Data-Based Ground Fault Diagnosis of Power Cable Systems

Ahmad, Iftikhar; Mabuchi, Hiroyuki; Kano, Manabu; Hasebe, Shinji; Inoue, Yoshikazu; Uegaki, Hiroaki

doi:10.9746/jcmsi.6.290

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…A kind of ensemble learning method, i.e., boosting, was used to develop the soft-sensor for prediction of conversion efficiency of the entrained flow coal gasification process [16]. Ensemble learning methods have recently gained the attention of researchers due to their high prediction accuracy and robustness [17]. In the boosting method, several weak models were combined to form a single efficient and robust model as demonstrated in Figure 2 (revised figure from [17]).…”

Section: Fundamentals Of Modeling and Analysis Methodsmentioning

confidence: 99%

“…Ensemble learning methods have recently gained the attention of researchers due to their high prediction accuracy and robustness [17]. In the boosting method, several weak models were combined to form a single efficient and robust model as demonstrated in Figure 2 (revised figure from [17]). On each round of developing a model, the data sample difficult in learning was getting more focus through weights allotted by the boosting mechanism.…”

Section: Fundamentals Of Modeling and Analysis Methodsmentioning

confidence: 99%

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Data-Based Prediction and Stochastic Analysis of Entrained Flow Coal Gasification under Uncertainty

Ahmad

Ayub

Mohammad

et al. 2019

Sensors

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Entrained flow gasification is a commonly used method for conversion of coal into syngas. A stable and efficient operation of entrained flow coal gasification is always desired to reduce consumption of raw materials and utilities, and achieve higher productivity. However, uncertainty in the process hinders the stability and efficiency. In this work, a quantitative analysis of the effect of uncertainty on the conversion efficiency of the entrained flow gasification is performed. A data-driven, i.e., ensemble, model of the process was developed to predict conversion efficiency of the process. Then sensitivity analysis methods, i.e., Sobol and Fourier amplitude sensitivity test, were used to analyze the effect of each individual process variables on conversion efficiency. For analyzing the collective impact of uncertainty in process variables on conversion efficiency, a non-intrusive polynomial chaos expansion (PCE) method was used. The PCE predicts probability distribution of the conversion efficiency. Reliability of the process was determined on the basis of percentage of the probability distribution falling within control limits. Measured data is used to derive the control limits for off-line reliability analysis. For on-line reliability analysis of the process, measured data is not available so a just-in-time method, i.e., k–d tree, was used. The k–d tree searches the nearest neighbor sample from a database of historical data to determine the control limits.

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Section: Fundamentals Of Modeling and Analysis Methodsmentioning

confidence: 99%

Section: Fundamentals Of Modeling and Analysis Methodsmentioning

confidence: 99%

Data-Based Prediction and Stochastic Analysis of Entrained Flow Coal Gasification under Uncertainty

Ahmad

Ayub

Mohammad

et al. 2019

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Boosting is based on the idea of developing a robust model by combining several weak models [24]. The concept of boosting is demonstrated in Figure 3 [25]. The models are developed in a series of rounds where the focus on incorrectly predicted target samples is increased with the help of increasing their respective weight.…”

Section: Soft-sensor Developmentmentioning

confidence: 99%

Data-Based Sensing and Stochastic Analysis of Biodiesel Production Process

et al. 2018

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Biodiesel production is a field of outstanding prospects due to the renewable nature of its feedstock and little to no overall CO 2 emissions to the environment. Data-based soft sensors are used in realizing stable and efficient operation of biodiesel production. However, the conventional data-based soft sensors cannot grasp the effect of process uncertainty on the process outcomes. In this study, a framework of data-based soft sensors was developed using ensemble learning method, i.e., boosting, for prediction of composition, quantity, and quality of product, i.e., fatty acid methyl esters (FAME), in biodiesel production process from vegetable oil. The ensemble learning method was integrated with the polynomial chaos expansion (PCE) method to quantify the effect of uncertainties in process variables on the target outcomes. The proposed modeling framework is highly accurate in prediction of the target outcomes and quantification of the effect of process uncertainty.

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A high-speed D-CART online fault diagnosis algorithm for rotor systems

Deng

Diao

et al. 2019

Appl Intell

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Data-Based Ground Fault Diagnosis of Power Cable Systems

Cited by 8 publications

References 19 publications

Data-Based Prediction and Stochastic Analysis of Entrained Flow Coal Gasification under Uncertainty

Data-Based Prediction and Stochastic Analysis of Entrained Flow Coal Gasification under Uncertainty

Data-Based Sensing and Stochastic Analysis of Biodiesel Production Process

A high-speed D-CART online fault diagnosis algorithm for rotor systems

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