Accurate buried pipeline state recognition based on acoustic signal is a difficult and important issue. This paper proposes a feature extraction method based on acoustic signal frame and principal component analysis (PCA) for condition monitoring in pipes. This method makes use of the property of nonstationary and multivariate data decomposition scales of pipeline acoustic signal. Signal framing is processed on the collected acoustic signals so that the signal frame series is obtained. Then, the sound pressure level of each frame signal is extracted, and the feature vector of the total sound pressure level is established. The PCA method is applied to optimize the extracted feature vector set for detecting the feature parameters. The acoustic signals related to different operating conditions of a pipeline are identified with the support vector machine. Research on a series of experiments shows that the proposed method for acoustic signal analysis can perform effectively for robust feature extraction and pipeline state identification.

A gearbox vibration signal contains non-linear impact characteristics and the significant feature information tends to be overwhelmed by other interference components, which make it difficult to extract the typical fault features fully and effectively. Aiming at the key issue of how to effectively extract the impact characteristics, a fault diagnosis method based on improved extreme symmetric mode decomposition (ESMD) and a support vector machine (SVM) is proposed in this paper. The vibration signal is adaptively decomposed into multiple intrinsic mode function (IMF) components by the improved ESMD and then a certain number of components are selected with the maximum kurtosis-envelope spectrum index. The singular spectral entropy, energy entropy and permutation entropy of each component are applied to construct the feature vector set, in which the dimensionality of the set is reduced with the distance separability criterion. Finally, the dimension-reduced feature vector set is input into the SVM for pattern recognition. Dynamic simulation and experimental gearbox research show that the improved ESMD method can extract and identify gearbox fault information effectively.