Using Predictive Analytics, Machine Learning and AI, to Prevent Downtime of your Critical Equipment

Asset-intensive industries like oil & gas, mining, energy and utilities use complex equipment such as rotary, compressors, haul trucks, and turbines in their day-to-day operation. Any unplanned downtime or major unforeseen equipment failure negatively impacts production, which affects the organization’s financial performance.

However, increasing instrumentation (“smart technology”) of equipment and infrastructure and wireless communications are enabling organizations to acquire volumes of asset performance data and become proactive in monitoring the condition of these assets. Furthermore, analytics are enabling organizations to develop sophisticated models of asset performance, predict component and equipment failure and assess the health of in-service equipment.

Driven by predictive analytics you can now detect even minor anomalies and failure patterns to determine the assets and operational processes that are at the greatest risk of problems or failure.

Advances in analytic algorithms enable organizations to identify signs of possible failure well in advance of previous methods. What-if analysis allows an organization to investigate potential scenarios to determine the most appropriate (economic, efficient, safe) means of responding to pending equipment failure.

Automated decision management can then recommend the best action to take in anticipation of equipment problems. This session will explain how the capabilities of Predictive Maintenance and Quality solution are being used by oil & gas, mining, energy and utility organizations worldwide to integrate relevant equipment data, including real-time, build models that predict maintenance needs, monitor asset performance, provide timely alerts, and recommended appropriate actions. These integrated capabilities allow these organizations to deploy limited resources more cost effectively, maximize equipment uptime and enhance quality and supply chain processes.

Use-case examples:

- Predict the failure of a monitored asset in order to fix it and avoid costly downtime

- Identify the root causes of asset failure to take corrective actions

- Minimize product quality and reliability issues to meet customer delivery schedules

- Others...

Key Points & Highlights

- Any unplanned downtime or major unforeseen equipment failure negatively impacts production, which affects the organization’s financial performance.

- What-if analysis allows an organization to investigate potential scenarios to determine the most appropriate (economic, efficient, safe) means of responding to pending equipment failure.

- Predict the failure of a monitored asset in order to fix it and avoid costly downtime

- Identify the root causes of asset failure to take corrective actions

- Minimize product quality and reliability issues to meet customer delivery schedules

Others

- Quality, Assets and Predictive Analytics

- Pure Statistical modeling for Analysis

- What-if analysis for predictive maintenance

- Machine Learning