scholarly journals Data-Driven Method for Predicting Remaining Useful Life of Bearing Based on Bayesian Theory

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 182
Author(s):  
Tianhong Gao ◽  
Yuxiong Li ◽  
Xianzhen Huang ◽  
Changli Wang
Keyword(s):  
Bayesian Model ◽  
Prediction Method ◽  
Vibration Signal ◽  
Bayesian Theory ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Economic Losses ◽  
Mechanical Equipment ◽  
Useful Life ◽  
Open Datasets

Bearings are some of the most critical industrial parts and are widely used in various types of mechanical equipment. Bearing health status can have a significant impact on the overall equipment performance, and bearing failures often cause serious economic losses and even casualties. Thus, estimating the remaining useful life (RUL) of bearings in real time is of utmost importance. This paper proposes a data-driven RUL prediction method for bearings based on Bayesian theory. First, time-domain features are extracted from the bearing vibration signal and data are fused to build a health indicator (HI) and a state model of bearing degradation. Then, according to Bayesian theory, a Bayesian model of state parameters and bearing life is established. The parameters of the Bayesian model are updated and bearing RUL is predicted by the Metropolis–Hastings algorithm. The method was validated by the XJTU-SY bearing open datasets and the prediction results are compared with the existing methods. Accuracy of the proposed method was demonstrated.

scholarly journals Bearing Remaining Useful Life Prediction Based on Naive Bayes and Weibull Distributions

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 944
Author(s):  
Nannan Zhang ◽  
Lifeng Wu ◽  
Zhonghua Wang ◽  
Yong Guan
Keyword(s):  
Naive Bayes ◽  
Prediction Method ◽  
Naïve Bayes ◽  
Remaining Useful Life ◽  
Statistical Characteristics ◽  
Mechanical Equipment ◽  
Weibull Distributions ◽  
Health States ◽  
Testing Stage ◽  
Useful Life

Bearing plays an important role in mechanical equipment, and its remaining useful life (RUL) prediction is an important research topic of mechanical equipment. To accurately predict the RUL of bearing, this paper proposes a data-driven RUL prediction method. First, the statistical method is used to extract the features of the signal, and the root mean square (RMS) is regarded as the main performance degradation index. Second, the correlation coefficient is used to select the statistical characteristics that have high correlation with the RMS. Then, In order to avoid the fluctuation of the statistical feature, the improved Weibull distributions (WD) algorithm is used to fit the fluctuation feature of bearing at different recession stages, which is used as input of Naive Bayes (NB) training stage. During the testing stage, the true fluctuation feature of the bearings are used as the input of NB. After the NB testing, five classes are obtained: health states and four states for bearing degradation. Finally, the exponential smoothing algorithm is used to smooth the five classes, and to predict the RUL of bearing. The experimental results show that the proposed method is effective for RUL prediction of bearing.

A Stochastic Asset Life Prediction Method for Large Fleet Datasets in Big Data Environment

2015 ◽  
Author(s):  
Behrad Bagheri ◽  
David Siegel ◽  
Wenyu Zhao ◽  
Jay Lee
Keyword(s):  
Monte Carlo ◽  
Big Data ◽  
Evaluation Method ◽  
Health Management ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Engineering System ◽  
Useful Life ◽  
Data Environment

Preventing catastrophic failures is the most important task of prognostics and health management approaches in industry where Remaining Useful Life (RUL) prediction plays a significant role to schedule required preventive actions. Regarding recent advances and trends in data analysis and in Big Data environment, industries with such foreseeing approach are able to maintain their fleet of assets more efficiently with higher assurance. To address this requirement, several physics-based and data-driven methods have been developed to predict the remaining useful life of various engineering systems. In current paper, we present a simple, yet accurate stochastic method for data-driven RUL prediction of complex engineering system. The approach is constructed based on selecting the most significant parameters from raw data by using the improved distance evaluation method as feature selection algorithms. Subsequently, the health value of units is assessed by logistic regression and the assessment output is used in a Monte Carlo simulation to estimate the remaining useful life of the desired system. During Monte Carlo iterations, several features are extracted to help filtering less accurate estimations and improve the overall prediction accuracy. The proposed algorithm is validated in two ways. First of all, the accuracy of RUL prediction is measured by applying the method to 2008 PHM data challenge gas-turbine dataset. Subsequently, gradual changes in RUL prediction of a particular test unit are measured to verify the behavior of the algorithm upon availability of additional historical data.

A Fusion Prognostics Framework Based on FMMEA and Bayesian Theory

2012 ◽  
Vol 569 ◽  
pp. 703-706
Author(s):  
Yong Jian Tao
Keyword(s):  
Failure Modes ◽  
Bayesian Theory ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Maintenance Cost ◽  
System Maintenance ◽  
Useful Life ◽  
Fusion Prognostics ◽  
Traditional Approaches ◽  
Actual Life

Prognostics approaches can assess system reliability in its actual life-cycle conditions, provide advance warning of failure, and reduce system maintenance cost. In all prognostics approaches, identification of appropriate monitored parameters, which can be employed to predict imminent failure, is critical. However traditional approaches, data-driven prognostics and model-based prognostics have their limitations. This paper proposes a fusion prognostics framework, which identifies the appropriate parameters monitored by failure modes, mechanisms and effects analysis (FMMEA), and predicts system remaining useful life by data-driven prognostics based on Bayesian theory. The fusion prognostics framework leverage the strength from both approaches to provide better predictions of remain useful life.

A method based on Dempster-Shafer theory and support vector regression-particle filter for remaining useful life prediction of crusher roller sleeve

2019 ◽  
Vol 20 (1) ◽  
pp. 106
Author(s):  
Haiping Liu ◽  
Jianjun Wu ◽  
Xiang Ye ◽  
Taijian Liao ◽  
Minlin Chen
Keyword(s):  
Particle Filter ◽  
Support Vector Regression ◽  
Prediction Method ◽  
Vibration Signal ◽  
Prediction Performance ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Dempster Shafer Theory ◽  
Useful Life ◽  
Shafer Theory

In order to solve the problem of accurately predicting the remaining useful life (RUL) of crusher roller sleeve under the partially observable and nonlinear nonstationary running state, a new method of RUL prediction based on Dempster-Shafer (D-S) data fusion and support vector regression-particle filter (SVR-PF) is proposed. First, it adopts the correlation analysis to select the features of temperature and vibration signal, and subsequently utilize wavelet to denoising the features. Lastly, comparing the prediction performance of the proposed method integrates temperature and vibration signal sources to predict the RUL with the prediction performance of single source and other prediction methods. The experiment results indicate that the proposed prediction method is capable of fusing different data sources to predict the RUL and the prediction accuracy of RUL can be improved when data are less available.

scholarly journals Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2135
Author(s):  
Marcin Witczak ◽  
Marcin Mrugalski ◽  
Bogdan Lipiec
Keyword(s):  
Historical Data ◽  
Field Effect Transistors ◽  
Failure Process ◽  
Practical Method ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Data Repository ◽  
Uncertainty Interval ◽  
Useful Life ◽  
Takagi Sugeno

The paper presents a new method of predicting the remaining useful life of technical devices. The proposed soft computing approach bridges the gap between analytical and data-driven health prognostic approaches. Whilst the former ones are based on the classical exponential shape of degradation, the latter ones learn the degradation behavior from the observed historical data. As a result of the proposed fusion, a practical method for calculating components’ remaining useful life is proposed. Contrarily to the approaches presented in the literature, the proposed ensemble of analytical and data-driven approaches forms the uncertainty interval containing an expected remaining useful life. In particular, a Takagi–Sugeno multiple models-based framework is used as a data-driven approach while an exponential curve fitting on-line approach serves as an analytical one. Unlike conventional data-driven methods, the proposed approach is designed on the basis of the historical data that apart from learning is also applied to support the diagnostic decisions. Finally, the entire scheme is used to predict power Metal Oxide Field Effect Transistors’ (MOSFETs) health status. The status of the currently operating MOSFET is determined taking into consideration the knowledge obtained from the preceding MOSFETs, which went through the run-to-failure process. Finally, the proposed approach is validated with the application of real data obtained from the NASA Ames Prognostics Data Repository.

Hybrid remaining useful life prediction method. A case study on railway D-cables

2021 ◽  
pp. 107746
Author(s):  
Yu Zang ◽  
Wei Shangguan ◽  
Baigen Cai ◽  
Huasheng Wang ◽  
Michael. G. Pecht
Keyword(s):  
Life Prediction ◽  
Prediction Method ◽  
Remaining Useful Life ◽  
Useful Life
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