Model Interpretability and Explainable AI in Manufacturing


In this talk, we present an industrial use case on “anomaly detection” in steel mills based on IoT sensor data. In large steel mills and manufacturing plants, the top reasons for unplanned downtime are:
• Failure of critical asset
• Quality spec of the end product in line not being met
• Operational limits outside the recommended range (e.g. process, human-safety, equipment-safety, etc.)

Unplanned downtime or line stoppage leads to loss of production or throughput and revenue loss.
Anomaly detection can serve as an early warning system, providing alerts on anomalous behavior that could be detrimental to the equipment health or affect process quality. In this work, we are performing multi-variate anomaly detection on time-series sensor data in a steel mill to help the maintenance engineers and process operators take proactive actions and help reduce plant downtime. Anomaly is presented to the customer in terms of:
• “time-intervals” – startTime: endTime chunks that exhibit deviant behavior
• “anomaly-state” – type association of anomaly to a specific pattern or cluster state
• “anomaly-contribution” – priority association to sensor signals that exhibited deviant behavior within the multi-variate list (more like signal importance)

We shall introduce the approach, where we reformulate the unsupervised modeling to a supervised formulation to incorporate SHAP, LIME, and other explainable tools. We shall illustrate the steps to provide the above-mentioned meta-data for an anomaly to make it explainable and consumable for the end-customer.


Pooja Balusani is a Data Scientist at for 2 years. She has a Bachelor of Technology in Computer Science and Engineering with a Specialization in Data Science from PES University, Bangalore. She worked on Reusable Data Science Assets, Product Quality, and Asset Health AI Applications.