Archive for machine learning – Page 2

Analytics and Digital Transformation Front and Center in Boston

My colleagues and I had the privilege of attending Tata Consultancy Services’ (TCS) Analyst Day event held in Boston on September 21, 2017. There were several interesting and informative presentations covering topics such as the concept of Business 4.0 and how TCS is deploying Digital, deep domain expertise and its deep and vast portfolio of […]

Read More →

“If It Ain’t Broke, Prevent It From Breaking:” IoT and IoE in Prescriptive Maintenance for Turbines

How IoT and IoE are Impacting Power Generation via Prescriptive Maintenance of Turbines From a mechanical standpoint, turbines are engineering marvels. These massive machines have thousands of delicate moving parts, each with precision tolerances necessary to achieve maximum efficiency and complex maintenance routines. Even a minute disturbance in the turbine’s system can trigger a catastrophic […]

Read More →

IIoT and Analytics Helping Independent E+P Firms to Optimize Oilfield Operations

ARC has been blogging for a few years now about how IIoT-enabled solutions can help owner-operators, independent E&P players and other oil & gas stakeholders can put them on the path towards digital transformation and operational excellence. Most of these opined blogs have focused on how rotating equipment such as pumps, compressors and turbines can […]

Read More →

Importance of Culture For (and On) IIoT Adoption

Having had the opportunity to attend the Operational Excellence in Energy, Chemicals & Resources conference held in Calgary, Canada and participate in the Digital Transformation track (sponsored by TCS) during the conference, I found there was as much discussion surrounding the topic of culture as there was on the topic of digital transformation, analytics/machine learning, […]

Read More →

Machine learning with data overload

Today’s massively increasing collections of data improve the potential value of data analytics applications like machine learning. How can you extract useful answers and conclusions from the data you have? In industry, the “data warehouse” concept has been used to merge operational data with business data. The basic idea is to collect and organize the […]

Read More →