Remaining Useful Life Prediction for Degradation Processes With Long-Range Dependence

Zhang, Hanwen; Chen, Maoyin; Xi, Xiaopeng; Zhou, Donghua

doi:10.1109/tr.2017.2720752

Cited by 49 publications

(38 citation statements)

References 30 publications

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“…To overcome this challenge, Si and his co-authors [13] assumed that the drift coefficient of the Wiener process is time-dependent without using any time-scale transformation, and then a closed-form PDF is obtained by changing the constant threshold to an arbitrary boundary. Recently, the validity of this modeling method has been proved by several scholars using numerical analyses [168], [169]. At current stage, the most popular link functions to characterize the relationship between the accelerated time and the drift coefficient are the power law model and the exponential model.…”

Section: A: Wiener Processmentioning

confidence: 99%

Modeling and Analysis of Performance Degradation Data for Reliability Assessment: A Review

Chen

Liu

et al. 2020

IEEE Access

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Reliability engineering plays an important role in the design, manufacture, maintenance, and replacement of industrial products. Over the last few decades, accelerated degradation testing (ADT) has been largely utilized to shorten test durations, reduce the samples needed, and provide sufficient degradation data to ensure the effective reliability assessment of the concerned products. Meanwhile, performance degradation modeling has been recognized as an essential approach to help researchers and producers understand the health conditions of the deteriorating systems. However, the diversity in reliability tests, degradation models, and statistical analysis techniques has increased the difficulty in selecting appropriate reliability assessment methods in specific scenarios. Besides, there are no systematic reviews focused on modeling and analysis of performance degradation data. Therefore, this paper aims to (1) present ADT fundamentals, including the basic theory, ADT methods, accelerated stress variables, type of acceleration models, as well as ADT optimization, (2) comprehensively review current states and future challenges in degradation modeling, (3) discuss the problem of model mis-specification and compare different approaches for parameter estimation, (4) highlight future opportunities and possible directions deserving further research.

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Section: A: Wiener Processmentioning

confidence: 99%

Modeling and Analysis of Performance Degradation Data for Reliability Assessment: A Review

Chen

Liu

et al. 2020

IEEE Access

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“…A two-stage degradation model is proposed in the literature [10], and the Bayesian method is used to estimate the model parameters. In addition to these, Cox proportional hazard model [11], nonlinear Wiener process model [12] and fractional Brownian motion model [13] have also been introduced into RUL prediction.…”

Section: New Faultmentioning

confidence: 99%

“…After comparison, 14 sensors data are selected from 21 sensors as the features of the engine. The numbers of the 14 sensors are [2,3,4,7,8,9,11,12,13,14,15,17,20,21].…”

Section: Experiments and Analysis A Experimental Data Introductionmentioning

confidence: 99%

An Improved PF Remaining Useful Life Prediction Method Based on Quantum Genetics and LSTM

Yang

Sun

2019

IEEE Access

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Remaining useful life (RUL) is the premise and basis of the equipment health management plan. As accurate as possible life prediction is of great significance to reliability and economy of equipment maintenance. In this paper, a data-driven improved particle filter (PF) RUL prediction method is proposed. A health indicator extraction method based on multi-feature fusion is introduced for the RUL prediction, which can visually show the degradation trend of the healthy state of the equipment. The degradation model and observation model of equipment health indicators are established, and the PF algorithm is used to track parameters of the model. A quantum genetic algorithm is employed to improve the problem of particle degradation in PF. On the basis of filter tracking, long short term memory (LSTM) network is used to predict the trend of model coefficients, which further improves the accuracy of RUL prediction. The experiment using the C-MAPSS data set shows the proposed method has a better prediction accuracy than other methods. INDEX TERMS Remaining useful life, particle filter, quantum genetic algorithm, long short term memory.

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“…FBm has also some applications in degradation modeling and remaining useful life prediction [20]. Zhang et al [21] use fBm to describe the degradation of the blast furnace wall. Qin and Lin [22] combine fBm with Delft3D, WRF model, and GIS to predict the trajectory of harmful algal blooms.…”

Section: Introductionmentioning

confidence: 99%

Multifractional Brownian Motion and Quantum-Behaved Partial Swarm Optimization for Bearing Degradation Forecasting

et al. 2020

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Gradual degradation of the bearing vibration signal is usually studied as a nonstationary stochastic time series. Roller bearings are working at high speed in a heavy load environment so that the combination of bearing faults gradually degraded during the rotation might lead to unpredicted catastrophic accidents. The degradation process has the property of long-range dependence (LRD), so that the fractional Brownian motion (fBm) is taken into account for a prediction model. Because of the dramatic changes in the bearing degradation process, the Hurst exponent that describes the fBm will change during the degradation process. A priori Hurst value of the conventional fBm in the prediction is fixed, thus inducing a minor accuracy of the prediction. To avoid this problem, we propose an improved prediction method. Based on the following steps, at the initial data processing, a skip-over factor is selected as the characteristics parameter of the bearing degradation process. A multifractional Brownian motion (mfBm) replaces the fBm for the degradation modeling. We will show that also our mfBm has the same property of long-range dependence as the fBm. Moreover, a time-varying Hurst exponent H(t) is taken to replace the constant H in fBm. Finally, we apply the quantum-behaved partial swarm optimization (QPSO) to optimize H(t) for a finite interval. Some tests and corresponding experimental results will show that our model QPSO + mfBm have a much better performance on the prediction effect than fBm.

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Remaining Useful Life Prediction for Degradation Processes With Long-Range Dependence

Cited by 49 publications

References 30 publications

Modeling and Analysis of Performance Degradation Data for Reliability Assessment: A Review

Modeling and Analysis of Performance Degradation Data for Reliability Assessment: A Review

An Improved PF Remaining Useful Life Prediction Method Based on Quantum Genetics and LSTM

Multifractional Brownian Motion and Quantum-Behaved Partial Swarm Optimization for Bearing Degradation Forecasting

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