MinneBOS - Boston's Field Guide to Data Science & Emerging Tech

This presentation will cover our current research on an unsolved problem - how to apply systems thinking to reduce prediction uncertainty from machine learning models applied to high-consequence outcomes. The specific focus of this presentation will be on machine damage progression to catastrophic failure. Every wrong prediction about the health of machine can either lead to a machine failure costing millions of dollars or causing an operator to stop a machine thinking it is damaged when it is not - both scenarios are bad. This presentation will focus on a specific kind of large machine - wind turbines and discuss our current challenges dealing with prediction uncertainty and related adverse business outcomes from trying to do this for thousands of such turbines.

The presentation will show our attempts at building a system to represent the entire system of factors which cause large machines to have damage progression and eventual failure. The presenter will try to illustrate why this is hard to build and deploy as features in machine learning models and discuss our various attempts at it. Uncertainty is a less-talked-about topic in machine learning but it is critical when these algorithms directly impact the physical world. In terms of technical content, this will touch upon topics such as signal-to-noise ratio in stochastic time-series, difference in spatial and temporal resolution of various data sources, uncertainty quantification and propagation framework (e.g. Kalman Filters) for variety of machine learning models and last but not the least, adverse impact of benign human habits - we will cover ideas that we have tried to deploy to quantify and detect these issues.

The overall goal of this presentation is to (a) describe a hard, unsolved problem of significant consequence not only economically but also on one of the greatest challenges facing us - climate change and, (b) simulate a discussion and ideas-exchange in the wider community.