Application of two non-linear prediction tools to the estimation of tunnel boring machine performance

Yağız, Saffet; Gökçeoğlu, Candan; Sezer, Ebru Akçapınar; İplikçi, Serdar

doi:10.1016/j.engappai.2009.03.007

Cited by 225 publications

(51 citation statements)

References 33 publications

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“…Data-driven predictors employ pattern recognition and machine learning to forecast changes in system states (Yagiz et al, 2009;Gupta and Ray, 2007). Since the last decade, more research interests in data-driven system state forecasting have shifted to the use of flexible models such as NNs (Atiya et al, 1999;Husmeier, 1999), NF systems (Jang, 1993), and recurrent neural fuzzy (RNF) systems (Liu et al, 2009).…”

Section: The Data-driven Prognostic Methodsmentioning

confidence: 99%

“…Data-driven methods use pattern recognition and machine learning to detect changes in system states (Yagiz et al, 2009;Gupta and Ray, 2007). The classical data-driven methods for nonlinear system prediction include the use of stochastic models such as the autoregressive (AR) model, the threshold AR model (Tong and Lim, 1980), the bilinear model (Subba, 1981), the projection pursuit (Friedman and Stuetzle, 1981), the multivariate adaptive regression splines (Friedman, 1991), and the Volterra series expansion (Brillinger, 1970).…”

Section: Introductionmentioning

confidence: 99%

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A data-model-fusion prognostic framework for dynamic system state forecasting

Liu¹,

Wang²,

Ma³

et al. 2012

Engineering Applications of Artificial Intelligence

157

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a b s t r a c tA novel data-model-fusion prognostic framework is developed in this paper to improve the accuracy of system state long-horizon forecasting. This framework strategically integrates the strengths of the data-driven prognostic method and the model-based particle filtering approach in system state prediction while alleviating their limitations. In the proposed methodology, particle filtering is applied for system state estimation in parallel with parameter identification of the prediction model (with unknown parameters) based on Bayesian learning. Simultaneously, a data-driven predictor is employed to learn the system degradation pattern from history data so as to predict system evolution (or future measurements). An innovative feature of the proposed fusion prognostic framework is that the predicted measurements (with uncertainties) from the data-driven predictor will be properly managed and utilized by the particle filtering to further update the prediction model parameters, thereby enabling markedly better prognosis as well as improved forecasting transparency. As an application example, the developed fusion prognostic framework is employed to predict the remaining useful life of lithium ion batteries through electrochemical impedance spectroscopy tests. The investigation results demonstrate that the proposed fusion prognostic framework is an effective forecasting tool that can integrate the strengths of both the data-driven method and the particle filtering approach to achieve more accurate state forecasting.

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Section: The Data-driven Prognostic Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A data-model-fusion prognostic framework for dynamic system state forecasting

Liu¹,

Wang²,

Ma³

et al. 2012

Engineering Applications of Artificial Intelligence

157

View full text Add to dashboard Cite

show abstract

“…In the NLMR technique, both nonlinear and linear relationships, e.g., exponential, logarithmic, and power, can be employed. The NLMR approach is used for the establishment of mathematical formulas to make a prediction on dependent variables based on known independent variables in the geotechnical engineering field (Yagiz et al 2009;Yagiz and Gokceoglu 2010;Shirani Faradonbeh et al 2015). Since GEP is conceptually non-linear, NLMR model is selected to develop PPV predictive model for comparison purpose.…”

Section: Ppv Prediction By Nlmr Modelmentioning

confidence: 99%

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Faradonbeh

Armaghani

Majid

et al. 2016

Int. J. Environ. Sci. Technol.

139

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Blasting is a widely used technique for rock fragmentation in opencast mines and tunneling projects. Ground vibration is one of the most environmental effects produced by blasting operation. Therefore, the proper prediction of blast-induced ground vibrations is essential to identify safety area of blasting. This paper presents a predictive model based on gene expression programming (GEP) for estimating ground vibration produced by blasting operations conducted in a granite quarry, Malaysia. To achieve this aim, a total number of 102 blasting operations were investigated and relevant blasting parameters were measured. Furthermore, the most influential parameters on ground vibration, i.e., burden-to-spacing ratio, hole depth, stemming, powder factor, maximum charge per delay, and the distance from the blast face were considered and utilized to construct the GEP model. In order to show the capability of GEP model in estimating ground vibration, nonlinear multiple regression (NLMR) technique was also performed using the same datasets. The results demonstrated that the proposed model is able to predict blast-induced ground vibration more accurately than other developed technique. Coefficient of determination values of 0.914 and 0.874 for training and testing datasets of GEP model, respectively show superiority of this model in predicting ground vibration, while these values were obtained as 0.829 and 0.790 for NLMR model.

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“…In general, PM can be performed using either data-driven methods or physicsbased approaches. Data-driven methods use pattern recognition and machine learning techniques to detect changes in system states [3,4]. It relies on past patterns of the degradation of similar systems to project future system states; their forecasting accuracy depends on not only the quantity but also the quality of system history data, which could be a challenging task in many real applications [2,5].…”

Section: Introductionmentioning

confidence: 99%

Particle Filter-Based Approach to Estimate Remaining Useful Life for Predictive Maintenance

Yang

Lou

Liu

et al. 2015

Current Approaches in Applied Artificial Intelligence

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Abstract. Estimation of remaining useful life (RUL) plays a vital role in performing predictive maintenance for complex systems today. However, it still remains a challenge. To address this issue, we propose a Particle filter (PF)-based method to estimate remaining useful life for predictive maintenance by employing PF technique to update the nonlinear predictive models for forecasting system states. In particular, we applied PF techniques to estimate remaining useful life by integrating data-driven modeling techniques in order to effectively perform predictive maintenance. After introducing the PF-based algorithm, the paper presents the implementation along with the experimental results through a case study of Auxiliary Power Unit (APU) starter prognostics. The results demonstrated that the developed method is useful for estimating RUL for predictive maintenance.

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Application of two non-linear prediction tools to the estimation of tunnel boring machine performance

Cited by 225 publications

References 33 publications

A data-model-fusion prognostic framework for dynamic system state forecasting

A data-model-fusion prognostic framework for dynamic system state forecasting

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Particle Filter-Based Approach to Estimate Remaining Useful Life for Predictive Maintenance

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