RTD signal identification using linear and nonlinear modified periodograms

One of the important applications of radioisotope in industry is the residence time distribution (RTD) measurement. RTD can be used for optimizing the design of the industrial system at the design stage and determination of the system malfunctions. The RTD signal may be subject to different sorts of noise; this leads to errors in the RTD calculations and hence leads to wrong analysis in determination of system malfunctions. This paper presents a proposed method for RTD signal identification based on power density spectrum (PDS). The cepstral features are extracted from the signal and from its linear and nonlinear modified periodograms. The neural networks are used for training and testing the proposed method. The proposed method is tested by RTD signals obtained from measurements carried out using radiotracer technique. The experimental results show that the proposed method with features extracted from the PDS of the RTD signal calculated using nonlinear modified periodogram (multitaper) is the most robust and reliable in RTD signal identification.