Estimating Remaining Useful Life With Three-Source Variability in Degradation Modeling

Si, Xiaosheng; Wang, Wenbin; Hu, Changhua; Zhou, Donghua

doi:10.1109/tr.2014.2299151

Cited by 190 publications

(139 citation statements)

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“…Si et al [185] attempted to characterize the effect of three sources of variation (temporal, unit-tounit, and measurement) contributing to the uncertainty of the estimated RUL via a general degradation model. The PDF of the underlying degradation state and random effect parameters were estimated based on KF.…”

Section: Kalman Filter-based Modelmentioning

confidence: 99%

Cloud-enabled prognosis for manufacturing

et al. 2015

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Advanced manufacturing depends on the timely acquisition, distribution, and utilization of information from machines and processes across spatial boundaries. These activities can improve accuracy and reliability in predicting resource needs and allocation, maintenance scheduling, and remaining service life of equipment. As an emerging infrastructure, cloud computing provides new opportunities to achieve the goals of advanced manufacturing. This paper reviews the historical development of prognosis theories and techniques and projects their future growth enabled by the emerging cloud infrastructure. Techniques for cloud computing are highlighted, as well as the influence of these techniques on the paradigm of cloud-enabled prognosis for manufacturing. Finally, this paper discusses the envisioned architecture and associated challenges of cloud-enabled prognosis for manufacturing

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Section: Kalman Filter-based Modelmentioning

confidence: 99%

Cloud-enabled prognosis for manufacturing

et al. 2015

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“…In the -step, the parameter estimate is updated through maximizing the expected function found in the -step, and then the updated parameter estimate is utilized to determine the distribution of the latent variables in the E-step of next iteration. By this way, EM algorithm iterates between the two steps until convergence [14,18].…”

Section: Unknown Parameter Fusion Identificationmentioning

confidence: 99%

“…Si et al in [6] reviewed the statistical data driven approaches for RUL estimation. The existing methods were classified into two categories: direct condition monitoring data based approaches and indirect condition monitoring data based approaches, which can be further divided into stochastic filtering based methods [7][8][9][10], covariance based hazard model methods [11,12], Wiener-process-based methods [13,14], Gamma process based methods [15,16], and Markovian-based methods [17] and others.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Estimation Based on Asynchronous Multisource Monitoring Information Fusion

Xue

et al. 2017

Journal of Control Science and Engineering

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An asynchronous RUL fusion estimation algorithm is presented for the hidden degradation process with multiple asynchronous monitoring sensors based on multisource information fusion. Firstly, a state-space type model is established by modeling the stochastic degradation as a Wiener process and transforming asynchronous indirectly observations in the fusion period to the fusion time. The statistical characteristics of involved noises and their correlations are analyzed. Secondly, the estimate of the hidden degradation state is obtained by applying Kalman filtering with correlated noises to the established state-space model, where the synchronized observations are fused. Also, the unknown model parameters are recursively identified based on the Expectation-Maximization (EM) algorithm with the Generic Algorithm (GA) adopted to solve the maximization problem. Finally, the probability distribution of RUL is obtained using the fused degradation state estimation and the updated identification result of the model parameters. Simulation results show that the proposed fusion method has better performance than the RUL estimation with single sensor.

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“…Integrating the influence of uncertainty measurement and individual differences into reliability analysis to achieve more accurate reliability analysis of stochastic degradation systems is worth studying. In recent years, many scholars have been devoted to this [1][2][3][4][5][6][7][8]. Zhang [4] models the degradation state evolution of a system through a diffusion process with piecewise but time-dependent drift coefficient functions.…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis of the High-speed Train Bearing Based on Wiener Process

Zhu

Nan

2018

Information

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Abstract:Because of the existence of uncertainty measurement in the process of bearings degradation, it is difficult to carry out the reliability analysis. The random performance degradation model is used to analyze the reliability life of high-speed train bearing with the characteristics of slow degradation process and relatively stable degradation path. The unknown coefficients are viewed as random variables in the model. According to the analysis to a bearing testing data, the reliability analysis of the bearing life is finally completed. The results show that the method can assess reliability of the bearing life with zero failure by taking full advantage of the performance degradation data in the small sample size.

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Estimating Remaining Useful Life With Three-Source Variability in Degradation Modeling

Cited by 190 publications

References 50 publications

Cloud-enabled prognosis for manufacturing

Cloud-enabled prognosis for manufacturing

Remaining Useful Life Estimation Based on Asynchronous Multisource Monitoring Information Fusion

Reliability Analysis of the High-speed Train Bearing Based on Wiener Process

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