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This study addresses the problem of performance reliability degradation and lifetime prediction in complex systems with high reliability but insufficient information. It comprehensively considers the effects of various uncertainties, such as measurement, cognitive, and stochastic uncertainties, and proposes a generalized normal grey cloud Bayesian Wiener process (GNGCBWP) stochastic degradation model. First, based on the historical degradation information of the system, the interval grey number sequence of the degradation increment is constructed, and the performance degradation path of the system is analyzed to determine the degradation model type. Then, combining the normal grey cloud and Bayesian estimation theory, the relevant definitions and theorems of generalized normal grey cloud Bayesian estimation (GNGCBE) are proposed. Finally, the parameter estimation results obtained using the GNGCBE method are substituted into the grey Wiener process degradation model to dynamically analyze performance reliability degradation and predict the remaining useful life (RUL) in real‐time. And the validity and scientificity of the proposed model is verified through a case study of laser equipment. Moreover, the comparison results of the models indicate that the proposed model has advantages in dynamic analysis of performance reliability degradation and real‐time RUL prediction for highly reliable complex systems with limited or poor degradation information. It can significantly reduce the impact of multiple uncertainties on the analysis results and improve the accuracy and reliability of the final prediction results. [ABSTRACT FROM AUTHOR]

We consider degrading engineering systems monitored by degrading sensors. Since accurate information is crucial for predicting system health condition and the subsequent decision-making, considering the effect of sensor degradation is highly important to determine the justified reliability characteristics of systems such as the remaining useful life (RUL). Although the concept of sensor degradation has been introduced previously in the literature, the remaining useful life estimation in this case or parameter estimation in the presence of sensor degradation has not been studied in detail. To fill the gap, this study aims to estimate the RUL of a system that is continuously monitored by a degrading sensor. In this work, to distinguish sensor degradation from that of the main system, an additional calibration sensor is used to accurately inspect the system health condition at certain points of time. Subsequently, maximum-a-posteriori estimation technique is employed to estimate the parameters for the system degradation process and maximum likelihood estimation is used to estimate the parameters of sensor degradation. A Kalman filter is then used to estimate the system and sensor states, followed by system RUL evaluation. A numerical example with simulated data is employed to illustrate the effectiveness of the proposed method. It is shown through the numerical study that neglecting sensor degradation can result in significant errors in RUL estimation, which can further impact the subsequent maintenance decisions. • We evaluate the remaining life of degrading systems monitored by degrading sensors. • We estimate the parameters of the degradation process for the sensor and system. • We estimate the system states with Kalman filter. • We highlight the importance of sensor degradation in system reliability evaluation. [ABSTRACT FROM AUTHOR]

Complex characteristics such as non-linearity and multi-stage are usually presented during the degradation process of aluminum electrolytic capacitors (AECs). Therefore, it is difficult to accurately predict the capacitors' remaining useful life (RUL), which is significant to the system's reliability. Based on the model of Wiener process with linear drift, a simple two-stage RUL predicting method for AECs is proposed in this paper. In the offline stage, the Box–Cox transformation (BCT) is first utilized to linearize the degraded data. Then the model parameters of BCT are identified, and the Shapiro–Wilk test is employed to verify whether the transformed data meet the linearity. In the online stage, based on similarity measurement, the drift coefficient of the Wiener process is updated in real-time. By combining BCT and similarity measurement, the nonlinear and multi-stage problems are effectively solved. Finally, the proposed method is verified on NASA's accelerated degradation data set. Experimental results show that the proposed method can automatically learn the capacitor's complex degradation characteristics and accurately predict its RUL. • The BCT (Box–Cox Transformation) is applied to the degradation of AECs (aluminum electrolytic capacitor), which makes it possible to model the degradation process by Wiener process with linear drift. • Based on similarity measurement, an online updating method of DC is proposed, which can automatically learn the multi-stage degradation characteristics of failure samples. • Based on BCT and similarity measurement, a two-stage RUL prediction method for AECs is proposed. [ABSTRACT FROM AUTHOR]

• Multivariate Wiener processes combining time-scale transformed was proposed. • The correlation of four copula functions and the reliability analysis was studied. • The RUL prediction model combining copula function was proposed. • The degradation failure of the aircraft lock mechanism was studied in detail. The failure mechanism and degradation analysis of an aircraft cabin door lock mechanism are studied. A data-driven degradation analysis is conducted based on the characteristics of two types of degradation data for the lock mechanism. The degradation process of the fluctuating elevation is described by Wiener process, and the lifetime is described using the Inverse Gauss (IG) process. The correlation between the two degradation processes is studied, and the correlation analysis is carried out with four copula functions. Moreover, the optimal model is obtained for reliability analysis using three model selection methods and Bayesian inference based on the Markov chain Monte Carlo (MCMC) method for parameter estimation. Finally, a residual life prediction model is proposed, and the accuracy of the model is tested with the error. The results show that the RUL prediction accuracy can be accepted, which helps to improve the reliability and service life of a type of aircraft lock mechanism. [ABSTRACT FROM AUTHOR]

The reliability data are very useful in maintenance operations since they are used to calculate the Mean Time To Failure. However, they are rarely used for the online calculation of the Remaining Useful Life (RUL). This is because the features measured in the reliability tests are not always measurable online. In this paper, the proposed solution is usage of physical knowledge of the components to build models linking these features to the variables measurable online. Then, the physical models are used to generate health indices whose evolution can be estimated and predicted online, and the reliability data used for initializing the trend models of the health indices. To guarantee the robustness of the remaining useful life estimation to changes in Condition Monitoring, this paper proposes to update online the drift parameter of Wiener process. The latter is used to model the trend of the health indices. The results obtained by the most used updating methods in the literature are analyzed and compared in order to highlight the influence of the model updating on prognosis performance. Experimental results, obtained by an application to estimate the RUL of MOS Field-Effect Transistor, show the effectiveness of the proposed method. • Degradation trend modeling based on physical knowledge and Reliability data • Remaining Useful Life prediction based on updating Wiener process coefficient using most used estimators • Fault prognosis of MOS Field-Effect Transistors • Prognosis performance analysis using universal metrics [ABSTRACT FROM AUTHOR]