In the era of Edge AI and Industrial IoT, the reflex answer to almost every anomaly detection problem is to throw a deep neural network or a complex Machine Learning (ML) model at it.
However, in critical production environments—such as high-rate fluid processing, robotics, or chemical distribution systems—standard ML faces three critical bottlenecks:
The Black Box Dilemma: Deep models cannot explain why an anomaly was triggered, making field-debugging impossible.
Data Scarcity: Real-world failure modes are rare. Gathering millions of dirty training samples is often an unrealistic luxury.
Environmental Shifting: When the physical medium changes (e.g., fluid moving from a calm flow to a highly turbulent phase), static ML models break down, causing a flood of false alerts.
