L2M NSERC- Machine Learning Based Tool to Predict Possible Failure Type of Oil and Gas Pipelines

油气管道基础设施市场受到由不同因素引起的管道故障事件的影响。实时或近实时地对油气管道故障事件进行量化，有助于更快、更合适地制定响应计划。因此，本提案的目的是开发一种数据驱动的管道故障评估产品，该产品结合了人工智能技术并利用了公开可用的故障数据库。Lab2Market计划将帮助提供工具和专家，向研究人员提供评估最终用户潜在需求所需的适当市场研究技能。

The oil and gas pipeline infrastructure market suffers from continuous pipeline failure events which is causedby different factors. Quantifying failure events of oil and gas pipelines in real- or near-real-time facilitates afaster and more appropriate response plan. Hence, the aim of this proposal is to develop a data-drivenpipeline failure assessment product, which incorporates artificial intelligence technology and utilizes publiclyavailable failure databases. The Lab2Market Program will help in providing access to tools and experts that informs researchers about appropriate market research skills required to assess potential needs by end-users.