Flexible truncation method for the reliability assessment of phased mission systems with repairable components

Lu, Jiming; Lundteigen, Mary Ann; Liu, Yiliu; Xiao-yue, WU

doi:10.17531/ein.2016.2.10

Cited by 7 publications

(7 citation statements)

References 26 publications

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“…Therefore, a flexible truncation limit may be applied to BDD problem explosion. While the truncation application is necessary to model increasing number of phases, the flexible truncation allows retention of the truncation error at low level as presented by Lu et al in [27].…”

Section: Introductionmentioning

confidence: 99%

Industrial gas turbine operating parameters monitoring and data-driven prediction

Pawełczyk¹,

Fulara²,

Sepe³

et al. 2020

Eksploatacja i Niezawodność – Maintenance and Reliability

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The article reviews traditional and modern methods for prediction of gas turbine operating characteristics and its potential failures. Moreover, a comparison of Machine Learning based prediction models, including Artificial Neural Networks (ANN), is presented. The research focuses on High Pressure Compressor (HPC) recoup pressure level of 4th generation LM2500 gas generator (LM2500+G4) coupled with a 2-stage High Speed Power Turbine Module. The researched parameter is adjustable and may be used to balance net axial loads exerted on thrust bearing to ensure stable gas turbine operation, but its direct measurement is technically difficult implicating the need to indirect measurement via set of other gas turbine sensors. Input data for the research have been obtained from BHGE manufactured and monitored gas turbines and consists of real-time data extracted from industrial installations. Machine learning models trained using the data show less than 1% Mean Absolute Percentage Error (MAPE) as obtained with the use of Random Forest and Gradient Boosting Regression models. Multilayer Perceptron Artificial Neural Networks (MLP ANN) models are reviewed, and their performance checks inferior to Random Forest algorithm-based model. The importance of hyperparameter tuning and feature engineering is discussed.

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Section: Introductionmentioning

confidence: 99%

Industrial gas turbine operating parameters monitoring and data-driven prediction

Pawełczyk¹,

Fulara²,

Sepe³

et al. 2020

Eksploatacja i Niezawodność – Maintenance and Reliability

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show abstract

“…Lu [10] proposed a decomposition approach combined with continuous-time Markov chains (CTMCs) to evaluate the reliability of PMS considering both combinatorial phase requirements and repairable components. The PMS consisting of a large number of phases and repairable components is studied in [9,11]. It is assumed that the failed component can only be repaired when the system is still operating, and it can be reused only in the next phase after its restoration.…”

Section: Introductionmentioning

confidence: 99%

Reliability assessment of repairable phased-mission system by monte carlo simulation based on modular sequence-enforcing fault tree model

Liu¹,

Krämer²,

Neumann³

2020

Eksploatacja i Niezawodność – Maintenance and Reliability

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“…Alternative methods are used in such a case, e.g. reliable phase diagrams proposed by Lu et al [24] or Dong et al [9], as well as Markov processes [11,16,34], which are particularly popular in the readiness assessment. In the literature one can find models describing single means of transport, e.g.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the readiness of special vehicles using the semi-Markov model

Borucka¹,

Niewczas²,

Hasilová³

2019

Eksploatacja i Niezawodność – Maintenance and Reliability

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Flexible truncation method for the reliability assessment of phased mission systems with repairable components

Cited by 7 publications

References 26 publications

Industrial gas turbine operating parameters monitoring and data-driven prediction

Industrial gas turbine operating parameters monitoring and data-driven prediction

Reliability assessment of repairable phased-mission system by monte carlo simulation based on modular sequence-enforcing fault tree model

Forecasting the readiness of special vehicles using the semi-Markov model

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