Metabolism and difference iterative forecasting model based on long-range dependent and grey for gearbox reliability

Li, He; Song, Wanqing; Zio, Enrico

doi:10.1016/j.isatra.2021.05.002

Cited by 7 publications

(3 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering the two auxiliary parameters θ = 1 and θ 0 , one can solve Equations ( 35) and (36) to obtain the skewness index β and the drift coefficient µ.…”

Section: 𝜑(𝜃; 𝛼 𝛽 𝜇 𝛿)| = 𝐸 𝑒mentioning

confidence: 99%

“…The corresponding results of RUL are shown in Figure 9. We use four standard error evaluation metrics to compare the predictive accuracy: mean absolute error, root mean square error, mean absolute percentage error, and Health Degree [36]. We use four standard error evaluation metrics to compare the predictive accuracy: mean absolute error, root mean square error, mean absolute percentage error, and Health Degree [36].…”

Section: Prediction Of Remaining Useful Lifementioning

confidence: 99%

See 1 more Smart Citation

Predicting the Remaining Useful Life of Turbofan Engines Using Fractional Lévy Stable Motion with Long-Range Dependence

Qi,

Zhu,

Yao

et al. 2024

Fractal Fract

Self Cite

View full text Add to dashboard Cite

Remaining useful life prediction guarantees a reliable and safe operation of turbofan engines. Long-range dependence (LRD) and heavy-tailed characteristics of degradation modeling make this method advantageous for the prediction of RUL. In this study, we propose fractional Lévy stable motion for degradation modeling. First, we define fractional Lévy stable motion simulation algorithms. Then, we demonstrate the LRD and heavy-tailed property of fLsm to provide support for the model. The proposed method is validated with the C-MAPSS dataset obtained from the turbofan engine. Principle components analysis (PCA) is conducted to extract sources of variance. Experimental data show that the predictive model based on fLsm with exponential drift exhibits superior accuracy relative to the existing methods.

show abstract

“…Considering the two auxiliary parameters θ = 1 and θ 0 , one can solve Equations ( 35) and (36) to obtain the skewness index β and the drift coefficient µ.…”

Section: 𝜑(𝜃; 𝛼 𝛽 𝜇 𝛿)| = 𝐸 𝑒mentioning

confidence: 99%

Section: Prediction Of Remaining Useful Lifementioning

confidence: 99%

Predicting the Remaining Useful Life of Turbofan Engines Using Fractional Lévy Stable Motion with Long-Range Dependence

Qi,

Zhu,

Yao

et al. 2024

Fractal Fract

Self Cite

View full text Add to dashboard Cite

show abstract

“…Reliable gearbox prediction is a complex problem. To overcome the gearbox reliability problem, a hybrid model based on the fractional Lévy stable motion, the gray model and the metabolism method was proposed [8]. In this model, the feature extraction method is used to reveal gearbox degradation and to solve gearbox insensitivity to weak faults.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Time-Series Framework for Daily-Based PM_2.5 Forecasting

Chiang

Horng

2021

IEEE Access

View full text Add to dashboard Cite

The impact of fine particulate matter on health has captured attention worldwide. Many studies have proven that fine particulate matter harms the respiratory system and the cardiovascular system. To prevent people from being harmed, many scientific research studies on PM 2.5 prediction have been conducted in recent years. Accurate PM 2.5 forecasting can not only alert people to stay away from concentrated areas but also provide the government with environmental policies in the future. In this paper, we propose a hybrid time-series prediction framework for daily-based PM 2.5 forecasting. The proposed framework consists of three components: the autoencoder, the dilated convolutional neural network, and the gated recurrent unit. The experimental dataset with 76 monitoring stations from the Taiwan Environmental Protection Administration is applied for comparison of the baseline and the proposed models. The proposed model is not only for the specified city-/county-wide region but also for the particular monitoring station/site to predict PM 2.5 concentration. By considering air quality data, meteorological data, and geographical data simultaneously, the proposed model can increase the accuracy of PM 2.5 prediction. In addition, the proposed PM 2.5 forecasting model can learn the location-centric spatial features and the dailybased temporal features simultaneously. The experimental results show that the prediction accuracy of the proposed model is superior to those of the baseline models.INDEX TERMS autoencoder, dilated convolutional neural network, gated recurrent unit, PM 2.5 forecasting

show abstract

On the prediction of power outage length based on linear multifractional Lévy stable motion

Song,

Deng,

Cattani

et al. 2024

Pattern Recognition Letters

View full text Add to dashboard Cite

Metabolism and difference iterative forecasting model based on long-range dependent and grey for gearbox reliability

Cited by 7 publications

References 42 publications

Predicting the Remaining Useful Life of Turbofan Engines Using Fractional Lévy Stable Motion with Long-Range Dependence

Predicting the Remaining Useful Life of Turbofan Engines Using Fractional Lévy Stable Motion with Long-Range Dependence

Hybrid Time-Series Framework for Daily-Based PM_2.5 Forecasting

On the prediction of power outage length based on linear multifractional Lévy stable motion

Contact Info

Product

Resources

About

Metabolism and difference iterative forecasting model based on long-range dependent and grey for gearbox reliability

Cited by 7 publications

References 42 publications

Predicting the Remaining Useful Life of Turbofan Engines Using Fractional Lévy Stable Motion with Long-Range Dependence

Predicting the Remaining Useful Life of Turbofan Engines Using Fractional Lévy Stable Motion with Long-Range Dependence

Hybrid Time-Series Framework for Daily-Based PM2.5 Forecasting

On the prediction of power outage length based on linear multifractional Lévy stable motion

Contact Info

Product

Resources

About

Hybrid Time-Series Framework for Daily-Based PM_2.5 Forecasting