Method for Predicting Failure Rate of Airborne Equipment Based on Optimal Combination Model

Li, WenQiang; Hou, Ning; Sun, Xichao

doi:10.1155/2021/5199982

Cited by 4 publications

(6 citation statements)

References 27 publications

(25 reference statements)

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“…For example, the [ 21 ] grey neural network and fuzzy recognition model are proposed to realize the fault prediction of avionics system, and the accuracy of the algorithm is improved by this method. Combining artificial neural network with genetic algorithm [ 22 ], it proposed constructing a combined prediction model of hybrid single model by analyzing the factors affecting the failure rate of airborne equipment based on [ 23 ] optimal combination forecast model, and the prediction performance of the combined model is verified by experiments. A combined model of [ 24 ] support vector regression (SVR), multiple regression, and principal component analysis is proposed.…”

Section: Literature Review Of Aircraft Failure Rate Predictionmentioning

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%

“…Grey neural network-fuzzy recognition [21], artificial neural network and genetics [22], MLR-GM (1, N)-PLS-BP-SVM [23],…”

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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“…16 Li et al proposed an optimal combination forecasting model that mixes five single machine learning models and showed high prediction accuracy. 17 Zhao et al proposed a multimodal method that integrates the classification ability of two neural network models so that complementary information can be identified to improve the accuracy of diagnosis results. 18 Pi et al designed a new Elman neural network (ENN) optimized by quantum-behaved adaptive particle swarm optimization (QAPSO) to achieve accurate results in aeroengine fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Method of aeroengine fault diagnosis based on a Fingerprint map

Gou

Pan³

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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The electrification and intelligentization trends of aeroengine systems present new challenges for the diagnostics of traditionally centralized control architectures. Scientific and practical fault diagnosis is of great significance for reducing aeroengine failures, improving operation efficiency, and ensuring safety. To realize accurate fault diagnosis, an aeroengine fault diagnosis method based on an improved fingerprint map is proposed in this paper. Based on aeroengine data analysis, ISTOA-SVM diagnostic model that embeds improved fingerprint map data as a priori knowledge is adopted to identify the fault sets. Data based on the statistics of the fingerprint map are processed with a ratio coefficient, and the feature parameters are appended through Floyd-principal component analysis (Floyd-PCA) to increase the identification data dimension. On the other hand, to improve the diagnostic performance of the classifier, the sooty terns optimization algorithm algorithm combined with chaotic maps, a variable helix mechanism and a separating operator is designed, and the ISTOA-SVM is constructed. The fault diagnosis method was validated by using a real-world dataset of aeroengines. Comparison experiments show that the proposed method is superior in performance over the state-of-the-art approaches in diagnostic precision under the constraint of limited data. The ISTOA-SVM addresses the issue that similar features may be confused in traditional fingerprint maps. The results are presented through 5 practical cases. The study demonstrates that the proposed method achieves superior performance and can be applied to aeroengine fault diagnosis.

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“…Generally, there are three main varieties of blueberries, including Rabbiteye blueberry, Northern Highbush blueberry, and Southern Highbush blueberry (Totad et al., 2020). In China, Rabbiteye blueberry, Northern Highbush blueberry, and Southern Highbush blueberry are all cultivated in the southern region, while Northern Highbush blueberry is mainly cultivated in the northern region (Y. Li, Pei, et al., 2021). Blueberry is rich in anthocyanins, vitamins, flavonoids, and other nutrients (Mustafa et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Modeling of soluble solid content of PE‐packaged blueberries based on near‐infrared spectroscopy with back propagation neural network and partial least squares (BP–PLS) algorithm

Chen,

Li,

Williams

et al. 2023

Journal of Food Science

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Blueberries are a nutritious and popular berry worldwide. The physical and chemical properties of blueberries constantly change through the cycle of the supply chain (from harvest to sale). The purpose of this study was to develop a rapid method for detecting the properties of packaged blueberries based on near‐infrared (NIR) spectroscopy. NIR was applied to quantitatively determine the soluble solid content (SSC) of polyethylene (PE)‐packaged blueberries. An orthogonal partial least squares discriminant analysis model was established to show the correlation between spectral data and the measured SSC. Multiplicative scattering correction, standard normal variable, Savitzky–Golay convolution first derivative, and normalization (Normalize) were used for spectra preprocessing. Uninformative variables elimination, competitive adaptive reweighted sampling, and iteratively retaining informative variables were jointly used for wavelength optimization. NIR‐based SSC prediction models for unpacked blueberries and PE‐packaged blueberries were developed using partial least squares (PLS). The prediction model for PE‐packaged samples (RP2= 0.876, root mean square error of prediction [RMSEP] = 0.632) had less precision than the model for unpacked samples (RP2= 0.953, RMSEP = 0.611). To reduce the effect of PE, the back propagation (BP) neural network and PLS were combined into the BP–PLS algorithm based on the residual learning algorithm. The model of BP–PLS (RP2= 0.947, RMSEP = 0.414) was successfully developed to improve the prediction accuracy of SSC for PE‐packaged blueberries. The results suggested a promising way of using the BP–PLS method in tandem with NIR for the rapid detection of the SSC of PE‐packaged blueberries.Practical ApplicationMost of the NIR‐based research used unpacked blueberries as samples, while the use of packaged blueberries would provide researchers with a better understanding of the crucial factors at different phases of the blueberry supply chain (from harvest to sale). To meet market demands and minimize losses, NIR spectroscopy has been proven to be a rapid and nondestructive method for the determination of the SSC of PE‐packaged blueberries. This study provides an effective method for monitoring the properties of blueberries in the entire supply chain.

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Method for Predicting Failure Rate of Airborne Equipment Based on Optimal Combination Model

Cited by 4 publications

References 27 publications

Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting

Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting

Method of aeroengine fault diagnosis based on a Fingerprint map

Modeling of soluble solid content of PE‐packaged blueberries based on near‐infrared spectroscopy with back propagation neural network and partial least squares (BP–PLS) algorithm

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