Aircraft APU failure rate prediction based on improved Weibull-based GRP

Zhang, Yujie; Yu, Peng; Wang, Peng; Wang, Lulu; Wang, Shaonian; Liao, Haitao

doi:10.1109/phm.2017.8079158

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…The statistical model is based on strict statistical theory and historical data information, which is used to extract the correlation between relevant variables or explanatory variables, and to establish and predict the model by statistical methods. Statistical models include regression analysis model [ 1 ], time series model (ARMA [ 2 ], SARIMA [ 3 ]), mathematical statistics model [ 4 ], Weibull statistical distribution model [ 5 ], Bayesian model [ 6 ], etc. The statistical model is characterized by a physical model to find the mapping relationship between the current state and future faults.…”

Section: Literature Review Of Aircraft Failure Rate Predictionmentioning

confidence: 99%

“…Regression analysis [1], time series [2,3], mathematical statistics [4], Weibull distribution statistics [5], Bayesian [6] Grey model GM (1, 1) [7][8][9], Verhulst [10] Machine learning model Artificial neural network (ANN) [11], BP neural network [12][13][14], generalized regression neural network (GRNN) [15], support vector machine (SVM) [16], least squares support vector machine (LS-SVM) [17], random forest [18] Deep learning model Long short-term memory (LSTM) [19], convolutional neural network (CNN) [20] Combined model…”

Section: Statistical Modelmentioning

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: Statistical Modelmentioning

confidence: 99%

Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting

Zhang

2022

Computational Intelligence and Neuroscience

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“…Li and Kang [29] used the autoregressive moving average (ARMA) model to predict the failure rate. Zhang et al [30] proposed the generalized regression equation based on Weibull distribution to predict the failure of aircraft auxiliary power unit and verified the performance of the proposed model using the three-year dataset provided by China Southern Airlines. When the statistical model encounters large changes in the outside world, it often has large deviations, resulting in low accuracy.…”

Section: Introductionmentioning

confidence: 97%

Aircraft Failure Rate Prediction Method Based on CEEMD and Combined Model

Hou

2022

Scientific Programming

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Accurate prediction of aircraft failure rate can improve flight safety and spare parts supply efficiency and effectively provide good maintenance and maintenance decisions and health management guidance. In order to achieve accurate prediction of non-linear and non-stationary aircraft failure rate, an aircraft failure rate prediction method based on the fusion of complementary ensemble empirical mode decomposition (CEEMD) and combined model is proposed. Firstly, the complementary set empirical mode is used to decompose the failure rate into multiple components with different frequencies, then the integrated moving average autoregressive model (ARIMA) model and grey Verhulst model are selected to predict different components, the entropy weight method is used to solve the coefficients of the combined model, and finally the prediction results of each prediction model are multiplied by their respective weight coefficients to obtain the final prediction results. The experiment was carried out by taking the actual case application of the failure rate data of the aircraft fuel control system as an example. Seven evaluation functions are used as evaluation criteria to evaluate the performance of the combined model. Experimental results show that the developed combined model is better than other models such as sum of squared error (SSE) and mean absolute error (MAE), which can significantly improve the prediction accuracy of aircraft failure rate. It is proved that the model can improve the accuracy and effectiveness of aircraft failure rate prediction. At the same time, the stability of the model has certain advantages over other models and has a good application prospect.

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“…By using the related stochastic equations and practical parameters, the quantitative relationship of the condition monitoring data can be formulated as the parametric model. Zhang et al [17] proposed the improved Weibull-based generalized renewal process model to predict the aircraft APU failure rate. Wang et al [18] proposed an anomaly detection method based on a relevance vector machine to detect the anomalous exhaust gas temperature (EGT) data of the APU.…”

Section: Introductionmentioning

confidence: 99%

Performance Sensing Data Prediction for an Aircraft Auxiliary Power Unit Using the Optimized Extreme Learning Machine

Liu

Wang²,

Guo

et al. 2019

Sensors

Self Cite

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The aircraft auxiliary power unit (APU) is responsible for environmental control in the cabin and the main engines starting the aircraft. The prediction of its performance sensing data is significant for condition-based maintenance. As a complex system, its performance sensing data have a typically nonlinear feature. In order to monitor this process, a model with strong nonlinear fitting ability needs to be formulated. A neural network has advantages of solving a nonlinear problem. Compared with the traditional back propagation neural network algorithm, an extreme learning machine (ELM) has features of a faster learning speed and better generalization performance. To enhance the training of the neural network with a back propagation algorithm, an ELM is employed to predict the performance sensing data of the APU in this study. However, the randomly generated weights and thresholds of the ELM often may result in unstable prediction results. To address this problem, a restricted Boltzmann machine (RBM) is utilized to optimize the ELM. In this way, a stable performance parameter prediction model of the APU can be obtained and better performance parameter prediction results can be achieved. The proposed method is evaluated by the real APU sensing data of China Southern Airlines Company Limited Shenyang Maintenance Base. Experimental results show that the optimized ELM with an RBM is more stable and can obtain more accurate prediction results.

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Aircraft APU failure rate prediction based on improved Weibull-based GRP

Cited by 7 publications

References 14 publications

Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting

Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting

Aircraft Failure Rate Prediction Method Based on CEEMD and Combined Model

Performance Sensing Data Prediction for an Aircraft Auxiliary Power Unit Using the Optimized Extreme Learning Machine

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