Remaining Useful Life Prediction for Degradation Processes With Memory Effects

Zong

Quality & Reliability Eng

et al. 2018

Focusing on improving the accuracy of existing life prediction models for optoelectronic products, the three‐parameter Weibull right approximation method (TPWRAM) was employed to substitute exponential function based on the least square method in the analysis and two‐staged methods. Two optimized models were established (Model I and Model II), based on maximum likelihood estimation and the Monte Carlo method, respectively. One group of conventional life tests (CLTs) of vacuum fluorescent display (VFD) were conducted to collect luminance degradation data for each sample, and the two optimized models were applied to achieve VFD life prediction and obtain mean time to failure, median life, and confidence intervals. The results indicate that the CLT test design is correct and feasible, the amount of data on luminance degradation is large, and the test data selection method is reasonable. Model I and Model II optimized by TPWRAM both reflect the VFD luminance variation law well, and the predicted life approaches VFD service life from user feedback, proving that the two models are precise, and thus, can provide technical references for researchers and engineers regarding aspects of life prediction.

“…To obtain the degradation path function, the following assumptions are made: 1 n samples are randomly selected from products and the luminance D ij of the i th at time t ij can be expressed as

D_{ij} = D (();;;, t_{italicij}, θ_{1 i}, θ_{2 i} \dots, θ_{italicki}) + ε_{ij} ((),,,;,,, i = 1 2 \dots n j = 0 1 \dots N_{i} - 1)

ε_{ij} \sim italicNormal ((),, 0, σ_{ε_{i}}^{2})

2Suppose that D ( t ij ; θ 1 i ; θ 2 i ⋯; θ ki ) follows the three‐parameter Weibull distribution

\overset{D}{true} ((), t_{italicij})

, then

\overset{D}{true} ((), t_{italicij}) = 1 - exp ({}, - {(\frac{t_{ij} - t_{0 i}}{β_{i}})}^{α_{i}})

in which α i = θ 2 i is a shape parameter, β i = θ 3 i is a scale parameter, and t 0 i = θ 1 i is a location parameter.…”

Section: Life Prediction Modelsmentioning

confidence: 99%

“…The test error ε ij follows the normal distribution, namely, ε ij ∼ Normal 0; σ 2 ε i . 22 2) Suppose that D(t ij ; θ 1i ; θ 2i ⋯; θ ki ) follows the threeparameter Weibull distribution D t ij À Á , then…”

Section: Tpwrammentioning

confidence: 99%

Two optimized models of life prediction based on luminance degradation for VFD under a conventional life test

Zong

Quality & Reliability Eng

et al. 2018

Quality & Reliability Eng

“…Because of the conciseness and feasibility, the condition monitoring (CM) data–based methods are quite popular in RUL prediction. Specifically, without any demand of expert knowledge or extra experiments, data‐driven methods have occupied a significant place in the existing literature, such as the utilization of Brownian motion (BM, also refers to the Wiener process) and fractional Brownian motion (FBM) . These stochastic models possess remarkable similarities that their described degradation processes should follow the specified distribution forms, and hence would facilitate the identification of unknown parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, some progress has been made with respect to the non‐Markovian degradation modeling . The FBM was first employed to construct the diffusion process for the purpose of capturing the memory effects from the degradation path.…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction for multivariable stochastic degradation systems with non‐Markovian diffusion processes

Zhou

Chen

et al. 2020

Self Cite

Multivariable stochastic degradation system (MSDS) is quite common in indus-tries such as blast furnace ironmaking, vehicle transportation, and aerospace manufacturing. Large-scale complex equipments may be affected by multiple factors, resulting in not just a single deteriorating performance characteristic. It is difficult to handle unknown failure structures of practical systems by using traditional univariate degradation modeling methods. A novel health index (HI) is constructed to quantitatively analyze the health state for the overall system. Considering the interaction between internal reactions and external environments, the fractional Brownian motion (FBM), a typical non-Markovian diffusion process, is added for the purpose of reflecting stochastic uncertainties and memory effects. Based on the wavelet estimators and the maximum likelihood estimation (MLE) algorithm, multi-sensor observations of degradation variables are analyzed simultaneously to identify model parameters. A closed-form distribution of system-level remaining useful life (RUL) is obtained with a mild two-layer approximation. Relevant case studies are then handled that adequately demonstrate the effectiveness and the practical utility of the proposed method.

“…However, BM is indeed a Markovian stochastic process. To address this deficiency, fractional Brownian motion (FBM) has been introduced into degradation modelling, FPT analysis, and RUL prediction . As a nonlinear extension of BM, FBM utilizes the Hurst exponent to describe the long‐term or short‐term dependency of degradation paths, and fits some non‐stationary diffusion processes in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction for fractional degradation processes under varying modes

Zhou

Chen

2019

Can J Chem Eng

Self Cite

Degradation processes of practical chemical engineering systems are difficult to model accurately because of complicated nonlinearities, non-stationarities, and non-Markovian properties. Traditional prognostics techniques tend to neglect the multi-mode switching issue, and therefore cannot be used for predicting the remaining useful life (RUL) of a piece of long-life equipment, especially when it is continuously operated under varying modes. Specifically, the stationarity of differential data also plays an important role in depicting the evolutionary trend, and further affects the extrapolation procedure for RUL prediction. In this paper, we construct a multi-mode degradation model with regard to fractional diffusion processes by considering both stationary and non-stationary increments. The drift term is represented as a time-related nonlinear function, while the diffusion term is driven by fractional Brownian motion (FBM) or sub-fractional Brownian motion (sub-FBM), according to the results of the stationary test. Based on the monitored data, the model is identified by combining change-point detection and parameter estimation. The closed-form distribution of RUL is then derived through a weak convergence transformation. A numerical example and a case study of a large blast furnace are provided to evaluate the performance of the proposed prognostics framework. K E Y W O R D S degradation, multi-mode, non-Markovian, remaining useful life, stationary test