Degradation modeling for real-time estimation of residual lifetimes in dynamic environments

Bian, Linkan; Gebraeel, Nagi; Kharoufeh, Jeffrey P.

doi:10.1080/0740817x.2014.955153

Cited by 85 publications

(44 citation statements)

References 29 publications

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“…Degradation modeling for pure time-dependent degradation processes has been well studied over the past two decades (Meeker and Escobar 1998, Bae and Kvam 2004, Liu and Tang 2010, Ye and Chen 2014, Xu and Chen 2017, and the modeling of degradation data under dynamic environments has also received much attention in recent years (Liao and Tian 2012, Zhou, Serban and Gebraeel 2014, Bian et al 2015, Hong et al, 2015. In the literature, there exists an important class of models which describes degradation paths by a stochastic processes…”

Section: Degradation Models Based On Stochastic Processesmentioning

confidence: 99%

A statistical modeling approach for spatio-temporal degradation data

Liu

Yeo

Kalagnanam

2018

Journal of Quality Technology

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This paper investigates the modeling of an important class of degradation data, which are collected from a spatial domain over time; for example, the surface quality degradation. Like many existing time-dependent stochastic degradation models, a special random field is constructed for modeling the spatio-temporal degradation process. In particular, we express the degradation at any spatial location and time as an additive superposition of two stochastic components: a dynamic spatial degradation generation process, and a spatio-temporal degradation propagation process. Some unique challenges are addressed, including the spatial heterogeneity of the degradation process, the spatial propagation of degradation to neighboring areas, the anisotropic and space-time non-separable covariance structure often associated with a complex spatio-temporal degradation process, and the computational issue related to parameter estimation. When the spatial dependence is ignored, we show that the proposed spatio-temporal degradation model incorporates some existing pure time-dependent degradation processes as its special cases. We also show the connection, under special conditions, between the proposed model and general physical degradation processes which are often defined by stochastic partial differential equations. A numerical example is presented to illustrate the modeling approach and model validation.

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Section: Degradation Models Based On Stochastic Processesmentioning

confidence: 99%

A statistical modeling approach for spatio-temporal degradation data

Liu

Yeo

Kalagnanam

2018

Journal of Quality Technology

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“…On the other hand, stress transition shocks occur at fixed times, i.e., the stress level changes instantaneously. Bian et al [27] considered that stress transitions may induce the shock damage to products at stress transition times. The shock damage is assumed to have a linear correlation with stress increment.…”

Section: Introductionmentioning

confidence: 99%

Reliability assessment considering stress drift and shock damage caused by stress transition shocks in a dynamic environment

Huang

Peng

Zhao

et al. 2019

Journal of Systems Engineering and Electronics

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Products are often subject to dynamic environmental conditions in field use. When stress transition occurs, products may be exposed to instantaneous shocks that result in shock damages to the products, causing a permanent change of the degradation signals. Meanwhile, under some conditions, instantaneous shocks also lead to stress drift, causing a temporary change of the degradation signals. In this paper, a degradation model is proposed to assess the reliability and predict the residual lifetime of products operating in a dynamic environment considering shock damage and stress drift. The model is established based on a Wiener process which combines a stress-dependent degradation rate function, a shock damage function and a stress drift function in response to the dynamic environment. The shock damage function is established as a linear function of the stress transition start level and the stress level increment. The stress drift function is established as the difference value of a specified function at the stress transition start and end levels. A simulation study is presented to demonstrate the application of the model, and a case study for miniature light bulbs is used to validate the effectiveness of the proposed model.

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“…Traditional reliability assessment methods regard the degradation process of system reliability or SOH as determined and seek to construct the underlying degradation model from a large number of historical data of similar equipments, without taking account the dynamics of operating conditions or specificity for a individual equipment [3][4][5][6][7][8]. Actually as noted by Bian, by now the majority of reliability prediction models are based on the assumption that the prevailing operating conditions are regarded as temporarily constant or irrelevant to the evolution process [9]. However, the engineering equipments in practical industrial systems, especially in the modern complex systems, usually work under varying operating conditions caused by not only the uncontrollable external environment such as ambient temperature or other circumstance factors, but also the controllable operating profiles or workloads.…”

Section: Introductionmentioning

confidence: 99%

“…This modeling framework is mostly appropriate when the operating conditions can be clearly distinguished and their effects on the reliability evolution processes are significant. For instance, consider the workload induced from an aircraft engine in different flight conditions as: takeoff, maximum climb, maximum cruise, loiter, flight idle, taxi, ground idle, and cutoff [9]. The evolution of workload in these conditions will be totally different and the switching of conditions is sudden and can be regarded as random, thus distinct dynamic multi-state conditions can be recognized in this case.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel support vector regression method for online reliability prediction under multi-state varying operating conditions

Tao

Zio

Zhao

2018

Reliability Engineering & System Safety

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Modeling the evolution of system reliability in the presence of Condition Monitoring (CM) signals is an important issue for improved reliability assessment and system lifetime prediction. In practice, during its lifetime, a system usually works under varying operating conditions due to internal or external factors such as the ambient environments, operational profiles or workloads. In this context, the system reliability can show varying evolution behaviors (follow changing underlying trajectories), which presents new challenges to describe precisely the dynamics of system reliability. Thus, this paper proposes a novel data-driven approach to address the problems including the identification of varying operating conditions, the construction and dynamical updating of evolution model, and finally the online prediction of system reliability, focusing on systems under one common and typical case of varying operating conditions, the multi-state operating condition. Experiments based on artificial data and some widely studied real reliability cases reveal that the proposed method has superior performance compared with some existing benchmark approaches, in the case under consideration. This improved reliability prediction provides fundamental basis for advanced prognostics such as the Remaining Useful Life (RUL) estimation.

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Degradation modeling for real-time estimation of residual lifetimes in dynamic environments

Cited by 85 publications

References 29 publications

A statistical modeling approach for spatio-temporal degradation data

A statistical modeling approach for spatio-temporal degradation data

Reliability assessment considering stress drift and shock damage caused by stress transition shocks in a dynamic environment

A novel support vector regression method for online reliability prediction under multi-state varying operating conditions

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