Pipeline damage and leak sound recognition based on HMM

In order to protect pipeline transportation and prevent from leakage incident caused by 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 sound LPCC (linear prediction cepstrum coefficient) and recognizing damage or leak signals with HMM (hidden Markov models). The continuous non-steady time-variety process is sub-framed and described with a series of short steady state sequences on the basis of acoustic signal characteristic analyses. LPCC which represents accurately each short-time acoustic signal is selected as the acoustic signal characteristic parameters and extracted effectively using Durbin algorithm; HMM is established to recognize damage types by Baum-Welch revaluation algorithm with the state-transfer probability and observing time sequences characteristic parameters; using Viterbi decoding algorithm realizes the search of best transfer route and achieved the corresponding export probability. The results show that the acoustic signal recognition rate is improved effectively based on sound spectrum LPCC and HMM, and can be up to 97%.