A framework combining acoustic features extraction method and random forest algorithm for gas pipeline leak detection and classification. (November 2021)

• Record Type: Journal Article
• Authors: Ning, Fangli; Cheng, Zhanghong; Meng, Di; Wei, Juan
• Is Part Of: Applied acoustics. Volume 182(2021)
• Publication Date: 2021-11
• Subjects: Gas pipeline leak -- Acoustic features extraction method -- Leak detection and classification -- Correlation coefficient -- Random forest; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2021.108255
• Languages: English
• ISSNs: 0003-682X

Abstract

Abstract: Monitoring the operation status of the gas pipeline is of great significance to ensure the safe and stable operation of the pipeline. A new framework combining the acoustic features extraction method and Random Forest (RF) algorithm is proposed for gas pipeline leak detection and classification under the strong background noise. Acoustic signal has the advantages of non-contact measurement and insensitive to structural resonances, but it is easy to collect signals with low Signal-to-Noise Ratio (SNR) under strong background noise. To improve SNR and extract useful acoustic features, the acoustic features extraction method based on Ensemble Empirical Mode Decomposition (EEMD) and Correlation Coefficient (CC) analysis is proposed. CC analysis is used to select appropriate Intrinsic Mode Functions (IMFs), which are decomposed by EEMD algorithm. For each selected IMF, Mel-Frequency Cepstral Coefficients (MFCC), time-domain features, waveform features are extracted to form a feature matrix. Because RF has the characteristic of a small training set requirement, which provides a solution to the difficulty of obtaining enough leak acoustic signals in the industrial environment, RF algorithm is employed for leak detection and classification. Experiments were conducted to verify the effectiveness of the proposed framework. When the sampling distance is set to 1 m and 6 m, the average accuracies of RF with extracted acoustic features are higher than the results with all features. The proposed framework is general and can be applied to various acoustic-based industrial equipment condition monitoring challenges.