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不断发生管道失效事件，其原因是多方面的。对油气管道的失效事件进行实时或近实时的量化，有利于更快、更恰当地制定响应计划。因此，这项提议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，并利用公共可用的故障数据库。Lab2Market计划将有助于提供获得工具和专家的途径，这些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.