A hybrid ARIMA–SVM model for the study of the remaining useful life of aircraft engines

Ordóñez, Celestino; Lasheras, Fernando Sánchez; Roca‐Pardiñas, Javier; Juez, Francisco Javier de Cos

doi:10.1016/j.cam.2018.07.008

Cited by 165 publications

(80 citation statements)

References 17 publications

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“…Training samples Training time [s] ARIMA SVM [6] 39.6843 -20631 -DCNN [1] 18.4480 1286.7 20631 -LSTM [5] 16.17 338 20631 714.53 DCNN [11] 12.61 273.7 17731 -WELM [5] 13.78 267.31 20631 5.04 HDNN [9] 13.017 245 20631 - The proposed approach performances are compared with a set of other recent approaches in the literature. The results from Table 2 indicate that the proposed algorithm has the ability to achieve a low score value depending on less training samples and less training time.…”

Section: Rmse Scorementioning

confidence: 99%

“…They adopted a time window features scaling as a preprocessing and appropriate features selection step to guarantee an accurate prediction of RUL. Ordóñez et al [6] proposed a hybrid autoregressive model combined with an improved SVM using genetic algorithm to build several estimation algorithms for early RUL prediction. Chen et al [7] used a SVM-based similarity approach for RUL prediction with the same C-MAPPS dataset.…”

Section: Introductionmentioning

confidence: 99%

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Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine

et al. 2020

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The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be conducted for the extraction of important information from data-driven sequences in high dimensional space in order to guarantee a reliable conclusion. In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction. Firstly, a new feature mapping technique based on stacked autoencoders is proposed to enhance features representations through an accurate reconstruction. In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance dynamic tracking ability of newly coming data. Moreover, a new updated selection strategy was developed in order to discard the unwanted data sequences and to ensure the convergence of the training model parameters to their appropriate values. The proposed approach is validated on the C-MAPSS dataset where experimental results confirm that it yields satisfactory accuracy and efficiency of the prediction model compared to other existing methods.

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Section: Rmse Scorementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine

et al. 2020

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“…Chen et al [71] proposed a nonlinear ARIMA model based on SVR (support vector regression). Zhang et al [72] combined EEMD and ARIMA to establish the EEMD-ARIMA model to predict hotel daily occupancy rate, which has obvious advantages in short-term prediction Celestino et al [73] combined ARIMA and SVM models for the remaining useful life of aircraft engines forecasting. Lee et al [74] combined ARIMA model with genetic programming to improve both models and commit the effectiveness of the new model.…”

Section: Review Of Arima Modelmentioning

confidence: 99%

Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model

Han

2019

Energies

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Forecasting energy demand is the basis for sustainable energy development. In recent years, the new discovery of East Africa’s energy has completely reversed the energy shortage, having turned the attention of the world to the East African region. Systematic research on energy forecasting in Africa, particularly in East Africa, is still relatively rare. In view of this, this study uses a variety of methods to comprehensively predict energy consumption in East Africa. Based on the traditional grey model, this study: (1) Integrated the power coefficient and metabolic principles, and then proposed non-linear metabolic grey model (NMGM) forecasting model; (2) Used Auto Regressive Integrated Moving Average Model (ARIMA) for secondary modeling, and then developed a metabolic grey model-Auto Regressive Integrated Moving Average Model (MGM-ARIMA) and non-linear metabolic grey model-Auto Regressive Integrated Moving Average Model (NMGM-ARIMA) combined models. In terms of the prediction interval, the data for 2000–2017 is a fit to the past stage, while the data for 2018–2030 is used for the prediction of the future stage. To measure the effect of the prediction, the study used the average relative error indicator to evaluate the accuracy of different models. The results indicate that: (1) Mean relative errors of NMGM, MGM-ARIMA, and NMGM-ARIMA are 2.9697%, 2.0969%, and 1.4654%, proving that each prediction model is accurate; (2) Compared with the single model, the combined model has higher precision, confirming the superiority and feasibility of model combination. After prediction, the conclusion shows that East Africa’s primary energy consumption will grow by about 4 percent between 2018 and 2030. In addition, the limitation of this study is that only single variable are considered.

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“…Adigizel [14] proposed a model to predict the effect of dust particles on photovoltaic modules using ANFIS. Ordonez [15] proposed Yifei [49] conducted a study comparing PSO and GA, and PSO was able to confirm the superior performance even at faster computation speed and variable optimization than GA.…”

Section: Introductionmentioning

confidence: 96%

Incremental Granular Model Improvement Using Particle Swarm Optimization

Yeom

Kwak

2019

Symmetry

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This paper proposes an incremental granular model (IGM) based on particle swarm optimization (PSO) algorithm. An IGM is a combination of linear regression (LR) and granular model (GM) where the global part calculates the error using LR. However, traditional CFCM clustering presents some problems because the number of clusters generated in each context is the same and a fixed value is used for fuzzification coefficient. In order to solve these problems, we optimize the number of clusters and their fuzzy numbers according to the characteristics of the data, and use natural imitative optimization PSO algorithm. We further evaluate the performance of the proposed method and the existing IGM by comparing the predicted performance using the Boston housing dataset. The Boston housing dataset contains housing price information in Boston, USA, and features 13 input variables and 1 output variable. As a result of the prediction, we can confirm that the proposed PSO-IGM shows better performance than the existing IGM.

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A hybrid ARIMA–SVM model for the study of the remaining useful life of aircraft engines

Cited by 165 publications

References 17 publications

Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine

Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine

Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model

Incremental Granular Model Improvement Using Particle Swarm Optimization

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