Failure Rate Analysis of Boeing 737 Brakes Employing Neural Network

Al‐Garni, Ahmed Z.; Jamal, Ahmad; Saeed, Farooq; Kassem, Abdel Meguid

doi:10.2514/6.2007-7854

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…Applying this optimization method to aircraft failure rate prediction can improve the prediction performance of the original model. It includes Holt-Winters seasonal model [ 28 ], AR model of neural network residual correction [ 29 ], Weibull regression model of artificial neural network [ 30 ], Weibull-based Generalized Renewal Process (WGRP) [ 31 ], Sparse direct support vector regression machine [ 32 ], Generalized weighting least-squares combination prediction [ 33 ], and other models to predict the failure rate, which has certain prediction effect. However, the structure and parameters of the combined model are uncertain in the prediction, and different parameters and structures will have a great impact on the prediction structure.…”

Section: Literature Review Of Aircraft Failure Rate Predictionmentioning

confidence: 99%

“…Holt-winters seasonal model [28], neural network residual correction AR [29], artificial neural network Weibull regression [30], Weibull-based generalized renewal process (WGRP) [31], sparse direct support vector machine regression [32], generalized weighting least-squares combination [33] Integrated combination model based on decomposition Empirical mode decomposition (EMD) and LS-SVM combination [34], correlation vector EMD and GMDH combination [35], EMD and RVM-GM combination [36], CEEMD and combined model [37] the combination forecasting theory system in 1969, this method has been widely concerned by scholars at home and abroad. Effective combination of different prediction models can be regarded as an effective supplement to the generation process of infinitely approaching real data.…”

Section: Model-based Combination Forecastingmentioning

confidence: 99%

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Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting

Zhang

2022

Computational Intelligence and Neuroscience

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Effective prediction of aircraft failure rate has important guiding significance for formulating reasonable maintenance plans, carrying out reliable maintenance activities, improving health management levels, and ensuring the safety of aircraft flight, etc. Firstly, combining the advantages of time series model in eliminating random accidental factors interference, grey model in dealing with poor information, and the characteristics of artificial neural network in dealing with nonlinear data, the failure rate of aircraft equipment is predicted by ARIMA model, grey Verhulst model, and BP neural network model, and secondly, based on the idea of variable weight, the method of sum of squares of errors is used to reciprocate. Shapley value method and IOWA operator method determine the weighting coefficient and establish three combined forecasting models for aircraft failure rate prediction, so as to improve the accuracy of the algorithm. Finally, taking the data of actual aircraft failure rate as the research object, the performance indexes of design prediction model are judged by Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Index of Agreement (IA), Theil Inequality Coefficient (TIC), Equal Coefficient (EC), Nash-Sutcliffe Efficiency coefficient (NSE), Pearson test, and violin diagram of forecast error distribution. The experimental results show that: The forecasting precision of the combination model is better than that of the single model, and the evaluation index of combination forecasting model based on IOWA operator is better than that of other combination forecasting models, thus improving the forecasting accuracy and reliability. Compared with other typical prediction models simultaneously, it is verified that the proposed combined prediction model has strong applicability, high accuracy, and good stability, which provides a practical and effective technical method for aircraft fault prediction and has good application value.

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Section: Literature Review Of Aircraft Failure Rate Predictionmentioning

confidence: 99%

Section: Model-based Combination Forecastingmentioning

confidence: 99%

Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting

Zhang

2022

Computational Intelligence and Neuroscience

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“…Broomhead and Lowe [3] used similar multilayer nets with Radial Based Functions (RBF). Al-Garni et al [4][5][6] utilized the back propagation approaches to model the failure of some aircraft systems, including air conditioning packs, landing gear tires and brakes. In these models, the network topology and architecture played a significant role in the accuracy of the predictions.…”

Section: Introductionmentioning

confidence: 99%

Application of Back Propagation Neural Network Algorithms on Modeling Failure of B-737 Bleed Air System Valves in Desert Conditions

Abdelrahman

Al‐Garni

Al-Wadiee

2012

AMM

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Accurate life prediction of aircraft engine components is very critical because it has a direct impact on aircraft safety and on operators’ profits. The engine bleed air system valves have considerably high failure rates when the engines are operated in desert conditions because of sand particles erosion and blockage. In this work, an Artificial Neural Network (ANN) model for the prediction of failure rate of the most important of these valves in Boeing 737 engines is developed and validated. A previously developed feed-forward back-propagation algorithm is implemented to train the ANN. The effects of changing the number of neurons in the input layer, the number of neurons in the hidden layer, the rate of learning, and the momentum constant are investigated. The model results are validated using comparisons with actual valves failure data from a local operator in Saudi Arabia, as well as comparisons with classical Weibull model results.

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“…This approach implies that t o is known. (10) All the tires were analyzed. Here, the complete analysis of only one tire, i.e.…”

Section: Ann Model For Present Analysismentioning

confidence: 99%

Artificial neural network application of modeling failure rate for Boeing 737 tires

Al‐Garni

Jamal

2011

Quality & Reliability Eng

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Failure Rate Analysis of Boeing 737 Brakes Employing Neural Network

Cited by 3 publications

References 16 publications

Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting

Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting

Application of Back Propagation Neural Network Algorithms on Modeling Failure of B-737 Bleed Air System Valves in Desert Conditions

Artificial neural network application of modeling failure rate for Boeing 737 tires

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