Detector

Engineering

Leak

Support vector machine

Telecommunications

Computer science

Acoustics

Mel-frequency cepstrum

Mechanical engineering

Environmental engineering

Physics

Pattern recognition (psychology)

Pipeline (software)

Leak detection

Image (mathematics)

Electronic engineering

Artificial intelligence

Sensitivity (control systems)

Pipeline transport

Noise (video)

Feature extraction

Speech recognition

Process Safety and Environmental Protection

A spherical detector (SD) is capable of closely approaching a leak point and collecting leak sounds from the inside of a long pipeline, thereby enabling an extremely high leak detection sensitivity. However, acoustic noises arise from collision and friction while the SD is rolling forward, hindering the identification of leak acoustic signals. To address this challenge, this work presents a pipeline leak identification method for an SD based on combining variational mode decomposition (VMD) and a support vector machine (SVM). A leak generation system is set up where the pipe is water-filled, pressurized, and tiltable, and the SD can stand still or roll to collect a sufficient variety of leak sound samples. By decomposing the noisy signals into different modes and selecting the modes with high correlations to reconstruct the signals, the VMD can significantly decrease the collision noise. Additionally, the Mel frequency cepstral coefficients (MFCCs) are extracted and used to constitute a characteristic vector for SVM-based leak recognition. The trained neural network effectively identifies the occurrence of a leak; the recognition accuracy can reach up to 93 %, with a satisfactory specificity of 89.6 %.

Pipeline leak detection based on variational mode decomposition and support vector machine using an interior spherical detector