Abstract
The axial length of pipe defects is not linear with the reflection coefficient, which is difficult to identify the axial length of the defect by the reflection coefficient method. Continuous Hidden Markov Model (CHMM) is proposed to accurately classify the axial length of defects, achieving the objective of preliminary quantitative evaluation. Firstly, wavelet packet decomposition method is used to extract the characteristic information of the guided wave signal, and Kernel Sliced Inverse Regression (KSIR) method is used to reduce the dimension of feature set. Then, a variety of CHMM models are trained for classification. Finally, the trained models are used to identify the artificial corrosion defects on the outer surface of the pipe. The results show that the CHMM model has better robustness and can accurately identify the axial defects.
Highlights
Guided wave technology is a kind of efficient detection technology, which has a great advantage in the detection of long distance pipeline, and it has been widely used in engineering
To identify the axial length of pipeline defect based on Continuous Hidden Markov Model (CHMM), the guided wave signals are collected and the feature is extracted, and Kernel Sliced Inverse Regression (KSIR) algorithm is used to reduce the dimensions of feature data for the application of CHMM
The results show that the CHMM models can accurately identify the axial defects, and achieve the objective of preliminary quantitative evaluation
Summary
Guided wave technology is a kind of efficient detection technology, which has a great advantage in the detection of long distance pipeline, and it has been widely used in engineering. We can obtain more structural information when further processing of the guided wave signal, and it’s helpful to judge the healthy state of the structure more accurately. Hidden Markov model (HMM) is applied in speech signal recognition in the early, and introduced into the fault diagnosis field [3]. The HMM and fault diagnosis are the same in essence, that is to say, the true state is "hidden", which can be detected only through observation vector. The raw data of pipeline axial defects is obtained by the longitudinal guided wave technique, and the characteristics are extracted using wavelet packet decomposition. The CHMM classified model is applied to the recognition of the defect axial length, achieving the preliminary quantification
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