Browsing by Author "Pilario, Karl Ezra"
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Item Open Access Development of a real-time objective gas-liquid flow regime identifier using kernel methods(IEEE, 2019-04-22) Eyo, Edem; Pilario, Karl Ezra; Lao, Liyun; Falcone, GioiaCurrently, flow regime identification for closed channels have mainly been direct subjective methods. This presents a challenge when dealing with opaque test sections of the pipe or at gas-liquid flow rates where unclear regime transitions occur. In this paper, we develop a novel real-time objective flow regime identification tool using conductance data and kernel methods. Our experiments involve a flush mounted conductance probe that collects voltage signals across a closed channel. The channel geometry is a horizontal annulus, which is commonly found in many industries. Eight distinct flow regimes were observed at selected gas-liquid flow rate settings. An objective flow regime identifier was then trained by learning a mapping between the probability density function (PDF) of the voltage signals and the observed flow regimes via kernel principal components analysis (KPCA) and multi-class Support Vector Machine (SVM). The objective identifier was then applied in real-time by processing a moving time-window of voltage signals. Our approach has: (a) achieved more than 90% accuracy against visual observations by an expert for static test data; (b) successfully visualized conductance data in 2-dimensional space using virtual flow regime maps, which are useful for tracking flow regime transitions; and, (c) introduced an efficient real-time automatic flow regime identifier, with only conductance data as inputsItem Open Access Identification of gas-liquid flow regimes using a non-intrusive Doppler ultrasonic sensor and virtual flow regime maps(Elsevier, 2019-05-17) Nnabuife, Godfrey; Pilario, Karl Ezra; Lao, Liyun; Cao, Yi; Shafiee, MahmoodThe accurate prediction of flow regimes is vital for the analysis of behaviour and operation of gas/liquid two-phase systems in industrial processes. This paper investigates the feasibility of a non-radioactive and non-intrusive method for the objective identification of two-phase gas/liquid flow regimes using a Doppler ultrasonic sensor and machine learning approaches. The experimental data is acquired from a 16.2-m long S-shaped riser, connected to a 40-m horizontal pipe with an internal diameter of 50.4 mm. The tests cover the bubbly, slug, churn and annular flow regimes. The power spectral density (PSD) method is applied to the flow modulated ultrasound signals in order to extract frequency-domain features of the two-phase flow. Principal Component Analysis (PCA) is then used to reduce the dimensionality of the data so as to enable visualisation in the form of a virtual flow regime map. Finally, a support vector machine (SVM) is deployed to develop an objective classifier in the reduced space. The classifier attained 85.7% accuracy on training samples and 84.6% accuracy on test samples. Our approach has shown the success of the ultrasound sensor, PCA-SVM, and virtual flow regime maps for objective two-phase flow regime classification on pipeline-riser systems, which is beneficial to operators in industrial practice. The use of a non-radioactive and non-intrusive sensor also makes it more favorable than other existing techniques.Item Open Access Mixed kernel canonical variate dissimilarity analysis for incipient fault monitoring in nonlinear dynamic processes(Elsevier, 2018-12-25) Pilario, Karl Ezra; Cao, Yi; Shafiee, MahmoodIncipient fault monitoring is becoming very important in large industrial plants, as the early detection of incipient faults can help avoid major plant failures. Recently, Canonical Variate Dissimilarity Analysis (CVDA) has been shown to be an efficient technique for incipient fault detection, especially under dynamic process conditions. CVDA can be extended to nonlinear processes by introducing kernel-based learning. Incipient fault monitoring requires kernels with both good interpolation and extrapolation abilities. However, conventional single kernels only exhibit one ability or the other, but not both. To overcome this drawback, this study presents a Mixed Kernel CVDA method for incipient fault monitoring in nonlinear dynamic processes. Due to the use of mixed kernels, both enhanced detection sensitivity and a better depiction of the growing fault severity in the monitoring charts are achieved. Looking ahead, this work takes a step towards understanding the impact of kernel behavior in process monitoring performance.Item Open Access A review of kernel methods for feature extraction in nonlinear process monitoring(MDPI, 2019-12-23) Pilario, Karl Ezra; Shafiee, Mahmood; Cao, Yi; Lao, Liyun; Yang, Shuang-HuaKernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries.Item Open Access Structural reliability assessment of offshore wind turbine support structures subjected to pitting corrosion‐fatigue: A damage tolerance modelling approach(Wiley, 2020-07-12) Shittu, Abdulhakim Adeoye; Mehmanparast, Ali; Shafiee, Mahmood; Kolios, Athanasios; Hart, Phil; Pilario, Karl EzraThe structural integrity of offshore wind turbine (OWT) support structures is affected by one of the most severe damage mechanisms known as pitting corrosion‐fatigue. In this study, the structural reliability of such structures subjected to pitting corrosion‐fatigue is assessed using a damage tolerance modelling approach. A probabilistic model that ascertains the reliability of the structure is presented, incorporating the randomness in cyclic load and corrosive environment as well as uncertainties in shape factor, pit size and aspect ratio. A non‐intrusive formulation is proposed consisting of a sequence of steps. First, a stochastic parametric Finite Element Analysis (FEA) is performed using SMART© crack growth and Design Xplorer© facilities within the software package ANSYS. Secondly, the results obtained from the FEA are processed using an Artificial Neural Network (ANN) response surface modelling technique. Finally, the First Order Reliability Method (FORM) is used to calculate the reliability indices of components. The results reveal that for the inherent stochastic conditions, the structure becomes unsafe after the 18th year, before the attainment of the design life of 20 years. The FEA results are in very good agreement with results obtained from analysis steps outlined in design standard BS 7910 and other references designated as ‘theoretical analysis methods’ in this study. The results predict, for the case study, that the pit growth life is approximately 56% of the total pitting corrosion fatigue life. Sensitivity analysis results show that the aspect ratio of pits at critical size plays a significant role on the reliability of the structure.