End-to-End 1D-CNN Leakage Detection in Low-Density Oil-Flow Pipelines Using Raw Simulated Vibration Signals
DOI:
https://doi.org/10.59261/jequi.v8i5.452Keywords:
1D-CNN, Leakage Detection, Raw Vibration Signal, Binary Time-Series ClassificationAbstract
Background: Leakage detection in low-density oil-flow pipelines remains challenging because leakage-induced vibrations can be masked by pump harmonics, flow-dependent vibrations, and background disturbances. This study investigates an end-to-end one-dimensional convolutional neural network (1D-CNN) that learns directly from raw vibration waveforms without handcrafted feature extraction.
Objective: This study aims to develop, validate, and benchmark a compact end-to-end 1D-CNN for binary leak/non-leak detection across five flow rates under defined noise conditions using a leakage-safe, flow-stratified evaluation protocol.
Methods: A physically informed numerical vibration dataset comprising 600 windows was generated for leak and non-leak conditions at flow rates of 20, 25, 30, 35, and 40 L/min. The data were divided into training, validation, and test sets using a 70:15:15 ratio. The proposed network contains three convolutional stages, global average pooling, a fully connected layer, dropout, and a sigmoid output, with 6,265 trainable parameters.
Results: On the independent test set, the model achieved 94.44% accuracy, 97.62% precision, 91.11% sensitivity, 97.78% specificity, an F1 score of 94.25%, an MCC of 0.891, and a ROC-AUC of 0.986. Performance remained approximately 94% at 20–30 dB SNR but decreased at 15 dB SNR.
Conclusion: The compact end-to-end 1D-CNN demonstrated strong leak/non-leak discrimination under the evaluated simulated conditions. Validation using real pipeline vibration measurements and broader operating conditions is required before practical deployment.
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