A hybrid of nonlinear autoregressive model with exogenous input and autoregressive moving average model for long-term machine state forecasting

Pham, Hong Thom; Tran, Van Tung; Yang, Bo‐Suk

doi:10.1016/j.eswa.2009.10.020

Cited by 65 publications

(30 citation statements)

References 19 publications

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“…The results obtained by Zhang were better in the case of mixed models, than those obtained with each model individually. Several authors have relied on Zhang's models, or have developed models based on the same principle of hybridisation [44]; [44]; [45]; [46].…”

Section: Forecasting With the Arima And Anns Modelsmentioning

confidence: 99%

Automobile spare-parts forecasting: A comparative study of time series methods

Vargas¹,

Cortés²

2017

Int. J. Automot. Mech. Eng.

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In Mexico, the automotive industry is considered to be strategic in the industrial and economic development of the country because it generates production, employment and foreign exchange. Good demand forecasts are needed for better manufacturing management. The time series modelling tools applied to the monthly demand forecasting of automobile spare parts in Mexico are assessed, for the case of a transnational enterprise, considering affordability. The classic methods of moving averages, final value and exponential smoothing, the prestigious autoregressive integrated models of moving averages (ARIMA), the rarely implemented artificial neural networks (ANNs) and the very little explored ARIMA-ANNs hybrid models are compared. A good performance of the models involving ANNs is observed, but they were not as steady as the ARIMA models in the post-sample periods. The mean absolute percentage error (MAPE) was reduced from an original 57% to 32.65%. The obtained results could help demonstrate the importance of improving industrial forecasting methodologies for better planning.

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Section: Forecasting With the Arima And Anns Modelsmentioning

confidence: 99%

Automobile spare-parts forecasting: A comparative study of time series methods

Vargas¹,

Cortés²

2017

Int. J. Automot. Mech. Eng.

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“…Pham et al [24] presented an improvement of hybrid of nonlinear autoregressive with exogenous input (NARX) model and autoregressive moving average model for long-term machine state forecasting based on vibration data. Shafie-khah et al [25], based on wavelet transform, autoregressive integrated moving average, and radial basis function neural networks (RBFN), proposed a novel hybrid model to forecast electricity price.…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

Hybrid Fuzzy Auto-Regressive Integrated Moving Average (FARIMAH) Model for Forecasting the Foreign Exchange Markets

Khashei¹,

Rafiei²,

Bijari³

2013

IJCIS

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Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Fuzzy autoregressive integrated moving average (FARIMA) models are the fuzzy improved version of the autoregressive integrated moving average (ARIMA) models, proposed in order to overcome limitations of the traditional ARIMA models; especially data limitation, and yield more accurate results. However, the forecasted interval of the FARIMA models may be very wide in some specific Circumstances. For instance, when data has high volatility or includes a significant difference or outliers. In this paper, a new hybrid model of FARIMA models is proposed by combining with probabilistic neural classifiers, called FARIMAH, in order to yield a more general and more accurate model than FARIMA models for financial forecasting in incomplete data situations. The main idea of the proposed model is based on this fact that the distribution of the actual values in the forecasted interval by FARIMA is not uniform. Thus, by detecting the spaces with more probability for actual values using the probabilistic classifier, narrower interval than traditional FARIMA models can be obtained. Empirical results of exchange rate markets forecasting indicate that the proposed model exhibit effectively improved forecasting accuracy, so it can be used as an alternative model to exchange rate forecasting, especially when the scant data made available over a short span of time.

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“…Among them, vibration is most commonly used because it is easy to measure and analyze. Besides, it can reflect the status of bearings roundly and timely [6]. It mainly takes two steps to realize accurate RUL prediction of bearings in vibration-based methods.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of Bearing with Vibration Signals Based on a Novel Indicator

Qiu

2017

Shock and Vibration

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Aiming at reducing the production downtime and maintenance cost, prognostics and health management (PHM) of rotating machinery often includes the remaining useful life (RUL) prediction of bearings. In this paper, a method combining the generalized Weibull failure rate function (WFRF) and radial basis function (RBF) neural network is developed to deal with the RUL prediction of bearings. A novel indicator, namely, the power value on the sensitive frequency band (SFB), is proposed to track bearing degradation process. Generalized WFRF is used to fit the degradation indicator series to reduce the effect of noise and avoid areas of fluctuation in the time domain. RBF neural network is employed to predict the RUL of bearings with times and fitted power values at present and previous inspections as input. Meanwhile, the life percentage is selected as output. The performance of the proposed method is validated by an accelerated bearing run-to-failure experiment, and the results demonstrate the advantage of this method in achieving more accurate RUL prediction.

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A hybrid of nonlinear autoregressive model with exogenous input and autoregressive moving average model for long-term machine state forecasting

Cited by 65 publications

References 19 publications

Automobile spare-parts forecasting: A comparative study of time series methods

Automobile spare-parts forecasting: A comparative study of time series methods

Hybrid Fuzzy Auto-Regressive Integrated Moving Average (FARIMAH) Model for Forecasting the Foreign Exchange Markets

Remaining Useful Life Prediction of Bearing with Vibration Signals Based on a Novel Indicator

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