the fluorophore. A detailed understanding of the MEF and its distance-dependence are important for its potential application in biomedical sensing. Our goal is to utilize intrinsic protein fluorescence for label-free binding assays. This is made possible by the use of metallic nanostructures which provide localized excitation and enhanced fluorescence of UV fluorophores and will also provide a way to separate the surface-bound proteins from the bulk samples. We evaluated varied probe distances from plasmonic nanostructures by the well-established layer-by-layer (LbL) technique. The investigated proteins were adsorbed on different numbers of alternate layers of poly(styrene sulfonate) (PSS) and poly(allylamine hydrochloride) (PAH). Bovine serum albumin (BSA) was electrostatically attached to the positively charged PAH layer, and goat and rabbit IgG were attached to negatively charged PSS layer. We obtained a maximum of a ~ 9 fold increase in fluorescence intensity from BSA at a distance of ~9 nm from the Al nanostructured surface. Approximately 6- and 7- fold increases were observed from goat and rabbit IgG at a distance of ~8 nm, respectively. The minimum lifetimes were about 3-fold shorter than those on bare control quartz slides for all three proteins. The time-resolved intensity decays were analyzed with a lifetime distribution model to understand the distance effect on the metal-fluorophore interaction in detail. The present study indicates the distance dependence nature of metal-enhanced intrinsic fluorescence of proteins and potential of LbL assembly to control the metal-to-fluorophore distance in the UV wavelength region.

A finite weakest-link model of lifetime distribution of high- k gate dielectrics under unipolar AC voltage stress

This paper presents a new probability distribution function for the breakdown lifetime of high- k gate dielectrics under unipolar AC voltage stress. This function is derived from a finite weakest-link model, where the gate oxide layer is considered to consist of many potential breakdown cells. Each potential breakdown cell is modeled as a series coupling of several subcells, which is analogous to the fiber-bundle model for the strength statistics of structures. The present model indicates that the type of lifetime distribution varies with the gate area and the dependence of the mean lifetime on the gate area deviates from the classical Weibull scaling law. It is shown that the model agrees well with the observed lifetime histograms of HfO 2 based gate dielectrics under unipolar AC voltage stress.

Lifetime Distribution Model of Port Facilities with Pitting Corrosion of Stochastic Processes

Pitting corrosion damages seriously the structure strength of port facilities. To estimate the lifetime of port facilities, a new lifetime distribution model of port facilities with pitting corrosion of stochastic processes is proposed. Pitting corrosion can be considered as a combination of two physical processes: pit initiation process and pit growth process. The pit generation process is modeled as a non homogeneous Poisson process. The pit growth process is represented by a gamma process. A lifetime distribution model is derived based on the combined stochastic processes. Finally the proposed models are tested and simulated. The results indicate that the proposed model is feasible and valid for estimating the lifetime of port facilities.