Rachna Dhand is currently working as Principal Machine Learning in Cybernetics with BHP Billiton, WA Australia. For most of the decade she had worked on variety of roles in Higher Education, Oil and Gas and Metals and Minerals sector across India and Australia.
She completed her doctorate in Control Engineering and Predictive Analytics from School of Engineering and Energy, Murdoch University in 2011. She is working in Data Science domain since 2011. Prior to her migration to Australia, she worked as an academic and was researching on programming methodologies like SOAP/CORBA and Internet message passing optimization from 2000 to 2007.
She has worked on more than 20 projects in Data Science and Analytics. Her expertise is in application of Time Series Modelling as well as Machine learning across varied domains including Education, Mining and Publishing sectors. All her projects are successfully deployed and play integral role in BI and interventions on Business improvement.
In last 6 years, Rachna has structured and driven significant changes in a variety of industrial domains focusing on the application Data Science and Predictive Analytics to drive business transformation.
Augmenting Machine Learning in Asset Reliability
Our strategy in BHP is to own and operate large, long-life, low-cost, expandable and upstream assets. And we are diversified by commodity, geography and market. This scale and diverse footprint puts us in the perfect position to leverage the next-generation of Artificial Intelligence and Machine Learning. Machine learning is problem focused. Our strategy is to apply machine learning to most complex problems we face with assets to drive insights as well as enhance their reliability. The focus of this presentation will be ‘How we embraced machine learning to our assets to enhance their reliability and fault tolerance’.