RailMind
— Adaptive Edge AI Engine
0.1ms deterministic inference on standard MCU — no NPU, no cloud, no training data. An online-adaptive engine with no end-to-end backpropagation, validated independently across 10 benchmark datasets, powering industrial predictive maintenance, real-time video intelligence, and beyond.
What is RailMind?
RailMind is an on-device adaptive engine — no end-to-end backpropagation, zero gradients at deployment. It eliminates cloud connectivity, training data, and GPU hardware. Born from a dissipative-cognition research program and distilled into a deployable substrate, it detects anomalies, flags distribution drift, and continuously adapts online — all within ~40KB at microsecond speeds.
Core Principles
Four foundational principles drive RailMind's architecture.
0.1ms Deterministic Inference
AI enters the control loop. On standard MCU hardware, RailMind achieves 100-microsecond deterministic inference — fast enough for real-time servo control and precision manufacturing.
No NPU Needed
Runs on standard ARM Cortex-M processors. Zero hardware upgrade cost — existing industrial MCU gains top-tier AI capability through firmware update alone. SIL-Ready architecture.
1000-Step Rapid Convergence
No weeks of training. The engine establishes a physical baseline in just 1000 sampling steps. A 5Hz device is ready in 200 seconds; a high-speed line in under 1 second.
Hybrid Edge Architecture
MCU handles microsecond perception, MPU handles decision-making. Sensor data is transformed into actionable maintenance recommendations — not just fault codes, but guidance on what to fix.
Performance
Deterministic per-step inference on Raspberry Pi 5 — hard real-time edge AI
CWRU bearing fault detection
Engine footprint — deployable on MCU-class hardware (*varies by application and device)
Controlled experiment runs across multiple research lines
Validated across 10 benchmark datasets spanning 6 signal domains — vibration (PdM & SHM), electrochemistry, satellite telemetry, human motion, audio — each domain validated independently
Pre-training, labeled data, or end-to-end backpropagation required
Live Demo
RailMind in action across rail, wind, marine, and industrial scenarios — edge-deployed predictive maintenance running on real production equipment.
Applications
Industrial Predictive Maintenance
Edge PdM for rotating machinery across rail, wind, marine, and general industry — validated on CWRU, PHM2022, and CASPER UR3e robot datasets. Covers 5Hz to 1000Hz equipment.
Video & Streaming Intelligence
Real-time video quality-of-experience detection, codec optimization, and semantic analysis — validated on streaming QoE benchmarks
Structural & Infrastructure Monitoring
Continuous structural health monitoring for bridges and civil infrastructure — validated on Z24 Bridge dataset
Architecture
A layered architecture from resource dynamics through competing computational units to edge deployment — MCU for perception, MPU for decision-making.
Competitive Landscape
| Capability | RailMind | Siemens Copilot | Augury | BrainChip |
|---|---|---|---|---|
| Inference latency | 0.1ms (MCU) | ~100ms (Cloud) | Cloud | ~1ms (ASIC) |
| NPU required | No (MCU only) | Cloud GPU | Cloud | Dedicated ASIC |
| On-device learning | Continuous | Fixed after training | ||
| Memory footprint | ~40 KB | Cloud | Cloud | ~1 MB ASIC |
| No end-to-end backprop | ||||
| Built-in drift detection | External | External | ||
| Multi-domain validated | 10 datasets, 6 domains | Vibration + thermal | Vibration only | Generic |
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