Predicting the Remaining Life of Propulsion System Bearings

Marble, S.; Morton, Brogan

doi:10.1109/aero.2006.1656121

Cited by 67 publications

(51 citation statements)

References 4 publications

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“…Recently, integrated prognostics methods [17][18][19][20][21][22][23][24][25][26] were developed to achieve real-time RUL prediction during the system operations by combining both condition monitoring (CM) data and physics of failure. The integrated methods usually have an updating process by assimilating observations, during which the uncertainty is expected to shrink so that the confidence increases in the predicted results.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The bearing spall propagation was investigated in Ref. [22] where Bayesian inference was applied to reduce prediction uncertainty. In recent years an increasing volume of literature was published that treated prognostic model as a dynamic system mainly because of its natural interface with real-time data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Prognostic algorithms have been proposed in a large number of publications for various industrial applications. Those algorithms are mostly either data-driven [3][4][5][6][7], physically motivated [8][9][10][11][12][13][14][15][16] or model and data integrated [17][18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Integrated Equipment Health Prognosis Considering Crack Initiation Time Uncertainty and Random Shock

Zhao

Xie

Tian

et al. 2017

Chin. J. Mech. Eng.

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With integrated equipment health prognosis, both physical models and condition monitoring data are utilized to achieve more accurate prediction of equipment remaining useful life (RUL). In this paper, an integrated prognostics method is proposed to account for two important factors which were not considered before, the uncertainty in crack initiation time (CIT) and the shock in the degradation. Prognostics tools are used for RUL prediction starting from the CIT. However, there is uncertainty in CIT due to the limited capability of existing fault detection tools, and such uncertainty has not been explicitly considered in the literature for integrated prognosis. A shock causes a sudden damage increase and creates a jump in the degradation path, which shortens the total lifetime, and it has not been considered before in the integrated prognostics framework either. In the proposed integrated prognostics method, CIT is considered as an uncertain parameter, which is updated using condition monitoring data. To deal with the sudden damage increase and reduction of total lifetime, a virtual gradual degradation path with an earlier CIT is introduced in the proposed method. In this way, the effect of shock is captured through identifying an appropriate CIT. Examples of gear prognostics are given to demonstrate the effectiveness of the proposed method.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Integrated Equipment Health Prognosis Considering Crack Initiation Time Uncertainty and Random Shock

Zhao

Xie

Tian

et al. 2017

Chin. J. Mech. Eng.

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show abstract

“…According to Marble and Morton (2005), bearing prognosis is the key to maximizing safety and asset availability while minimizing logistical costs, by allowing maintenance to be proactive rather than reactive. However, Jardine et al (2006) noted that although prognosis is much more efficient than diagnosis to achieve zero-downtime performance, diagnosis is required when fault prediction of prognosis fails and a fault occurs.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Emission-Based Prognostics of Slow Rotating Bearing Using Bayesian Techniques Under Dependent and Independent Samples

Aye

Heyns

2015

Applied Artificial Intelligence

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This study develops a novel degradation assessment index (DAI) from acoustic emission signal obtained from slow rotating bearings and integrates same into alternative Bayesian methods for the prediction of remaining useful life (RUL). The DAI is obtained by the integration of polynomial kernel principal component analysis (PKPCA), Gaussian mixture model (GMM) and exponentially weighted moving average (EWMA). The DAI is then used as inputs in several Bayesian regression models such as the multi-layer perceptron (MLP), radial basis function (RBF), Bayesian linear regression (BLR), Gaussian mixture regression (GMR) and the Gaussian process regression (GPR) for RUL prediction. The combination of the DAI with the GPR model, otherwise, known as the DAI-GPR gave the best prediction with the least error. The findings show that the GPR model is suitable and effective in the prediction of RUL of slow rotating bearings and robust to varying operating conditions. Further, the findings are also robust when the training and tests sets are obtained from dependent and independent samples. Therefore, the GPR model is found useful for monitoring the condition of machines in order to implement effective preventive rather than reactive maintenance, thereby maximizing safety and asset availability.

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“…The model based prognostic related to the use of analytical models, it provide more precise results however real systems are non-linear and degradation mechanisms are stochastic and difficult to obtain in the form of analytical models [6]. A method for health condition prediction of propulsion system bearings was developed by Marble and Morton [7] based on bearing spall propagation physical.…”

Section: Introductionmentioning

confidence: 99%

Bearings Prognostic Using Mixture of Gaussians Hidden Markov Model and Support Vector Machine

Sloukia¹,

Bouarfa²,

Medromi³

et al. 2013

IJNSA

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Prognostic of future health state relies on the estimation of the Remaining Useful Life (RUL) of physical systems or components based on their current health state. RUL can be estimated by using three main approaches: model-based, experience-based and data-driven approaches. This paper deals with a datadriven prognostics method which is based on the transformation of the data provided by the sensors into models that are able to characterize the behavior of the degradation of bearings. For this purpose, we used Support

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Predicting the Remaining Life of Propulsion System Bearings

Cited by 67 publications

References 4 publications

Integrated Equipment Health Prognosis Considering Crack Initiation Time Uncertainty and Random Shock

Integrated Equipment Health Prognosis Considering Crack Initiation Time Uncertainty and Random Shock

Acoustic Emission-Based Prognostics of Slow Rotating Bearing Using Bayesian Techniques Under Dependent and Independent Samples

Bearings Prognostic Using Mixture of Gaussians Hidden Markov Model and Support Vector Machine

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