Research on pipeline state recognition method based on acoustic signal frame PCA

Abstract

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.

Research on a Fault Diagnosis Method for Crankshafts Based on Improved Multi-Scale Permutation Entropy

“…A method using a Variational Mode Decomposition (VMD) [10] algorithm was proposed in 2014, which overcomes the modal aliasing defects of EMD and minimizes the noise interference, but it comes with the drawback that the penalty parameter α and decomposition parameter K need to be determined manually based on experience, which depresses the efficiency of the signal processing. Therefore, due to the shortcomings exhibited by the above signal decomposition methods, based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) [11] proposed by Torres et al, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) proposed by Colominas et al [12], which effectively suppresses the noise interference, greatly depresses the mode aliasing, and reduces the impact of spurious components on feature extraction.…”