The oil and gas industry is undergoing one of the most transformative and challenging business periods in its history. Companies are tasked with improving the efficiency of their operations while reducing their environmental footprints and finding support structures for new developments. Each company in the industry is tasked with these challenges in the face of capital spending reductions of nearly 20 percent in the past year alone.
Keeping machinery and pipelines in efficient working order is a common challenge in the oil and gas industry. The very nature of the pipeline industry makes it even more critical to identify the cause of potential faults or failures before they have an opportunity to occur.
Emerging technologies like the Industrial Internet of Things (IIoT), big data analytics, and cloud data storage are enabling more vehicles, industrial equipment, and assembly robots to send condition-based data to a centralized server, making fault detection easier, more practical, and more direct.
Identifying potential issues in a proactive manner allows energy companies to deploy their maintenance services more effectively and to improve equipment up-time. The critical features that help to predict faults or failures are often buried in structured data. Artificial intelligence models can identify anomalous behavior, and the information derived from the equipment sensors can be turned into meaningful and actionable insights for proactive maintenance of assets, further preventing incidents that result in asset downtime or accidents.
This is commonly known as predictive maintenance, and the addition of data analytics enables organizations to forecast when or if functional equipment will fail so that its maintenance and repair can be scheduled before the failure occurs.
The bigger companies have already been using this form of predictive maintenance for more than a decade. Small- and medium-sized companies in the manufacturing sector also can reap its advantages by keeping repair costs low and meeting initial operational costs for new operations.