Development Timeline
| Milestone | Focus | Status |
|---|---|---|
| Foundation | Core architecture and dynamics | Completed |
| Representation | Internal structure formation | Completed |
| Validation | Subsystem stress testing | Completed |
| Competition | Multi-unit interaction dynamics | Completed |
| Edge | Hardware-optimized deployment | In progress |
Technical Highlights
- Gradient-free online learning — no backpropagation, no loss functions, no optimizer
- Hundreds of computational units competing in real-time under finite resource constraints
- No pre-training required — no labeled datasets, no offline preparation
- 0.1ms per-step deterministic inference on Raspberry Pi 5
- AUC 0.996 on CWRU bearing fault detection
- 10 benchmark datasets spanning 6 signal domains — each validated independently, same architecture throughout
- ~40KB model footprint — deployable on MCU-class hardware (varies by application)
- Over 1.8 million controlled experiment runs across multiple research lines
Multi-Channel Architecture
RailMind outputs through three parallel channels — each serving a distinct integration use case:
| Channel | Output | Use Case |
|---|---|---|
| Ch.1 Health Vector | Adaptive internal representation projected to compact diagnostic space | Fault detection and classification |
| Ch.2 Drift Gate | Zero-compute distribution shift detector | Alerts when the input distribution shifts |
| Ch.3 Raw State | Full internal representation | Custom downstream tasks and research |
Industrial Validation
Validated independently across six distinct signal domains — same architecture, per-domain deployment:
| Domain | Dataset | Key Metric | Domain Type |
|---|---|---|---|
| Vibration (PdM) | CWRU Bearing | AUC 0.996 | Industrial fault |
| Vibration (PdM) | PHM 2022 Rock Drill | F1 0.91 | Industrial fault |
| Vibration (PdM) | CASPER UR3e Robot | Multi-axis regime detection (6-axis, 1.76M rows) | Robotics PdM |
| Vibration (SHM) | Z24 Bridge | AUC 0.99 (within-stream temporal protocol) | Structural health |
| Electrochemistry | NASA Battery | AUC 0.958 (+14.7 pp) | Degradation |
| Human Motion | UCI HAR / WISDM v2 | Multi-regime detection | Activity recognition |
| Audio | ESC-50 / DCASE | Positive lift (+0.9 pp) | Acoustic scene |
| Satellite Telemetry | ESA-ADB SMAP | CEF0.5 = 0.911 | Aerospace |
| Satellite Telemetry (temporal) | ESA-ADB SMAP | AUC 0.979 | Aerospace |
The engine’s internal representation consistently outperforms scalar readouts by 13–49 percentage points across tested domains. Catastrophic forgetting after sequential multi-fault exposure: 2.3% (CWRU 3-fault continual protocol).
Aerospace Differentiator
Satellite telemetry anomaly detection on the ESA-ADB SMAP benchmark:
- CEF0.5 = 0.911 [0.897, 0.923] — exceeds published reference of 0.888
- Zero dynamic memory allocation — satellite-grade reliability
- Static computational budget: O(1) per sample, deterministic execution path
Note: Non-apple-to-apple comparison with different train/test splits; confidence interval provided.
Edge Performance
Production-verified performance on commodity hardware:
| Metric | Value |
|---|---|
| RPi5 median latency | 0.1 ms (103 μs) |
| RPi5 P95 latency | 217 μs |
| Mac M4 median latency | 53 μs |
| Throughput (RPi5) | 8,102 steps/s |
| Total memory | 112.3 KB |
Memory breakdown:
| Component | Size |
|---|---|
| Engine state (static) | 61.6 KB |
| PCA projection matrix | 10.3 KB |
| RF classifier model | 40.4 KB |
Zero heap allocation in the hot path — all static buffers. No garbage collection pauses, no fragmentation over 24/7 industrial deployment.
Drift Detection
Built-in distribution shift detection with zero additional computation:
- True Detection Rate: 84%
- False Alarm Rate: 1.4%
- Detects distribution drift and sensor degradation as it happens
- Operating-condition changes are handled via explicit condition input (ConditionInput) — not inferred from the signal