Identification of delay factors in C-130 aircraft overhaul and finding solutions through data analysis

Khan, Nasrullah; Manarvi, Irfan Anjum

doi:10.1109/aero.2011.5747591

Cited by 5 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In one of the landmark papers, Kaiming He and others recognized the downfalls of the typically used ReLUok activation function and proposed the alternative which is improvement over Leaky ReLU and demonstrating improvement in image classification error neural network [13]. Thus, the used activation function is as in formula (18):…”

Section: Neural Network -Multilayer Perceptrons (Mlp)mentioning

confidence: 99%

“…A modern aircraft's turbofan engine is a complex mechanical system with numerous components that need to be properly maintained to continue its safe and profitable operation. As the components deteriorate they need to be replaced or repaired which drive the engine off wing for often time consuming overhaul [8] and creates a cost burden requiring proper engine fleet management to continue the aircraft operation [18].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predictive modelling of turbofan engine components condition using machine and deep learning methods

Matuszczak¹,

Żbikowski²,

Teodorczyk³

2021

Eksploatacja i Niezawodność – Maintenance and Reliability

View full text Add to dashboard Cite

The article proposes an approach based on deep and machine learning models to predict a component failure as an enhancement of condition based maintenance scheme of a turbofan engine and reviews currently used prognostics approaches in the aviation industry. Component degradation scale representing its life consumption is proposed and such collected condition data are combined with engines sensors and environmental data. With use of data manipulation techniques, a framework for models training is created and models' hyperparameters obtained through Bayesian optimization. Models predict the continuous variable representing condition based on the input. Best performed model is identified by detemining its score on the holdout set. Deep learning models achieved 0.71 MSE score (ensemble meta-model of neural networks) and outperformed significantly machine learning models with their best score at 1.75. The deep learning models shown their feasibility to predict the component condition within less than 1 unit of the error in the rank scale.

show abstract

Section: Neural Network -Multilayer Perceptrons (Mlp)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%