A gas pipeline leakage detection method based on multichannel acoustic signals

Gas pipeline leakage can cause adverse social impacts such as waste of resources and environmental pollution. Existing leakage detection methods ignore the practical problem that aging or damaged sensors can affect detection accuracy. In this paper, we propose a method for gas pipeline leakage detection using multichannel acoustic signals. We consider the signals that acquired by damaged acoustic sensors as outliers. First, Local outlier factor is used to calculate the outlier degree of each channel signal, and channels with outliers are removed. Dynamic time warping barycenter averaging is then used to integrate the remaining channel signals into one composite signal. Next, we extract the features of the composite signal in the time domain, frequency domain, and time frequency domain. Finally, One-class support vector machine is used to determine whether a gas pipeline leakage occurs. In the experiment, we validated the effectiveness of the proposed method on multichannel acoustic signals, which were collected at compressor stations operating in PipeChina. Experimental results show that the proposed method has a high recognition accuracy. The dataset is composed of the acoustic signals produced by the real operation of the pipeline, indicating that the proposed method has a good practical application value.