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The purpose of this paper is to highlight the need for degradation data in order to improve the reliability and the mean residual life estimation of a specific item of equipment and to adapt the preventive maintenance tasks accordingly.

An initial reliability model which uses a degradation-based reliability model that is built from the collection of hitting times of a failure threshold. The proposed maintenance model is based on the cost/availability criterion. The estimation of both reliability and optimum time for preventive maintenance are updated with all new degradation data that are collected during operating time.

An improvement for the occurrences of maintenance tasks which minimizes the mean cost per unit of time and increases the availability.

Inspection tasks to measure the degradation level should be realized at least one time for each item of equipment at a specific time determined by the proposed methodology.

The introduction of a criterion which helps the maintainer to decide to postpone or not the preventive replacement time depending on the measured degradation level of a specific item of equipment.

The purpose of this paper is to develop a risk‐based integrity model for the optimal replacement of offshore process components, based on the likelihood and consequence of failure arising from time‐dependent degradation mechanisms.

Risk is a combination of the probability of failure and its likely consequences. Offshore process component degradation mechanisms are modeled using Bayesian prior‐posterior analysis. The failure consequences are developed in terms of the cost incurred as a result of failure, inspection and maintenance. By combining the cumulative posterior probability of failure and the equivalent cost of degradations, the operational life‐risk curve is produced. The optimal replacement strategy is obtained as the global minimum of the operational risk curve.

The offshore process component degradation mechanisms are random processes. The proposed risk‐based integrity model can be used to model these processes effectively to obtain an optimal replacement strategy. Bayesian analysis can be used to model the uncertainty in the degradation data. The Bayesian posterior estimation using an M‐H algorithm converged to satisfactory results using 10,000 simulations. The computed operational risk curve is observed to be a convex function of the service life. Furthermore, it is observed that the application of this model will reduce the risk of operation close to an ALARP level and consequently will promote the safety of operation.

The developed model is applicable to offshore process components which suffer time‐dependent stochastic degradation mechanisms. Furthermore, this model is developed based on an assumption that the component degradation processes are independent. In reality, the degradation processes may not be independent.

The developed methodology and models will assist asset integrity engineers/managers in estimating optimal replacement intervals for offshore process components. This can reduce operating costs and resources required for inspection and maintenance (IM) tasks.

The frequent replacement of offshore process components involves higher cost and risk. Similarly, the late replacement of components may result in failure and costly breakdown maintenance. The developed model estimates an optimal replacement strategy for offshore process components suffering stochastic degradation. Implementation of the developed model improves component integrity, increases safety, reduces potential shutdown and reduces operational cost.

The purpose of constructing a degradation index (DI) is to better characterize the degradation degree of mechanical transmission compared with relying solely on spectral oil data, which leads to an accurate estimation of the failure time when the transmission no longer fulfills its function.

The DI is modeled using a weighted average function with two desirable properties: maximizing the monotonic trend and minimizing the variance of failure threshold between different transmissions. The method includes concentration modification, data selection and data fusion steps that lead to a reasonable mechanical transmission degradation model. The proposed methodology was verified through a case study involving multispectral oil data sampled from several power-shift steering transmissions.

The results show that the DI outperforms all spectral oil data. Compared with the existing spectral oil data-based degradation modeling approach for mechanical transmissions, the present methodology provides an accurate RUL prediction.

There are several important directions for future research: First, more degradation data (i.e. ferrography) that are tailored to the degradation modeling of mechanical transmission need to be involved. Second, more effective degradation data selection methodologies that are applicable for multiple data types need to be developed. Third, kernel methods that can fuse the nonlinear degradation data need to be investigated.

The novelty of this methodology lies in integrating the multiple degradation data in a unified DI. And the main contribution of this paper is to establish a new direction in degradation modeling and RUL prediction of mechanical transmission.

As the physical dimensions of the devices are reduced to the submicrometer regime, the hot‐carrier reliability has become an important issue in the scaling of the p‐MOSFET as well as the n‐MOSFET. In this paper, we present a unified approach for p‐MOSFET degradation due to the trapping of the hot electrons in the gate oxide layers. A physical analytical model, based on the pseudo two‐dimensional model, is derived for the first time to describe the linear and saturation drain current degradation. The model has been verified by comparing the calculation and the measurement from submicron p‐MOSFET's with different channel lengths and oxide thickness. There are no empirical parameters in the model. Two physical parameters: the capture cross section and the density of states of electron traps, which can be determined independently from the measured degradation characteristics, are valid for both the linear current and the saturation current degradation. The simple expression is very suitable for the predicting of the circuit reliability.

The purpose of this paper is to analyze the dependent competing failure reliability of harmonic drive (HD) with strength failure and degradation failure.

Based on life tests and stiffness degradation experiments, Wiener process is used to establish the accelerated performance degradation model of HD. Model parameter distribution is estimated by Bayesian inference and Markov Chain Monte Carlo (MCMC) and stiffness degradation failure samples are obtained by a three-step sampling method. Combined with strength failure samples of HD, copula function is used to describe the dependence between strength failure and stiffness degradation failure.

Strength failure occurred earlier than degradation failure under high level accelerated condition; degradation failure occurred earlier than strength failure under medium- or low-level accelerated condition. Gumbel copula is the optimum copula function for dependence modeling of strength failure and stiffness degradation failure. Dependent competing failure reliability of HD is larger than independent competing failure reliability.

The reliability evaluation method of dependent competing failure of HD with strength failure and degradation failure is first proposed. Performance degradation experiments during accelerated life test (ALT), step-down ALT and life test under rated condition are conducted for Wiener process based step-down accelerated performance degradation modeling.

This paper aims to develop a dynamic civil facility degradation prediction model to forecast the reliability performance tendency and remaining useful life under imperfect maintenance based on the inspection records and the maintenance actions.

A real-time hidden Markov chain (HMM) model is proposed in this paper to predict the reliability performance tendency and remaining useful life under imperfect maintenance based on rare failure events. The model assumes a Poisson arrival pattern for facility failure events occurrence. HMM is further adopted to establish the transmission probabilities among stages. Finally, the simulation inference is conducted using Particle filter (PF) to estimate the most probable model parameters. Water seals at the spillway hydraulic gate in a Taiwan's reservoir are used to examine the appropriateness of the approach.

The results of defect probabilities tendency from the real-time HMM model are highly consistent with the real defect trend pattern of civil facilities. The proposed facility degradation prediction model can provide the maintenance division with early warning of potential failure to establish a proper proactive maintenance plan, even under the condition of rare defects.

This model is a new method of civil facility degradation prediction under imperfect maintenance, even with rare failure events. It overcomes several limitations of classical failure pattern prediction approaches and can reliably simulate the occurrence of rare defects under imperfect maintenance and the effect of inspection reliability caused by human error. Based on the degradation trend pattern prediction, effective maintenance management plans can be practically implemented to minimize the frequency of the occurrence and the consequence of civil facility failures.

The main objective of this work is to assess the current capabilities of different commercial finite element (FE) codes in simulating the progressive damage of composite structures under quasi-static loading condition in post-buckling regime.

Progressive failure analysis (PFA) methodologies, available in the investigated FE codes, were applied to a simple test case extracted from literature consisting in a holed composite plate loaded in compression.

Results of the simulations are significantly affected by the characteristic parameters needed to feed the degradation models implemented in each code. Such parameters, which often do not have a physical meaning, have to be necessarily set upon fitting activity with an experimental database at coupon level. Concerning the test case, all the codes were found able to capture the buckling load and the failure load with a good accuracy.

This paper would to give an insight into the PFA capabilities of different FE codes, providing the guidelines for setting the degradation model parameters which are of major interest.

Reports that measurable amounts of polymer degradation occur during the fabrication of objects from polymer coated ceramic powders by selective laser sintering (SLS). Argues that because the binder is important in achieving strong green parts that can be handled with minimal breakage during post‐processing operations, it is essential to minimize the extent of binder losses. As the first step towards understanding the mechanisms of binder degradation, this paper presents a thermal model of the physical system, noting that the agreement between theory and experiment are good. The model is used to help determine the most influential parameters affecting binder losses during fabrication from polymer coated powders. Predicts that adjustments to laser beam diameter, laser scanning distance and gaseous environment will strongly affect polymer binder degradation during processing. Further predicts correctly that polymer degradation during SLS processing is not sensitive to the inherent degradation kinetics of the polymer.

In some environments, the failure rate of a system depends not only on time but also on the system condition, such as vibrational level, efficiency and the number of random shocks, each of which causes failure. In this situation, systems can keep working, though they fail gradually. So, the purpose of this paper is modeling multi-state system reliability analysis in capacitor bank under fatal and nonfatal shocks by a simulation approach.

In some situations, there may be several levels of failure where the system performance diminishes gradually. However, if the level of failure is beyond a certain threshold, the system may stop working. Transition from one faulty stage to the next can lead the system to more rapid degradation. Thus, in failure analysis, the authors need to consider the transition rate from these stages in order to model the failure process.

This study aims to perform multi-state system reliability analysis in energy storage facilities of SAIPA Corporation. This is performed to extract a predictive model for failure behavior as well as to analyze the effect of shocks on deterioration. The results indicate that the reliability of the system improved by 6%.

The results of this study can provide more confidence for critical system designers who are engaged on the proper system performance beyond economic design.

With the rapid development and stable operated application of lithium-ion batteries used in uninterruptible power supply (UPS), the prediction of remaining useful life (RUL) for lithium-ion battery played an important role. More and more researchers paid more attentions on the reliability and safety for lithium-ion batteries based on prediction of RUL. The purpose of this paper is to predict the life of lithium-ion battery based on auto regression and particle filter method.

In this paper, a simple and effective RUL prediction method based on the combination method of auto-regression (AR) time-series model and particle filter (PF) was proposed for lithium-ion battery. The proposed method deformed the double-exponential empirical degradation model and reduced the number of parameters for such model to improve the efficiency of training. By using the PF algorithm to track the process of lithium-ion battery capacity decline and modified observations of the state space equations, the proposed PF + AR model fully considered the declined process of batteries to meet more accurate prediction of RUL.

Experiments on CALCE dataset have fully compared the conventional PF algorithm and the AR + PF algorithm both on original exponential empirical degradation model and the deformed double-exponential one. Experimental results have shown that the proposed PF + AR method improved the prediction accuracy, decreases the error rate and reduces the uncertainty ranges of RUL, which was more suitable for the deformed double-exponential empirical degradation model.

In the running of UPS device based on lithium-ion battery, the proposed AR + PF combination algorithm will quickly, accurately and robustly predict the RUL of lithium-ion batteries, which had a strong application value in the stable operation of laboratory and other application scenarios.