An NLP-based approach for Pipeline Monitoring and Leak Detection

Pipelines are an imperative part of the energy sector and have a substantial impact on the Canadian environment, and economy. The slightest mishap in pipelines can lead to devasting environmental impacts as well as huge financial consequences. Therefore, developing sound automated pipeline monitoring by leveraging artificial intelligence (AI) and machine learning (ML) for safe and reliable leak detection is highly desirable. The use of ML/AI techniques in pipeline monitoring is challenging mainly due to the vast amount and frequency of data. This study seeks to devise an innovative and novel Natural Language Processing (NLP)-based approach for pipeline leak detection by leveraging Hifi’s innovative fiber technology which provides a recognizable sequence, known as signature, to events based on the sounds they make.

Faculty Supervisor:

Mohammad Moshirpour

Student:

Partner:

Hifi Engineering Inc

Discipline:

Engineering

Sector:

Mining

University:

University of Calgary

Program: