Non-linear Wiener process–based cutting tool remaining useful life prediction considering measurement variability

Sun, Huibin; Pan, Junlin; Zhang, Jiduo; Cao, Dali

doi:10.1007/s00170-020-05264-3

Cited by 44 publications

(11 citation statements)

References 18 publications

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“…In M-step, we substitute the expectations of μ i,k and ω i,k that are obtained in E-step into equation (23), and the estimated values of hyperparameters a l+1 , b l+1 , c l+1 , and d l+1 after l + 1 iterations under the kth stage degradation can be obtained as…”

Section: Complexitymentioning

confidence: 99%

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Residual Lifetime Prediction with Multistage Stochastic Degradation for Equipment

Gao

Qiguo

2020

Complexity

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Residual useful lifetime (RUL) prediction plays a key role of failure prediction and health management (PHM) in equipment. Aiming at the problems of residual life prediction without comprehensively considering multistage and individual differences in equipment performance degradation at present, we explore a prediction model that can fit the multistage random performance degradation. Degradation modeling is based on the random Wiener process. Moreover, according to the degradation monitoring data of the same batch of equipment, we apply the expectation maximization (EM) algorithm to estimate the prior distribution of the model. The real-time remaining life distribution of the equipment is acquired by merging prior information of real-time degradation data and historical degradation monitoring data. The accuracy of the proposed model is demonstrated by analyzing a practical case of metalized film capacitors, and the result shows that a better RUL estimation accuracy can be provided by our model compared with existing models.

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Section: Complexitymentioning

confidence: 99%

“…Si et al in [2] derived a random Wiener process model to predict the RUL of equipment by merging a recursive filtering algorithm and an EM algorithm for parameter estimation. With due consideration of the influence of measurement error, a nonlinear random Wiener model is proposed to predict remaining useful life [23].…”

Section: Introductionmentioning

confidence: 99%

Residual Lifetime Prediction with Multistage Stochastic Degradation for Equipment

Gao

Qiguo

2020

Complexity

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“…Statistical modeling approaches describe the tool state space in the form of the probability distribution function, which can effectively deal with uncertainties in the machining process [12]. Therefore, some researchers used statistical models to characterize the tool wear process, for example Wiener process [13], Gamma process [14], inverse Gaussian process [15], etc. It is often difficult to obtain sufficient offline measurement data in practical engineering, and the statistical modeling methods require many experimental data to accurately identify the model parameters.…”

Section: Introductionmentioning

confidence: 99%

A hybrid remaining useful life prediction method for cutting tool considering the wear state

Xiang

Pan

et al. 2022

Int J Adv Manuf Technol

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Accurate cutting tool remaining useful life (RUL) prediction is of significance to guarantee the cutting quality and minimize the production cost. Recently, physics-based and data-driven methods have been widely used in the tool RUL prediction. The physicsbased approaches may not accurately describe the time-varying wear process due to a lack of knowledge for underlying physics and simplifications involved in physical models, while the data-driven methods may be easily affected by the quantity and quality of data.To overcome the drawbacks of these two approaches, a hybrid prognostics framework considering tool wear state is developed to achieve an accurate prediction. Firstly, the mapping relationship between the sensor signal and tool wear is established by support vector regression (SVR). Then, the tool wear statuses are recognized by support vector machine (SVM) and the results are put into a Bayesian framework as prior information.Thirdly, based on the constructed Bayesian framework, parameters of the tool wear model are updated iteratively by the sliding time window and particle filter algorithm. Finally, the tool wear state space and RUL can be predicted accordingly using the updating tool wear model. The validity of the proposed method is demonstrated by a high-speed machine tool experiment. The results show that the presented approach can effectively reduce the uncertainty of tool wear state estimation and improve the accuracy of RUL prediction.

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“…Tsai et al [16] analyzed that some quality characteristics (QC) whose degradation over time can be related to the reliability of the product and established the degradation model by taking this variability into account. Sun et al [17] modeled the tool wear process of a cutting tool with the Wiener process considering the measurement variability and then estimated the RUL of cutting tool.…”

Section: Introductionmentioning

confidence: 99%

Three-Stage Wiener-Process-Based Model for Remaining Useful Life Prediction of a Cutting Tool in High-Speed Milling

Liu¹,

Yang²,

You³

2022

Sensors

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Tool condition monitoring can be employed to ensure safe and full utilization of the cutting tool. Hence, remaining useful life (RUL) prediction of a cutting tool is an important issue for an effective high-speed milling process-monitoring system. However, it is difficult to establish a mechanism model for the life decreasing process owing to the different wear rates in various stages of cutting tool. This study proposes a three-stage Wiener-process-based degradation model for the cutting tool wear estimation and remaining useful life prediction. Tool wear stages classification and RUL prediction are jointly addressed in this work in order to take full advantage of Wiener process, as this three-stage Wiener process definitely constitutes to describe the degradation processes at different wear stages, based on which the overall useful life can be accurately obtained. The numerical results obtained using extensive experiment indicate that the proposed model can effectively predict the cutting tool’s remaining useful life. Empirical comparisons show that the proposed model performs better than existing models in predicting the cutting tool RUL.

show abstract

Non-linear Wiener process–based cutting tool remaining useful life prediction considering measurement variability

Cited by 44 publications

References 18 publications

Residual Lifetime Prediction with Multistage Stochastic Degradation for Equipment

Residual Lifetime Prediction with Multistage Stochastic Degradation for Equipment

A hybrid remaining useful life prediction method for cutting tool considering the wear state

Three-Stage Wiener-Process-Based Model for Remaining Useful Life Prediction of a Cutting Tool in High-Speed Milling

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