CHICAGO, Aug. 26, 2021 (GLOBE NEWSWIRE) — VelocityEHS, the global leader in cloud-based environmental, health, safety (EHS) and environmental, social, and corporate governance (ESG) software, announced today that Dr. Julia Penfield, Ph.D., principal machine learning scientist at VelocityEHS, has received the Best Paper Award for her work on the application of machine learning in predictive online monitoring for the maintenance of power system assets at the 2021 icSmartGrid conference. The paper, “Machine Learning Based Online Monitoring of Step-Up Transformer Assets in Electrical Generating Stations,” was co-authored by Matt Holland, Maintenance Engineer at BC Hydro and provides evidence of the significant financial and safety benefits of applying machine learning to predictive monitoring programs.

Machine learning is a sub-set of artificial intelligence which can “learn” patterns and behavior in data. When applied to predictive maintenance scenarios, machine learning-based solutions eliminate most of the guesswork around the data collected over time to monitor the operating state of equipment to find patterns that can help predict and prevent failures. This can lead to major cost savings, higher predictability, and the increased availability and use of the systems being monitored.

“Our studies prove the increasingly common view that machine learning in predictive maintenance outperforms traditional maintenance strategies,” said Dr. Penfield. “Furthermore, BC Hydro’s success in using machine learning for a predictive online monitoring risk identification scheme demonstrates that machine learning is both an attainable and useable tool all companies should implement to achieve operational excellence. Whether its analyzing incident reports for categorization, facilitating efficient root cause and corrective action identification, extracting requirements from regulatory documents for auditing purposes, or using computer vision for remote ergonomic evaluation and analysis, machine learning replaces the costly human labor efforts required to complete these tasks to significantly increase safety, time/cost efficiencies, productivity, and profitability.”

Dr. Julia Penfield is globally recognized for her significant contributions to the machine learning field. After graduating from the University of British Columbia (UBC) with a Ph.D. in the Application of Machine Learning in Electrical Engineering, she continued working in the electric…

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