AI-Driven Hardware Telemetry Architecture for Predictive Device Health

Abstract

The contemporary industries are based on billions of devices that are inextricably linked and the health of which must be controlled to avoid expensive non-productive time. AI-based hardware telemetry architecture A system that gathers telemetry data (e.g. sensor readings, performance counters) on equipment constantly and uses artificial intelligence to forecast equipment health and impending failures. In the present paper, a proposal offers an architecture that suggests the use of edge computing and machine learning to help facilitate predictive maintenance, which would no longer be based on reactive repairs but on proactive intervention. The suggested system is comprised of on-site AI analytics, on-device sensors and real-time data streaming to identify anomalies and predict faults prior to their occurrence. The AI model was also highly predictive (more than 90 percent) in forecasting precursors to failures in experimental assessment, whereas the edge-based processing maintained end-to-end latency (tens of milliseconds). Such outcomes show that an AI-based telemetry can considerably decrease the unwanted downtime and maintenance expenses through the ability to make a timely alarm and fixes.