Efficient fault detection and diagnosis of distillation column using gamma scanning



This paper presents an efficient approach for automated fault detection and diagnosis of the distillation column by analyzing the gamma intensity variations. Gamma-ray scanning is one of the most common techniques that is used in industrial radioisotopes applications. It is used to get signals that reflect the inner details of a distillation column. These signals are gamma intensity variation that comes from material density variation in the column. In the proposed approach, the signals of each distillation column are divided into frames. Each frame contains the signal of just one column tray. Therefore, the position of the defected tray can be determined exactly in the column. Discrete wavelet transform, power density spectrum, higher order statistics (HOS), area under the curve (AUC) and correlation between signals are features that are extracted to be used in artificial neural network. The proposed results prove that the selection of HOS with correlation and AUC effectively achieves the fault detection and diagnosis approach in different high noise environments.


Distillation column faults Gamma scanning Higher order statistics Bispectrum Power density spectrum Artificial neural network 


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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2018

Authors and Affiliations

  1. 1.Engineering DepartmentNuclear Research Center, Atomic Energy AuthorityCairoEgypt

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