An Implementation of Prognosis with Dynamic Bayesian Networks

Przytula, K. Wojtek; Choi, Arthur

doi:10.1109/aero.2008.4526616

Cited by 10 publications

(7 citation statements)

References 20 publications

(21 reference statements)

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“…More recently, Dynamic Bayesian Networks (DBNs) [35], a tool generalizing the HMMs and the Kalman filter, have been exploited to perform failure prognostics. Prytzula and Choi [36] proposed an integrated DBN based diagnostic and prognostics method where the uncertainty related to the operating conditions is taken into account. Similarly, Muller et al [7] proposed a DBN based procedure, integrating both the degradation mechanism and the maintenance actions in the same model.…”

Section: ) Model-based Prognosticsmentioning

confidence: 99%

A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models

et al. 2012

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This paper addresses a data-driven prognostics method for the estimation of the Remaining Useful Life (RUL) and the associated confidence value of bearings. The proposed method is based on the utilization of the Wavelet Packet Decomposition (WPD) technique, and the Mixture of Gaussians Hidden Markov Models (MoG-HMM). The method relies on two phases: an off-line phase, and an on-line phase. During the first phase, the raw data provided by the sensors are first processed to extract features in the form of WPD coefficients. The extracted features are then fed to dedicated learning algorithms to estimate the parameters of a corresponding MoG-HMM, which best fits the degradation phenomenon. The generated model is exploited during the second phase to continuously assess the current health state of the physical component, and to estimate its RUL value with the associated confidence. The developed method is tested on benchmark data taken from the "NASA prognostics data repository" related to several experiments of failures on bearings done under different operating conditions. Furthermore, the method is compared to traditional time-feature prognostics and simulation results are given at the end of the paper. The results of the developed prognostics method, particularly the estimation of the RUL, can help improving the availability, reliability, and security while reducing the maintenance costs. Indeed, the RUL and associated confidence value are relevant information which can be used to take appropriate maintenance and exploitation decisions. In practice, this information may help the maintainers to prepare the necessary material and human resources before the occurrence of a failure. Thus, the traditional maintenance policies involving corrective and preventive maintenance can be replaced by condition based maintenance.

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Section: ) Model-based Prognosticsmentioning

confidence: 99%

A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models

et al. 2012

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“…More recently, Dynamic Bayesian Networks [11], a tool generalizing the HMMs and the Kalman filter, have been exploited to perform failure prognostic. Prytzula and Choi [32] proposed an integrated DBNs based diagnostic and prognostic method where the uncertainty related to the operating conditions is taken into account. Similarly, Muller et al [12] proposed a DBNs based procedure integrating both the degradation mechanism and the maintenance actions in the same model.…”

Section: Prognostic Approachesmentioning

confidence: 99%

CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks

Diego

Medjaher

Zerhouni

2012

Mechanical Systems and Signal Processing

198

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The failure of critical components in industrial systems may have negative consequences on the availability, the productivity, the security and the environment. To avoid such situations, the health condition of the physical system, and particularly of its critical components, can be constantly assessed by using the monitoring data to perform on-line system diagnostics and prognostics. The present paper is a contribution on the assessment of the health condition of a Computer Numerical Control (CNC) tool machine and the estimation of its Remaining Useful Life (RUL). The proposed method relies on two main phases: an off-line phase and an on-line phase. During the first phase, the raw data provided by the sensors are processed to extract reliable features. These latter are used as inputs of learning algorithms in order to generate the models that represent the wear's behavior of the cutting tool. Then, in the second phase, which is an assessment one, the constructed models are exploited to identify the tool's current health state, predict its RUL and the associated confidence bounds. The proposed method is applied on a benchmark of condition monitoring data gathered during several cuts of a CNC tool. Simulation results are obtained and discussed at the end of the paper.

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“…The developed methodology highlighted the displacement of the slope between 1 and 1.5 cm as the most likely. Furthermore, due to the importance of evaluating the operational condition of a component, subsystem, or a system, Bayesian estimation is also widely exploited for failure prognosis and diagnosis [ 31 , 32 , 33 ]. Zárate et al [ 34 ] proposed a two-phase framework to predict the length of a fatigue crack in structural elements.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Estimation for Reliability Engineering: Addressing the Influence of Prior Choice

Leoni

BahooToroody

Khalaj

et al. 2021

IJERPH

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Over the last few decades, reliability analysis has attracted significant interest due to its importance in risk and asset integrity management. Meanwhile, Bayesian inference has proven its advantages over other statistical tools, such as maximum likelihood estimation (MLE) and least square estimation (LSE), in estimating the parameters characterizing failure modelling. Indeed, Bayesian inference can incorporate prior beliefs and information into the analysis, which could partially overcome the lack of data. Accordingly, this paper aims to provide a closed-mathematical representation of Bayesian analysis for reliability assessment of industrial components while investigating the effect of the prior choice on future failures predictions. To this end, hierarchical Bayesian modelling (HBM) was tested on three samples with distinct sizes, while five different prior distributions were considered. Moreover, a beta-binomial distribution was adopted to represent the failure behavior of the considered device. The results show that choosing strong informative priors leads to distinct predictions, even if a larger sample size is considered. The outcome of this research could help maintenance engineers and asset managers in integrating their prior beliefs into the reliability estimation process.

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An Implementation of Prognosis with Dynamic Bayesian Networks

Cited by 10 publications

References 20 publications

A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models

A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models

CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks

Bayesian Estimation for Reliability Engineering: Addressing the Influence of Prior Choice

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