(186d) "Data Scientist in a Box" for the Industrial Practitioner

In this talk, we present a machine learning approach that empowers the industrial practitioner to quickly solve their domain specific big data problems, and utilize the operational insight gained to improve safety, quality performance, and reduce downtime. This approach provides visibility and control in the hands of the practitioners who understand the operational processes and equipment, without requiring a team of data scientists. The practitioners feed the machine learning system with time series operational data, and based on the predictive insights and alerts, determine the corrective action to take. An important advantage of this approach is “continuous learning” - the practitioner can use new learnings and events to adapt, retrain, and refine the model online.

The application of this “data scientist in a box” approach in the process industry is presented through three case studies. The first case study focuses on proactive process monitoring by a chemicals manufacturer to provide early insight into a sequence of process operations. This insight is available in advance of quality measurements and allows operations to act proactively and minimize off-spec product. The second case study focused on early detection of stream off-spec in a refinery. The model was trained on both normal as well as identified upset conditions across processing units that led to the stream being off-spec. We show how the model was able to provide advance warning of the off-spec condition, which would have otherwise resulted in expensive storage and reprocessing costs. The third case study focuses on proactive health monitoring of critical equipment by a global industrial equipment manufacturer. The model was trained on operational data with identified fault conditions. The framework was integrated with the customer infrastructure to issue notifications as precursors to fault conditions were detected.

For each of the case studies, details of the implementation, specific fault scenarios predicted, as well as organizational learnings from the implementation will be discussed.