Time Series AI for Anomaly Detection and Diagnosis

Artificial Intelligence (AI) and Machine Learning (ML) techniques have been used to solve complex manufacturing operations problems. Applying ML and AI to anomaly detection and diagnosis at scale, however, has been a significant challenge. This paper discusses how Falkonry’s Time Series AI platform leverages ML/AI for automated detection and diagnosis of equipment in steelmaking, detecting precursor conditions to critical equipment failures 3-10 weeks in advance of breakdowns. This methodology is scalable across use cases without the need for data scientists. Precedent detection of novel equipment conditions provides insight into maintenance operations that would otherwise have been missed. Such insights lead to proactive maintenance interventions, thus avoiding loss of production due to unexpected downtime events.