Multi-Modal ILI Data Fusion for Combined Diagnostics of Pipeline

用于管道组合诊断的多模态 ILI 数据融合

• 批准号: 576744-2022
• 负责人: Liu, ZhengZ
• 金额: $ 1.78万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=761730
• 关键词: Multi; Modal; ILI; Data; Fusion

Metal loss and hidden flaws are critical threats to the pipeline for transporting oil and gas products. Non-destructive testing (NDT) techniques are employed as the in-line inspection (ILI) to evaluate and maintain the structural integrity during the pipeline's entire lifecycle. However, due to the limitation of the individual NDT technique, multiple NDT methods are often applied, and a comprehensive interpretation of the inspection results from the combination will achieve a precise integrity analysis. ROSEN's magnetic flux leakage (MFL) and ultrasonic wall measurement (UTWM) inspection tools are designed to apply high-level magnetization in combination with high-power ultrasonic waves to the pipeline. This combination of two NDT applications combines the best of the two techniques to ensure the pipeline's structural integrity during its entire lifespan. However, an automated process to interpret and analyze the ILI data is critical for accurate and reliable condition assessment. This research project is to develop analytic algorithms to fuse the MFL and UTWM data for the combined diagnosis of pipeline integrity issues. The research will first focus on the uncertainty analysis of the ILI data. Then, we will convert ILI data from one domain to another with deep learning approaches, for example, translating MFL data to UTMW data and vice versa. With the knowledge of the uncertainty, the translated ILI data can be aligned or registered based on the salient features and fused with the autoencoder network. The outcomes of the research will be integrated into ROSEN's RoCombo MFL/UTWM inspection system for improved characterization of interacting defects. Thus, the comprehensive inspection with data analytics can achieve reliable results to support the decision-making for pipeline management.

金属损失和隐藏的缺陷是对石油和天然气产品输送管道的严重威胁。无损检测(NDT)技术被用作在线检测(ILI)技术，用于评估和维护管道在整个生命周期中的结构完整性。然而，由于单个无损检测技术的局限性，往往需要使用多种无损检测方法，对组合检测结果进行综合解释才能实现精确的完整性分析。罗森的漏磁(MFL)和超声波测壁(UTWM)检测工具旨在结合高功率超声波对管道施加高水平磁化。这两种无损检测应用的组合结合了两种技术中的最佳技术，以确保管道在其整个使用寿命期间的结构完整性。然而，解释和分析ILI数据的自动化过程对于准确和可靠的状况评估至关重要。本研究项目旨在开发分析算法来融合漏磁和UTWM数据，用于管道完整性问题的联合诊断。研究首先将重点放在ILI数据的不确定性分析上。然后，我们将使用深度学习方法将ILI数据从一个领域转换到另一个领域，例如，将MFL数据转换为UTMW数据，反之亦然。在知道不确定性的情况下，翻译后的ILI数据可以基于显著特征进行对准或配准，并与自动编码器网络融合。研究结果将被整合到罗森的RoCombo MFL/UTWM检测系统中，以改进交互缺陷的表征。因此，综合检测和数据分析可以获得可靠的结果，为管道管理决策提供支持。