A Pipeline Abnormal Signal Detection Method Based on 1D-Faster R-CNN

Abstract

Aimed at the detection difficulty of local abnormal signals during pipeline operation, this paper takes the local abnormal signals as the detected targets, and proposes a new method based on target detection to extract abnormal signals with different amplitude and shape; and for the case where there are few actual leak samples, combined with the characteristics that the training samples of each module of the model itself are derivative samples of the original sample, so as to realize the small sample training of the model. Finally, a new pipeline leak detection and location method is proposed by combining the 1D-faster R-CNN with the cross-correlation location method based on signal matching. The experimental results show that the proposed method effectively extracts local abnormal signals, accurately alarms leak signals, and eliminates the false alarms caused by the recognition errors of normal signals.