Two-stage physics-based Wiener process models for online RUL prediction in field vibration data

Yan, Bingxin; Ma, Xiaobing; Huang, Guifa; Zhao, Yu

doi:10.1016/j.ymssp.2020.107378

Cited by 70 publications

(26 citation statements)

References 41 publications

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“…Based on the nonlinear characteristics in the degradation process of lithium-ion battery, Zhu et al [18] transformed the nonlinear degradation model into a linear model so that the product degradation process could be modeled by the Wiener process. Yan et al [19] described a two-stage degradation process with nonlinear time-varying mean and variance and a life degradation model with individual differences in the location of change points.…”

Section: Application Of the Wiener Process Methodsmentioning

confidence: 99%

Research on the Life Prediction Method of Meters Based on a Nonlinear Wiener Process

et al. 2022

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Due to the high reliability of present meters, it is difficult to obtain the failure time of meters through accelerated life tests. Based on the failure data of the accelerated life test, this paper studies the mathematical model based on the Wiener process and establishes the degradation model of the instrument by the maximum likelihood to estimate the parameters of the Wiener model. With full consideration of the possible nonlinear effects in modeling, the time scale transformation method is used to study and obtain the reliability life prediction model of smart meters based on nonlinear data. Finally, the reliability life prediction model of meters is verified and evaluated through the example data of the accelerated life test of smart meters. Compared with the conventional method, this method has less error in calculating the reliability, greatly saves test time, and has a higher accuracy than estimating the lifetime model using the Wiener process directly.

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Section: Application Of the Wiener Process Methodsmentioning

confidence: 99%

Research on the Life Prediction Method of Meters Based on a Nonlinear Wiener Process

et al. 2022

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“…φ t−△t and ω t are the corresponding noises. x t is the system state at time t. e system model can be developed based on some datadriven empirical models, such as Wiener process models [32,33] and gamma process models [34,35]. In this paper, we apply a common Wiener process model as the basis degradation function of our modeling framework, and we will introduce it in the next section.…”

Section: Particle Filtermentioning

confidence: 99%

Remaining Useful Life Prediction of Quay Crane Hoist Gearbox Bearing under Dynamic Operating Conditions Based on ARIMA‐CAPF Framework

Sun

Xiong

Dong

2021

Shock and Vibration

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The remaining useful life (RUL) prediction of quay crane (QC) bearings is of great significance to port production safety. An RUL prediction framework of QC bearing under dynamic conditions is proposed. Firstly, the load is discretized, and the corresponding operating conditions are classified. Then, the Autoregressive Integrated Moving Average (ARIMA) model is utilized to predict the load and corresponding operating conditions. Secondly, a Wiener process considering degradation rates and jump coefficients under different operating conditions is developed as the state transfer function. Finally, a condition-activated particle filter (CAPF) is proposed to predict the system state and the bearing’s RUL. The proposed prediction framework is verified by the hoist bearing life cycle data from a port in Shanghai collected by the NetCMAS system. The prediction results by the ARIMA-CAPF framework in comparison with three other prediction strategies identify the effectiveness.

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“…Despite the aforementioned advancements in vibration-based RUL estimation, the application of such methods to railway vehicle wheels using on-board measurements is limited to two recent studies [7,8]. Within these, RUL estimation of wheels with severe fault types in their treads such as spalling and cracks is attempted through methods of the first category.…”

Section: Introductionmentioning

confidence: 99%

On-board vibration-based remaining useful life estimation of hollow worn railway vehicle wheels

Iliopoulos

Sakellariou

Fassois

et al. 2022

J. Phys.: Conf. Ser.

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The feasibility of on-board remaining useful life (RUL) estimation of hollow worn railway vehicle wheels under varying travelling speed using vibration signals from the vehicle’s bogie, is for the first time explored. A variant of the Multiple Model Power Spectral Density (MM-PSD) method is employed, treating RUL estimation as a multi-class classification problem, where each class corresponds to a specific condition of hollow worn wheels associated with an empirical remaining mileage. The method classifies the current, unknown, condition of hollow worn wheels to one of the available by examining the vehicular dynamics similarity among them through the amplitude of the sharp periodic valleys (antiresonances) induced due to the Wheelbase Filtering (WF) effect. The vibration signals are obtained from Monte Carlo experiments based on the high-fidelity SIMPACK software, while the remarkable performance of the method is demonstrated via thousands of inspection cases.

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Two-stage physics-based Wiener process models for online RUL prediction in field vibration data

Cited by 70 publications

References 41 publications

Research on the Life Prediction Method of Meters Based on a Nonlinear Wiener Process

Research on the Life Prediction Method of Meters Based on a Nonlinear Wiener Process

Remaining Useful Life Prediction of Quay Crane Hoist Gearbox Bearing under Dynamic Operating Conditions Based on ARIMA‐CAPF Framework

On-board vibration-based remaining useful life estimation of hollow worn railway vehicle wheels

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