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

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Start: 4:00PM
End: 4:30PM
Date:
Saturday, March 9, 2024
Location
Details
Room 1 - Mary Jackson Stage
407 9 Ave SE, Calgary, AB
Calgary, AB T2G 2K7
Canada
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About This Talk

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

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Speakers

Oscar Cruz

CTO, Bow River Solutions
CTO
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Oscar Cruz

CTO, Bow River Solutions
CTO

Oscar Cruz is a distinguished Cybernetic and Computer System engineer renowned for his expertise in predictive analytics, machine learning, and artificial intelligence. Beginning his academic journey at La Salle University, he honed his skills in cybernetic and computer systems. Oscar's professional trajectory spans diverse roles in software development, business intelligence, and analytics across industries such as construction, mining, transportation, government, and education.

Throughout his career, Oscar has demonstrated leadership by forming global teams dedicated to delivering cutting-edge analytics solutions, collaborating with esteemed institutions and companies across Canada, the USA, and Mexico.

As a contributor to the Data Analytics Program and a key figure in shaping SAIT's Analytics bootcamp program, he enriches education with expertise in dimensional modelling, machine learning, and artificial intelligence. Currently serving as the CTO and Business Analytics Practice Leader at Bow River Solutions, Oscar drives transformative developments in technology consulting. Specializing in data management, Bow River Solutions collaborates with industry-leading vendors under Oscar's strategic guidance, including IBM, Tableau, Minitab, Databricks, Denodo, Terraform, SAP, Informatica, and Microsoft.

Oscar Cruz's legacy lies not only in the technologies he champions but also in the profound impact he makes on individuals and organizations embracing the future through innovation and data-driven solutions.

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