Prognosis of machine health condition using neuro-fuzzy systems

Wang, Wilson Q.; Golnaraghi, M. F.; Ismail, F.

doi:10.1016/s0888-3270(03)00079-7

Cited by 280 publications

(180 citation statements)

References 11 publications

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“…Thus, it can be directly applied to generate ARMA model without necessitating higher order of differencing. Basing on ACF, PACF and experimental results, ARMA (3,4) model for envelope acceleration data and ARMA (3, 3) for peak acceleration data are chosen in this study. Furthermore, MLE is used to estimate the model parameters ϕ i , φ i .…”

Section: Resultsmentioning

confidence: 99%

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

Pham

Tran

Yang

2010

Expert Systems with Applications

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This paper presents an improvement of hybrid of nonlinear autoregressive with exogenous input (NARX) and autoregressive moving average (ARMA) for long-term machine state forecasting based on vibration data. In this study, vibration data is considered as a combination of two components which are deterministic data and error. The deterministic component may describe the degradation index of machine, whilst the error component can depict the appearance of uncertain parts. An improved hybrid forecasting model, namely NARX-ARMA model, is carried out to obtain the forecasting results in which NARX network model which is suitable for nonlinear issue is used to forecast the deterministic component and ARMA model are used to predict the error component due to appropriate capability in linear prediction. The final forecasting results are the sum of the results obtained from these single models. The performance of the NARX-ARMA model is then evaluated by using the data of low methane compressor acquired from condition monitoring routine. In order to corroborate the advances of the proposed method, a comparative study of the forecasting results obtained from NARX-ARMA model and traditional models is also carried out. The comparative results show that NARX-ARMA model is outstanding and could be used as a potential tool to machine state forecasting.

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Section: Resultsmentioning

confidence: 99%

“…However, data-driven based techniques, which are frequently based on artificial intelligence, can flexibly generate the forecasting models regardless of the complexity of system. Therefore, these techniques that some of those have been proposed in references [1][2][3][4][5] are the first selection of researchers'…”

Section: Introductionmentioning

confidence: 99%

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

Pham

Tran

Yang

2010

Expert Systems with Applications

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“…NFs are computationally effective techniques and are thereby well suited for practical problems, where it is easier to gather data than to formalize the behavior of the system being studied. Actual developments confirm the interest of using NFs in forecasting applications [7,8]. (Note that the purpose of the authors is not to present NFs systems as the single tools for prognostics, but as adequate to face with the implementation restriction and requirements.…”

Section: Introductionmentioning

confidence: 80%

A neuro-fuzzy self built system for prognostics: a way to ensure good prediction accuracy by balancing complexity and generalization

El-Koujok

Gouriveau

Zerhouni

2010

2010 Prognostics and System Health Management Conference

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“…Most of these techniques use artificial intelligence which can generate the flexible and appropriate models for almost failure modes. Consequently, data-driven approaches that some of those have been proposed in references [4][5][6][7] are firstly examined.…”

Section: Fig 1 Fidelity Of Prognostic Approachesmentioning

confidence: 99%

Data-driven approach to machine condition prognosis using least square regression tree

Tran

Yang

2009

J Mech Sci Technol

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Machine fault prognosis techniques have been profoundly considered in the recent time due to their substantial profit for reducing unexpected faults or unscheduled maintenance. With those techniques, the working conditions of components, the trending of fault propagation, and the time-to-failure are precisely forecasted before they reach the failure thresholds. In this work, we propose an approach of Least Square Regression Tree (LSRT), which is an extension of the Classification and Regression Tree (CART), in association with one-step ahead prediction of time-series forecasting techniques to predict the future condition of machine. In this technique, the number of available observations is firstly determined by using Cao's method and LSRT is employed as prediction model in the next step. The proposed approach is evaluated by real data of low methane compressor. Furthermore, a comparison of the predicted results obtained from CART and LSRT are carried out to prove the accuracy. The predicted results show that LSRT offers a potential for machine condition prognosis.

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Prognosis of machine health condition using neuro-fuzzy systems

Cited by 280 publications

References 11 publications

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

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

A neuro-fuzzy self built system for prognostics: a way to ensure good prediction accuracy by balancing complexity and generalization

Data-driven approach to machine condition prognosis using least square regression tree

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