Inferences for Exponentiated Gamma Constant-Stress Partially Accelerated Life Test Model Based on Generalized Type-I Hybrid Censored Data

In this paper, the exponentiated gamma distribution (EGD) with generalized Type-I hybrid censored data under constant-stress partially accelerated life test (CSPALT) model is considered. The Bayesian and E-Bayesian estimation methods, as well as the maximum likelihood estimation method, are discussed for the parameter of the distribution and the acceleration factor. The E-Bayesian and Bayesian estimates are derived by using the squared error loss (SEL) and the LINEX loss functions. The MCMC method is applied for deriving the Bayesian and then E-Bayesian estimates. Moreover, a real data set is given for the illustrative purpose. After all, an evaluation is performed for the results of the proposed methods.

Reliability Modeling and Analysis of Multi-Degradation of Momentum Wheel Based on Copula Function

The momentum wheel is a key component of the satellite attitude control system and has a direct impact on the reliability and overall life of the satellite. The momentum wheel has the characteristics of a high reliability, long life, and complex failure mechanics, which leads to expensive maintenance and a low reliability of the test sample. Therefore, it is challenge to implement an accelerated life test. The traditional life data statistical method has great difficulty in solving the reliability analysis of the momentum wheel. A reliability calculation method based on copula function for multi-degradation is proposed. Firstly, the key factors affecting the reliability of the momentum wheel are analyzed, and the lubricant residual quantity and current are selected as the degradation quantity. Secondly, the wiener process is used to model the degradation of a single degradation quantity, and the edge distribution function of the momentum wheel reliability is obtained. Considering that the correlation between multiple degradation quantities has a non-negligible influence on the reliability analysis result, the copula function is introduced to describe the correlation, and the edge distributions are fused to obtain the joint distribution function of the momentum wheel reliability.

Inference for One-Shot Devices with Dependent k-Out-of-M Structured Components under Gamma Frailty

A device that performs its intended function only once is referred to as a one-shot device. Actual lifetimes of such kind of devices under test cannot be observed, and they are either left-censored or right-censored. In addition, one-shot devices often consist of multiple components that could cause the failure of the device. The components are coupled together in the manufacturing process or assembly, resulting in the failure modes possessing latent heterogeneity and dependence. In this paper, we develop an efficient expectation–maximization algorithm for determining the maximum likelihood estimates of model parameters, on the basis of one-shot device test data with multiple failure modes under a constant-stress accelerated life-test, with the dependent components having exponential lifetime distributions under gamma frailty that facilitates an easily understandable interpretation. The maximum likelihood estimate and confidence intervals for the mean lifetime of k-out-of-M structured one-shot device under normal operating conditions are also discussed. The performance of the proposed inferential methods is finally evaluated through Monte Carlo simulations. Three examples including Class-H failure modes data, mice data from ED01 experiment, and simulated data with four failure modes are used to illustrate the proposed inferential methods.

Reliability of one-shot device with generalized gamma lifetime under cyclic accelerated life-test

One-shot devices are products or equipments that can be used only once. A nature characteristic of one-shot devices is that they get destroyed immediately after their use, and therefore their actual lifetimes are never observable. The only information observed is the condition whether they worked or not at the time they are used. These days the quality of products are significantly improved, so that the information obtained under a normal test during a short time is quite limited. A typical test to induce more failures is the accelerated life-test, which is developed by increasing the stress levels under test. In this paper, we will investigate the reliability of one-shot devices with generalized gamma fatigue life under accelerated life-tests with various cyclic temperature fluctuations by assuming a Norris-Landzberg model. Generalized gamma involves many common lifetime distributions, such as gamma, Weibull, lognormal, and positive stable distributions, as special cases. Norris-Landzberg model takes not only temperature change, highest testing temperature, but also the cycling frequency into account when modeling the number of cycles-to-failure, resulting a generalized model with the well-known Coffin-Manson model and Coffin-Manson-Arrhenius model as special cases. Associated inferences are developed. The performance of the proposed model and inferential methods will be evaluated with simulation study and model discrimination. Finally, the chip-scale package solder joints data is analyzed to illustrate the considered model and inferential methods developed in this paper.

Bearing Performance Degradation Assessment Based on SC-RMI and Student’s t-HMM

Bearing performance degradation assessment (PDA), as an important part of prognostics and health management (PHM), is significant to prevent major accidents and economic losses in industry. For the data-driven PDA, the extraction and selection of features is quite important. To better integrate the degradation information, the bearing performance degradation assessment based on SC-RMI and Student’s t-HMM is proposed in this article. Firstly, spectral clustering was used as a preprocessing step to cluster features with similar degradation curves. Then, rank mutual information, which is more suitable for trendability estimation of long time series, was utilized to select the optimal feature from each cluster. The feature selection method based on these two steps is called SC-RMI for short. With the selected features, Student’s t-HMM, which is more robust to outliers, was utilized for performance degradation modeling and assessment. The verifications based on an accelerated life test and the public XJTU-SY dataset showed the superiority of the proposed method.