Work

Research on the Multi-Sensor Fusion Model for Pipeline Corrosion and the Identification Method of Pitting Corrosion

Year: 2026

Type: article

Abstract: The depth of corrosion pits is critical to the safe operation of pipelines. Conventionally, monitoring of metal pipeline corrosion has generally relied on a single technique. Among these, the ultrasonic thickness measurement method and the field signature method are widely used in pipeline monitoring systems. The ultrasonic method has high reliability and accuracy for monitoring the thickness of pipelines, but it is limited by coupling and measurement range. Additionally, the field signature method often suffers from inadequate identification of corrosion pits and lower measurement accuracy. To address these limitations, a multi-sensor fusion model is proposed to monitor corrosion in metal pipelines. The multi-sensor fusion model is constructed by alternately arranging ultrasonic sensors and field signature probes, and a dedicated fusion algorithm is designed. The integrated model leverages the complementary strengths of both techniques while mitigating their individual shortcomings. Furthermore, an artificial neural network is employed to accurately identify pitting depth, thereby resolving the challenge in discriminating corrosion pit depths. Experimental results demonstrate that the multi-sensor fusion model can overcome the inherent drawbacks associated with a single technique. Consequently, it enhances the overall reliability, measurement accuracy, and operational range of the pipeline corrosion measurement system.

Source: Sensors

DOI: 10.3390/s26030936

Authors: Shilei Huang, Wenyang Li, Junbi Liao

Institutions: Chengdu University, Sichuan University

Language: English

Topic: Non-Destructive Testing Techniques

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137