Model selection for degradation modeling and prognosis with health monitoring data

Nguyen, Khanh T. P.; Fouladirad, Mitra; Grall, Antoine

doi:10.1016/j.ress.2017.08.004

Cited by 57 publications

(28 citation statements)

References 45 publications

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“…These information can be evaluated using the RUL probability distribution. However, in literature, this distribution is obtained primarily based on the assumption about the degradation modeling [31][32][33][34]. In reality, especially in the case of complex systems with multiple sensor sources, it is not easy to derive the underlying degradation model.…”

Section: New Dynamic Predictive Maintenance Frameworkmentioning

confidence: 99%

“…Even if theoretical models can be built, for some stochastic deterioration processes, the RUL distribution cannot be directly obtained with the analytical approaches. The RUL estimation in these cases has to be based on the simulation techniques that could be difficult to be used in real time applications [31,32]. To overcome this situation, we present in this section a new data-driven prognostic method that directly provides the probability of the system failure without prior knowledge of the failure mechanism.…”

Section: New Dynamic Predictive Maintenance Frameworkmentioning

confidence: 99%

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A new dynamic predictive maintenance framework using deep learning for failure prognostics

Nguyen

Medjaher

2019

Reliability Engineering & System Safety

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242

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Section: New Dynamic Predictive Maintenance Frameworkmentioning

confidence: 99%

Section: New Dynamic Predictive Maintenance Frameworkmentioning

confidence: 99%

A new dynamic predictive maintenance framework using deep learning for failure prognostics

Nguyen

Medjaher

2019

Reliability Engineering & System Safety

Self Cite

242

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show abstract

“…Although various kinds of model-selection criteria have been proposed, such as minimum description length [15], Akaike information criterion [16], Bayesian information criterion [12], and empirical average log-likelihood, current model-selection criteria are not comprehensive. Usually, they primarily consider the sampling errors in parameter estimation [17]. Indeed, owing to indistinct degradation characteristics and insufficient selection criteria, a fixed degradation model may be unscientific, especially for real-time RUL prediction with incomplete degradation trajectory.…”

Section: Introductionmentioning

confidence: 99%

Online remaining-useful-life estimation with a Bayesian-updated expectation-conditional-maximization algorithm and a modified Bayesian-model-averaging method

et al. 2020

Sci. China Inf. Sci.

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Online remaining-useful-life (RUL) estimation is an effective method with respect to ensuring the safety of complex-huge systems. Generally, current methods assume a specific degradation model when degradation values are observed in the initial degradation phase. However, this assumption may not always be robust enough owing to the often-ambiguous inherent incipient-degradation characteristic. Therefore, besides model-parameter uncertainty, the uncertainty of the degradation model is worth examining in online RUL estimations. In this paper, a Bayesian-updated expectation-conditional-maximization (ECM) algorithm is adopted to address the uncertainty of prior parameters, and a modified Bayesian-model-averaging method is used to deal with the uncertainty of the degradation model. Then, simulation studies are conducted to analyze the effectiveness of the proposed fusion algorithm. Results suggest that the Bayesian-updated ECM algorithm and modified Bayesian-model-averaging method effectively address the associated uncertainties of model parameters and the degradation model itself. Finally, we apply the proposed fusion algorithm to predict the RUL of a gyroscope.

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“…Many scholars [ 2 , 3 , 4 , 5 , 6 , 7 ] have conducted extensive research on health status prediction. For example, Nguyen et al studied the selection of different degradation models using a large number of health monitoring data [ 8 ]. Most of the systems for health status prediction have been modeled based on one of several approaches: the gamma process [ 9 , 10 , 11 ], Wiener process [ 12 , 13 ], Markov process [ 14 ], general generation function [ 15 ], Monte Carlo Simulation [ 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Health Status of an Unmanned Aerial Vehicles Data-Link System Based on a Bayesian Network

Wang

Guo

Wang³

et al. 2018

Sensors

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Unmanned aerial vehicles (UAVs) require data-link system to link ground data terminals to the real-time controls of each UAV. Consequently, the ability to predict the health status of a UAV data-link system is vital for safe and efficient operations. The performance of a UAV data-link system is affected by the health status of both the hardware and UAV data-links. This paper proposes a method for predicting the health state of a UAV data-link system based on a Bayesian network fusion of information about potential hardware device failures and link failures. Our model employs the Bayesian network to describe the information and uncertainty associated with a complex multi-level system. To predict the health status of the UAV data-link, we use the health status information about the root node equipment with various life characteristics along with the health status of the links as affected by the bit error rate. In order to test the validity of the model, we tested its prediction of the health of a multi-level solar-powered unmanned aerial vehicle data-link system and the result shows that the method can quantitatively predict the health status of the solar-powered UAV data-link system. The results can provide guidance for improving the reliability of UAV data-link system and lay a foundation for predicting the health status of a UAV data-link system accurately.

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Model selection for degradation modeling and prognosis with health monitoring data

Abstract: Model selection for degradation modeling and prognosis with health monitoring data. Reliability Engineering and System Safety, Elsevier, 2018, 169, pp.

Cited by 57 publications

References 45 publications

A new dynamic predictive maintenance framework using deep learning for failure prognostics

A new dynamic predictive maintenance framework using deep learning for failure prognostics

Online remaining-useful-life estimation with a Bayesian-updated expectation-conditional-maximization algorithm and a modified Bayesian-model-averaging method

Predicting the Health Status of an Unmanned Aerial Vehicles Data-Link System Based on a Bayesian Network

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