Pipeline Damage and Leak Detection Based on Sound Spectrum LPCC and HMM

In order to protect pipeline transportation and prevent from leakage incident for manmade damage or natural factors, it is very important to carry out such researches as active protecting and accurate positioning. Designed the pipeline prevention monitoring and leak detecting system based on calculating LPCC (linear prediction cepstrum coefficient) and using HMM (hidden Markov models) to recognise damage acoustic signals. The continuous non-steady time-variety process was sub-framed and described with a series of short steady sequences on the basis of acoustic signal characteristic analysed. LPCC which represents accurately each short-time acoustic signal was selected as the acoustic signal characteristic parameters and extracted effectively using Durbin algorithm; HMM was established to recognise damage types by Baum-Welch revaluation algorithm with the state-transfer probability and observing time sequences characteristic parameters; using Viterbi decoding algorithm realized the search of best transfer route and achieved the corresponding export probability. The results show that the acoustic singles recognition rate is improved effectively based on sound spectrum LPCC and HMM,and can be up to 97%