Power density spectrum for the identification of residence time distribution signals

One of the most important applications of radioisotopes in industry is the residence time distribution (RTD) measurement. RTD can be used for optimizing the design of industrial systems and determining their 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 the determination of system malfunctions. This paper presents a proposed approach for RTD signal identification based on power density spectrum (PDS). The cepstral features are extracted from the signal or/and its PDS. The PDS is estimated using nonparametric, parametric, and eigen-analysis methods. The identification results are analyzed and compared for different estimation methods in order to select the best PDS estimation method for RTD signal identification. Neural networks are used for training and testing in the proposed approach. The proposed approach is tested using RTD signals obtained from the measurements carried out with radiotracer technique. The experimental results show that the proposed approach with features extracted from the PDS of the RTD signals calculated using eigen-analysis methods is the most robust and reliable in RTD signal identification.

► A new method for RTD signal identification based on power density spectrum (PDS) is proposed. ► The idea of applying feature extraction from the PDS of RTD signal is interesting and has novelty. ► The performance of the proposed approach is tested in the presence of different kinds of noise. ► The proposed approach is robust and reliable especially in the presence of noise. ► The proposed approach can achieve recognition rates up to 100%.