Accelerated Degradation Testing With Long-Term Memory Effects

Si, Wujun; Shao, Yunfei; Wei, Wei

doi:10.1109/tr.2020.2997404

Cited by 12 publications

(1 citation statement)

References 45 publications

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“…The general path model is used with individual random effects as a degradation path model and the covariate process is described using a vector time series model. Si et al [46] proposed a novel accelerated degradation testing (ADT) model by integrating the covariates effect such as the long-term degradation memory effect. Simulation studies were implemented to illustrate the efficiency of the developed methods.…”

Section: Introductionmentioning

confidence: 99%

Prognostic and Classification of Dynamic Degradation in a Mechanical System Using Variance Gamma Process

2021

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Recently, maintaining a complex mechanical system at the appropriate times is considered a significant task for reliability engineers and researchers. Moreover, the development of advanced mechanical systems and the dynamics of the operating environments raises the complexity of a system’s degradation behaviour. In this aspect, an efficient maintenance policy is of great importance, and a better modelling of the operating system’s degradation is essential. In this study, the non-monotonic degradation of a centrifugal pump system operating in the dynamic environment is considered and modelled using variance gamma stochastic process. The covariates are introduced to present the dynamic environmental effects and are modelled using a finite state Markov chain. The degradation of the system in the presence of covariates is modelled and prognostic results are analysed. Two machine learning algorithms k-nearest-neighbour (KNN) and neural network (NN) are applied to identify the various characteristics of degradation and the environmental conditions. A predefined degradation threshold is assigned and used to propose a prognostic result for each classification state. It was observed that this methodology shows promising prognostic results.

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Section: Introductionmentioning

confidence: 99%

Prognostic and Classification of Dynamic Degradation in a Mechanical System Using Variance Gamma Process

2021

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Deep learning the Hurst parameter of linear fractional processes and assessing its reliability

Boros,

Csanády,

Ivkovic

et al. 2024

Quality & Reliability Eng

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This research explores the reliability of deep learning, specifically Long Short‐Term Memory (LSTM) networks, for estimating the Hurst parameter in fractional stochastic processes. The study focuses on three types of processes: fractional Brownian motion (fBm), fractional Ornstein–Uhlenbeck (fOU) process, and linear fractional stable motions (lfsm). The work involves a fast generation of extensive datasets for fBm and fOU to train the LSTM network on a large volume of data in a feasible time. The study analyses the accuracy of the LSTM network's Hurst parameter estimation regarding various performance measures like root mean squared error (RMSE), mean absolute error (MAE), mean relative error (MRE), and quantiles of the absolute and relative errors. It finds that LSTM outperforms the traditional statistical methods in the case of fBm and fOU processes; however, it has limited accuracy on lfsm processes. The research also delves into the implications of training length and valuation sequence length on the LSTM's performance. The methodology is applied to estimating the Hurst parameter in li‐ion battery degradation data and obtaining confidence bounds for the estimation. The study concludes that while deep learning methods show promise in parameter estimation of fractional processes, their effectiveness is contingent on the process type and the quality of training data.

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Reliability modeling and statistical analysis of accelerated degradation process with memory effects and unit-to-unit variability

Chen,

Li,

Xie

2025

Applied Mathematical Modelling

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Accelerated Degradation Testing With Long-Term Memory Effects

Cited by 12 publications

References 45 publications

Prognostic and Classification of Dynamic Degradation in a Mechanical System Using Variance Gamma Process

Prognostic and Classification of Dynamic Degradation in a Mechanical System Using Variance Gamma Process

Deep learning the Hurst parameter of linear fractional processes and assessing its reliability

Reliability modeling and statistical analysis of accelerated degradation process with memory effects and unit-to-unit variability

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