Electronic Prognostics

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 Electronic Prognostics (EP) for mission- and business-critical systems is a comprehensive approach for proactively detecting and isolating failures, supporting condition-based maintenance (CBM), and estimating the remaining useful life (RUL) of key electronic components in real time.

TNP has extensive experience developing EP technologies for components, subsystems, and fully integrated rack-level systems. The foundation of these capabilities is TNP’s second-generation Continuous System Telemetry Harness (CSTH2), which continuously collects time-series data that reflect the health and operating condition of active hardware and software elements.

These telemetry streams provide quantitative indicators of both physical variables (e.g., distributed temperatures, voltages, currents, fan speeds, vibration levels) and performance variables (e.g., CPU and memory utilization, throughput, queue depth, I/O volume), as well as various quality-of-service (QoS) metrics.

All CSTH2 signals are continuously archived in a circular “black-box” data recorder and simultaneously analysed in real time using TNP’s AI-MSET advanced pattern-recognition engine for early anomaly detection and RUL prediction, each with quantified confidence factors.

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EP enables early and sensitive detection of many failure mechanisms known to affect electronic systems, including:

• Environmental degradation (thermal irregularities, airflow blockages,      worn fan motors)
• Software aging (memory leaks, resource contention, and hang      conditions in both COTS and mil-spec platforms)
• Sensor degradation or failure
• Power supply and capacitor aging
• Mechanical, electrical, and optical interconnect deterioration
• Inferential sensing, where failed or drifting sensors are replaced by accurate analytical estimates to ensure mission continuity despite sensor faults or calibration loss

By integrating CSTH telemetry with advanced AI-based pattern recognition, TNP’s Electronic Prognostics technology enhances component reliability, boosts system availability, and improves root-cause analysis. This, in turn, reduces the costly occurrence of “no fault found” (NFF) events — a major logistics and maintenance challenge across enterprise and defense computing environments.

Examples of Failure Precursor Mechanisms in Enterprise Servers, Storage, and Network Switch Systems AI-MEST Catches Proactively

• Delamination of bonded IC components
• Departure from coplanarity in stacked components
• Dust fouling of heat sinks, power supply internals
• Interconnect degradation for optical, copper, and mechanical sockets
• Electrostatic discharge (ESD) phenomena
• Propagation of vibrational resonances
• I/O throughput degradation in HDDs from fan vibrations’
• Wear-out of components in electromechanical systems (disk drivers, fan trays, turbo blowers)
• Leakage of electrolyte in electrolytic capacitors
• Loose bus-bar cap nuts that lead to thermal issues
• Capacitors installed by vendor with reversed polarity
• Spontaneous fan oscillations from sensor degradation, producing excessive thermal cycling and accelerated aging problems
• Solder joint cracking and crack propagation (changes resistances, leading to motherboard component failures
• Contaminated molding compounds in IC chips and memory modules
• Sensor failures in general 
• Memory leads in IT software stacks are very difficult to root cause. AI-MEST can proactively identify the onset of memory leaks and root causing the origin
• Power supplies with all types of AC/DC and DC/DC PSU degradation modes; microscopic cracks in ceramic PSUs that lead to overheating
• Bit error rates in interconnects and Fibre-Channel-Arbitrated Loops
• Heat-Sink-Attach issues due to errors during assembly
• Gradual lubrication dry-out in fan motors
• Fan bearing out-of-roundness degradation