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A generalized distribution with wide range of skewness and elongation will be suitable for the data mining and compatible for the misspecification of the distribution. Hence, the purpose of this paper is to present a distribution-based approach for estimating degradation reliability considering these conditions.

Tukey’s g-and-h distribution with the quantile expression is introduced to fit the degradation paths of the population over time. The Newton–Raphson algorithm is used to approximately evaluate the reliability. Simulation verification for parameter estimation with particle swarm optimization (PSO) is carried out. The effectiveness and validity of the proposed approach for degradation reliability is verified by the two-stage verification and the comparison with others’ work.

Simulation studies have proved the effectiveness of PSO in the parameter estimation. Two degradation datasets of GaAs laser devices and crack growth are performed by the proposed approach. The results show that it can well match the initial failure time and be more compatible than the normal distribution and the Weibull distribution.

Tukey’s g-and-h distribution is first proposed to investigate the influence of the tail and the skewness on the degradation reliability. In addition, the parameters of the Tukey’s g-and-h distribution is estimated by PSO with root-mean-square error as the object function.

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.

As an important connecting component, the reliability of aluminium alloy welded joints influences the whole structural effectiveness and stability of equipment. The purpose of this paper is to propose a novel reliability estimation approach to the welded joints based on time-transformed Wiener process with automatic image measurement of crack growth. The crack length information of the welded joints is incorporated into reliability analysis to reflect the product time-varying characteristics.

The proposed approach is superior to other crack growth estimations in that it innovatively introduce a non-contact and flexible photogrammetry technique.First, on-line crack growth images of aluminium alloy welded joints are acquired by the designed monitor system. Second, crack length is calculated with image measurement, then the crack growth data during the manufacturing process is prepared. Finally, a time-transformed Wiener process is used to modeling the degradation, and reliability estimation is carried out with Wiener model. The approach has been validated on five 7075-T7351 welded joint samples.

The method has a twofold task: first, the extraction of crack length growth data by a sequence of image processing. The main step is to model the crack skeleton with crack skeleton tree, and remove it edges to calculate the length of crack; second, the prediction of crack growth and reliability estimation.

The limitation of proposed method should not be ignored. The pixel/mm scale should be calibrated in advance that means once we have built the monitor system, the relative position of the CCD camera and the surveyed crack cannot change anymore. It has reduced the flexibility. To improve this, we can obtain binocular vision in crack image measurement. The 3-D measurements could solve calibration problem and provide more information, such as the depth and the orientation of crack to research. Therefore, future work can be centered on the improvement of monitor system and measurement precision.

In the paper a novel method to estimate reliability of crack growth from welded joint based on image measurement has been presented. This method could be widely applied in different filed of manufacturing systems, reliability engineering and structural analysis.

The purpose of this paper is the reliability analysis for systems with dependent gamma degradation process and Weibull failure time.

Consider a life testing experiment in which a sample of n devices starts to operate at t=0 and the data are available on failure time and failure-evolving process on each individual, called in some contents wear or degradation. Ignoring the between performance characteristics dependency structure may lead us to different reliability estimations, while the dependency justly exists. In previous research, dependency between the degradation process and hard failure time has been studied in limited detail (special closed form expression). Thereafter, the dependency between two degradation processes with the same structure (gamma process) in a system is considered using the copula function.

The results indicate that ignoring the dependency structure may lead us to different reliability estimations while the dependency justly exists.

This study gives some contributions that evaluate reliability metrics with more than one failure mechanism that may not be independent and possibly follow a different distribution function. The authors have used the copula function as a basis to develop a proposal model and analysis methods. In addition, the authors discussed the identifiability of the copula. Finally, simulation data were used to review the suggested approach.

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.

This paper aims to develop a computational approach to analyze the mechanical behavior, perfusion bioreactor test and degradation of the designed scaffolds. Five types of pore architecture scaffolds have been made using a computer-aided designed tool and fabricated through fused deposition modeling.

Compressive structural analysis has been performed using the finite element method to forecast the mechanical performance of the scaffolds. Also, the experimental study was done to validate the simulation outcomes. A computational fluid dynamic analysis was performed to ascertain the fluid pressure distribution, velocity profile, wall shear stress, strain rate and permeability of scaffolds. The interconnected pore architecture of the scaffolds plays a crucial role in enhancing the mechanical properties and fluid flow characteristics.

The scaffolds with continuous vertical support columns resulted in better strength because they provide better ways to transfer the load. The pore architecture of the scaffold plays a significant role in the path of fluid flow. Scaffolds with regular interconnected pore architecture showed better accessibility of the fluid. The degradation analysis showed that the degradation rate is dependent on the architecture of the scaffolds because of different surface area to volume ratios.

The simulation results provide a straightforward prediction of the scaffold suitability in terms of mechanical strength, perfusion and degradation behavior.

Online health monitoring of large complex equipment has become a trend in the field of equipment diagnostics and prognostics due to the rapid development of sensing and computing technologies. The purpose of this paper is to construct a more accurate and stable grey model based on similar information fusion to predict the real-time remaining useful life (RUL) of aircraft engines.

First, a referential database is created by applying multiple linear regressions on historical samples. Then similarity matching is conducted between the monitored engine and historical samples. After that, an information fusion grey model is applied to predict the future degradation trajectory of the monitored engine considering the latest trend of monitored sensory data and long-term trends of several similar referential samples, and the real-time RUL is obtained correspondingly.

The results of comparative analysis reveal that the proposed model, which is called similarity-based information fusion grey model (SIFGM), could provide better RUL prediction from the early degradation stage. Furthermore, SIFGM is still able to predict system failures relatively accurately when only partial information of the referential samples is available, making the method a viable choice when the historical whole life cycle data are scarce.

The prediction of SIFGM method is based on a single monotonically changing health indicator (HI) synthesized from monitoring sensory signals, which is assumed to be highly relevant to the degradation processes of the engine.

The SIFGM can be used to predict the degradation trajectories and RULs of those online condition monitoring systems with similar irreversible degradation behaviors before failure occurs, such as aircraft engines and centrifugal pumps.

This paper introduces the similarity information into traditional GM(1,1) model to make it more suitable for long-term RUL prediction and also provide a solution of similarity-based RUL prediction with limited historical whole life cycle data.

The purpose of this paper is to present a weakly supervised learning method to perform health evaluation and predict the remaining useful life (RUL) of rolling bearings.

Based on the principle that bearing health degrades with the increase of service time, a weak label qualitative pairing comparison dataset for bearing health is extracted from the original time series monitoring data of bearing. A bearing health indicator (HI) quantitative evaluation model is obtained by training the delicately designed neural network structure with bearing qualitative comparison data between different health statuses. The remaining useful life is then predicted using the bearing health evaluation model and the degradation tolerance threshold. To validate the feasibility, efficiency and superiority of the proposed method, comparison experiments are designed and carried out on a widely used bearing dataset.

The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.

The method achieves the transformation of bearing health from qualitative comparison to quantitative evaluation via a learning algorithm, which is promising in industrial equipment health evaluation and prediction.

The purpose of this paper is to analyze the time-dependent reliability of harmonic drive.

The transient finite element analysis (FEA) of harmonic drive is established to calculate the stress under different loads. Combined with the residual strength model and random variables, the time-dependent reliability model of harmonic drive is deduced by the stochastic perturbation method and Edgeworth series. Based on accelerated life tests, the degradation parameters are estimated by maximizing likelihood function. Under variable load, the key stress from transient FEA is transformed into probability density function by kernel density estimation, and the residual strength model is modified by adding adjustment factors to deal with strength degradation under different loads.

The critical position of stress concentration from transient FEA is consistent with the fatigue fracture position at the accelerated life test sample. Compared with the time-dependent reliability method with equivalent circular-shell static stress or empirical degradation parameters, the proposed method has the smallest prediction error of failure life. Under variable load, the state function should be expanded to second-order series for avoiding error items relevant to variance. The failure life expectation under random variable load is smaller than that under constant load.

The time-dependent reliability method of harmonic drive is firstly proposed under constant and variable load. The transient FEA of harmonic drive is established to calculate the stress for strength analysis. The accelerated life test of harmonic drive is conducted for degradation parameters estimation. The adjustment factor is added to the residual strength model for strength degradation under different loads.

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.