A novel percussion-based approach for pipeline leakage detection with improved MobileNetV2

Pipelines are susceptible to oil and gas leaks during long-distance transportation due to factors such as damage from external forces and aging. However, existing pipeline leakage detection technologies that rely on physical inspections or sensors installed on pipelines are time-consuming and costly. In this paper, a percussion approach based on improved MobileNetV2 is proposed for pipeline leakage detection. Firstly, the influence of pipe leakage size on vibration characteristics was investigated by theoretical analysis and numerical simulation. Subsequently, experiments were conducted to assess the validity of the proposed method. The sounds produced by hammering the pipe under different damage conditions were recorded using a smartphone. The improved MobileNetV2 model was then used for classifying Mel spectrogram and Mel frequency cepstrum coefficient (MFCC) features extracted from the recorded sound signals. This model incorporates a multi-scale feature fusion module, which allows it to capture features at different scales and enhances its ability to differentiate between damage conditions. Experimental results show that using Mel spectrogram as input for the improved MobileNetV2 achieves a higher accuracy compared to using MFCC, with 100% accuracy for identifying leakage damage and 99.87% for classifying leak size. Compared to other methods, the improved MobileNetV2 exhibits superior classification performance while maintaining the lightweight characteristics of the original MobileNetV2. In conclusion, the improved model demonstrates significant enhancements in classification performance and operational efficiency, making it a promising approach for processing percussive signals.