Leak detection of gas pipelines using acoustic signals based on wavelet transform and Support Vector Machine

Abstract

Leak detection of gas pipelines has attracted extensive attention in recent years because such a leak could result in significant damage to society. This paper proposes an integrated leak detection method using acoustic signals based on wavelet transform and Support Vector Machine (SVM). Specifically, the optimal wavelet basis is selected by the entropy-based algorithm adaptively, with which acoustic signals gathered by acoustic sensors are first pre-processed by wavelet transform. Then useful features containing leak severity information are extracted from multi-domain components of the acoustic signals. Moreover, for leak detection and severity classification, the Relief-F algorithm is applied to select the most discriminative features. Furthermore, selected features are used as the input of SVM classifiers to identify the leak severity of gas pipelines. The effectiveness of the proposed method is validated using laboratory experiments. The results demonstrate that the proposed method achieves high accuracy of 99.4% to determine the leak state and non-leak state by using the first three most discriminative features and 95.6% to classify the normal and several leak severity conditions by using the first five most discriminative features. Therefore, it is effective for leak detection and promising for the development of a real-time monitoring system.