Degradation Trend Prognostics for Rolling Bearing Using Improved R/S Statistic Model and Fractional Brownian Motion Approach

Li, Qing; Liang, Steven Y.

doi:10.1109/access.2017.2779453

Cited by 25 publications

(19 citation statements)

References 43 publications

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“…Lu et al [16] propose a prognostic algorithm using the variable forgetting factor, recursive least-square, combined with an auto-regressive and moving average model. Li et al [17] propose a prognostics methodology based on improved R/S statistic and Fractional Brownian Motion (FBM). Ayhan et al [18] propose to run a bank of parallel-running remaining useful life predictors to mitigate the risk of getting unstable results in contrast to a single predictor.…”

Section: Prognosticsmentioning

confidence: 99%

Probability-Based Algorithm for Bearing Diagnosis with Untrained Spall Sizes

Tam

Kalech

Rokach

et al. 2020

Sensors

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Bearing spall detection and predicting its size are great challenges. Model-based simulation is a well-known traditional approach to physically model the influence of the spall on the bearing. Building a physical model is challenging due to the bearing complexity and the expert knowledge required to build such a model. Obviously, building a partial physical model for some of the spall sizes is easier. In this paper, we propose a machine-learning algorithm, called Probability-Based Forest, that uses a partial physical model. First, the behavior of some of the spall sizes is physically modeled and a simulator based on this model generates scenarios for these spall sizes in different conditions. Then, the machine-learning algorithm trains these scenarios to generate a prediction model of spall sizes even for those that have not been modeled by the physical model. Feature extraction is a key factor in the success of this approach. We extract features using two traditional approaches: statistical and physical, and an additional new approach: Time Series FeatuRe Extraction based on Scalable Hypothesis tests (TSFRESH). Experimental evaluation with well-known physical model shows that our approach achieves high accuracy, even in cases that have not been modeled by the physical model. Also, we show that the TSFRESH feature-extraction approach achieves the highest accuracy.

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Section: Prognosticsmentioning

confidence: 99%

Probability-Based Algorithm for Bearing Diagnosis with Untrained Spall Sizes

Tam

Kalech

Rokach

et al. 2020

Sensors

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“…From the view of application, roughly, the existing prediction approaches can be divided into two categories: (a) parametric-based methods and (b) nonparametric-based methods. In the literature, parametric-based methods mainly include time-series methods such as autoregressive moving average (ARMA) [7,8], fractional autoregressive integrated moving average (FARIMA) [9,10], fractional Brownian motion (FBM) [11,12], hidden Markov model (HMM) [13,14] and grey theoretical model (GTM) [15,16], etc. Generally, the parametric-based methods overcome the hurdle of predictive availability during long-term prediction (according to needs) which assumes the model's parameters to be constants in the predicted region.…”

Section: Of 27mentioning

confidence: 99%

“…Figure 8 shows the peak-to-peak values of the whole lifetime of bearing 1. Accordingly, the health indicators, i.e., equivalent vibration intensity (EVI) [9,11], Kurtosis and EVI of bearing 2, bearing 3 and bearing 4 are illustrated in Figure 9a-c, respectively. It is seen that the amplitudes of bearings 1, 3 and 4 have gradual increasing trends, in addition, the whole test life of bearing 3 is the shortest due to harsh operating conditions, which indicates that the extremely failures are occurred before the experiment stops, thus, representing abrupt degradation processes, whereas the EVI amplitudes of bearings 2 show gradual increases; it might be concluded that the design/manufacturing quality and fatigue resistance strength are much higher than others under the same operating conditions.…”

Section: Experimental Validationsmentioning

confidence: 99%

Intelligent Prognostics of Degradation Trajectories for Rotating Machinery Based on Asymmetric Penalty Sparse Decomposition Model

Liang

2018

Symmetry

Self Cite

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The ability to accurately track the degradation trajectories of rotating machinery components is arguably one of the challenging problems in prognostics and health management (PHM). In this paper, an intelligent prediction approach based on asymmetric penalty sparse decomposition (APSD) algorithm combined with wavelet neural network (WNN) and autoregressive moving average-recursive least squares algorithm (ARMA-RLS) is proposed for degradation prognostics of rotating machinery, taking the accelerated life test of rolling bearings as an example. Specifically, the health indicators time series (e.g., peak-to-peak value and Kurtosis) is firstly decomposed into low frequency component (LFC) and high frequency component (HFC) using the APSD algorithm; meanwhile, the resulting non-convex regularization problem can be efficiently solved using the majorization-minimization (MM) method. In particular, the HFC part corresponds to the stable change around the zero line of health indicators which most extensively occurs; in contrast, the LFC part is essentially related to the evolutionary trend of health indicators. Furthermore, the nonparametric-based method, i.e., WNN, and parametric-based method, i.e., ARMA-RLS, are respectively introduced to predict the LFC and HFC that focus on abrupt degradation regions (e.g., last 100 points). Lastly, the final predicted data could be correspondingly obtained by integrating the predicted LFC and predicted HFC. The proposed methodology is tested using degradation health indicator time series from four rolling bearings. The proposed approach performed favorably when compared to some state-of-the-art benchmarks such as WNN and largest Lyapunov (LLyap) methods.

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“…Song et al presented an FBM‐based stochastic model to extract information from the vibration intensity of the bearings, which exhibited good performance on the diagnosis of weak faults. Based on the R/S statistic and the stochastic differential equation, Li et al combined BM and FBM to track the degradation trends of the rolling bearings with the existence of sharp transition points. Zhang et al discussed the issues of age‐ and state‐dependence in depth and integrated the FBM as a new framework for degradation modeling and for RUL prediction.…”

Section: Introductionmentioning

confidence: 99%

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

Zhou

Chen

et al. 2020

Quality & Reliability Eng

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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.

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Degradation Trend Prognostics for Rolling Bearing Using Improved R/S Statistic Model and Fractional Brownian Motion Approach

Cited by 25 publications

References 43 publications

Probability-Based Algorithm for Bearing Diagnosis with Untrained Spall Sizes

Probability-Based Algorithm for Bearing Diagnosis with Untrained Spall Sizes

Intelligent Prognostics of Degradation Trajectories for Rotating Machinery Based on Asymmetric Penalty Sparse Decomposition Model

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

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