A Nonlinear Prognostic Model for Degrading Systems With Three-Source Variability

Zheng, Jianfei; Si, Xiaosheng; Hu, Changhua; Zhang, Zhengxin; Jiang, Wei

doi:10.1109/tr.2015.2513044

Cited by 53 publications

(55 citation statements)

References 41 publications

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“…It is further assumed that ( ) t  and ( ) B t are mutually and statistically independent. These assumptions have been widely adopted in studies on degradation modeling [6], [10], [16], [17].…”

Section: Degradation Model Descriptionmentioning

confidence: 99%

“…Equations (1) and (2) constitute the basic linear degradation model with measurement errors, considering both the time-varying and measurement uncertainties in the degradation process [2]. This model and its nonlinear extension, i.e., the time-varying degradation rate, have been applied to the degradation modeling of battery data [10], LED data [16], and fatiguecrack data [17].…”

Section: Degradation Model Descriptionmentioning

confidence: 99%

“…Jin et al [10] and Si et al [15] employed a random-drift Wiener process model with measurement errors to predict the remaining useful life of a linear degradation system, respectively. Zheng et al [17] further extended this linear degradation model to nonlinear degradation situations. By introducing the measurement errors into the degradation process model, the robustness of the model used to estimate the actual degradation state could be improved [2], [16].…”

Section: Introductionmentioning

confidence: 99%

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Degradation Modeling Based on Wiener Process Considering Multi-Source Heterogeneity

Xiao

Zhang

et al. 2020

IEEE Access

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Degradation modeling using heterogeneous degradation data of a system population is critical for prognostics and health management. The heterogeneity may result from the inherent differences in the degrading systems and measuring instruments, as well as from the extrinsic environmental differences, making it difficult to predict the failure. Current studies have not considered the multiple sources of heterogeneity simultaneously, and consequently, have failed to capture the actual degradation process. To explain and quantify the combined effect of multi-source heterogeneity, we present a random effects Wiener process model with heteroscedastic measurement errors, where the parameters of the drift, diffusion, and error variance are assumed as random variables following a certain distribution. The Markov chain Monte Carlo method is adopted to estimate the posterior distributions of the actual degradation states and the model parameters under a hierarchical Bayesian framework. Based on the concept of first hitting time, the failure time distribution is estimated. To verify the presented approach, comparative studies are conducted with simulation and laser degradation data. The results show that considering multi-source heterogeneity can further eliminate the inter-individual variation in degradation data and improve the model fitting and prediction accuracy.

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Section: Degradation Model Descriptionmentioning

confidence: 99%

Section: Degradation Model Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Degradation Modeling Based on Wiener Process Considering Multi-Source Heterogeneity

Xiao

Zhang

et al. 2020

IEEE Access

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“…However, the work does not make full use of the degradation data so that a new degradation model including measurement errors was driven [12]. Other degradation model based on Wiener process incorporating measurement errors can refer to reference [13][14][15][16]. Therefore, the impacts of measurement errors on the proposed model are taken into account.…”

Section: Introductionmentioning

confidence: 99%

Accelerated Degradation Model of Nonlinear Wiener Process Based on Fixed Time Index

et al. 2019

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In the process of extrapolating a lifetime distribution function under normal storage conditions through nonlinear accelerated degradation data, time indexes under the normal storage conditions are usually set to the mean value of time indexes under various accelerated stresses. However, minor differences in time indexes may lead to great changes in the assessment results. For such a problem, an accelerated degradation model of a nonlinear Wiener process based on a fixed time index is established first and meanwhile, the impact of the measurement error is considered. Then, the probability density function is normalized, and multiple unknown parameters are estimated by using fminsearch function in MATLAB and multiple iterations. Finally, the model is validated by accelerated degradation test data of accelerometers and the O-type rubber sealing rings. The results show that there is a difference of 30,710 h for accelerometers between the mean time to failure under normal storage conditions obtained by the proposed method and the mean time to failure when the time indexes are the mean value of those under various accelerated stresses, and the main cause of the difference is compared and analyzed. A similar phenomenon is observed in the case study of O-type rubber sealing rings.

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“…However, it is difficult to acquire such large amount of training data to built suitable machine learning algorithms. Model-based approaches allow us to overcome this drawback by taking into account the physical degradation phenomenon of the system and deriving appropriate mathematical models [9], [32], [37]. Furthermore, prior knowledge on the degradation phenomenon helps us to limit the list of candidate models.…”

Section: Introductionmentioning

confidence: 99%

New Methodology for Improving the Inspection Policies for Degradation Model Selection According to Prognostic Measures

2018

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OATAO is an open access repository that collects the work of some Toulouse researchers and makes it freely available over the web where possible. This is an author's version published in: https://oatao.univ-toulouse.fr/23372Official URL : https://doi.Abstract-Health monitoring data are vital for failure prognostic and maintenance planning. Continuous monitoring data or frequent inspections can provide a large amount of information on degradation evolution and therefore ensure the quality of deterioration modeling and the lifetime prognostic accuracy. However, they are usually very costly, and sometimes inpractible in real engineering applications. Therefore, it is essential to address the issue of the appropriate amount of monitoring data. This paper proposes a new methodology to help the companies improving their actual inspection/monitoring policy to reduce operation and maintenance costs but also ensure the information quality. We investigate different types of inspection policies including periodic or non-periodic ones by considering multiples functions of the system degradation state that are linear, concave or convex. The best policies are chosen based on a multiobjective optimization problem dealing with the inspection cost and the information level. The advantages and disadvantages of the proposed methodology are discussed through numerous numerical examples for different types of degradation process, particularly Wiener and Gamma processes that have been largely addressed in the framework of degradation modeling.

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A Nonlinear Prognostic Model for Degrading Systems With Three-Source Variability

Cited by 53 publications

References 41 publications

Degradation Modeling Based on Wiener Process Considering Multi-Source Heterogeneity

Degradation Modeling Based on Wiener Process Considering Multi-Source Heterogeneity

Accelerated Degradation Model of Nonlinear Wiener Process Based on Fixed Time Index

New Methodology for Improving the Inspection Policies for Degradation Model Selection According to Prognostic Measures

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