Cloud-based Fault Detection and Classification for Oil & Gas Industry

TL;DR

This paper presents a Lambda architecture for the oil and gas industry that integrates cloud-based data processing with fault detection and classification modules for DCS/SCADA systems, enhancing operational reliability.

Contribution

It introduces a novel Lambda architecture tailored for oil and gas industry data, including implementation insights for sensor fault detection and classification in cloud environments.

Findings

Successful integration of fault detection modules in cloud architecture

Enhanced fault classification accuracy in oil & gas sensor data

Practical insights into cloud deployment challenges

Abstract

Oil & Gas industry relies on automated, mission-critical equipment and complex systems built upon their interaction and cooperation. To assure continuous operation and avoid any supervision, architects embed Distributed Control Systems (DCS), a.k.a. Supervisory Control and Data Acquisition (SCADA) systems, on top of their equipment to generate data, monitor state and make critical online & offline decisions. In this paper, we propose a new Lambda architecture for oil & gas industry for unified data and analytical processing on data received from DCS, discuss cloud integration issues and share our experiences with the implementation of sensor fault-detection and classification modules inside the proposed architecture.