Deploy edge-native predictive maintenance on rotating equipment without cloud connectivity or retraining. RailMind adapts online to each machine's physical baseline and detects emerging faults in real time.

• Real-time fault detection at microsecond speeds
• No labeled training data required
• Cross-validated on industrial bearing datasets

A single engine architecture validated on 35+ public datasets across 19 domains. Each domain is modelled independently on-device; no retraining, no end-to-end backpropagation.

• 35+ public datasets, 19 domains — no architecture changes
• Per-domain independent modeling — no cross-domain transfer assumed
• Condition-aware deployment via explicit condition input (ConditionInput)

Real-time quality-of-experience monitoring deployed in streaming video decoding pipelines — detecting encoding anomalies, bitrate degradation, and quality shifts directly on edge devices. The same engine validated across industrial fault detection applies without architecture changes.

• Validated in streaming video decoding pipeline — no cloud required
• Detects encoding anomalies and bitrate degradation in real time
• Same engine, no architecture changes from industrial to video

Anomaly detection on satellite telemetry streams, evaluated on the public ESA-ADB SMAP dataset. Zero dynamic memory allocation ensures satellite-grade reliability with deterministic, O(1) per-sample execution.

• Evaluated on the public ESA-ADB benchmark
• Zero dynamic memory allocation
• No architecture changes from ground to space

Continuous structural integrity monitoring for bridges and civil infrastructure — validated on the Z24 Bridge dataset. The same engine detecting bearing faults in factories monitors structural anomalies in large-scale infrastructure without any architecture changes.

• Validated on Z24 Bridge real-world structural dataset
• Continuous on-device monitoring — no cloud dependency
• No architecture changes from industrial PdM to civil SHM
• Detects structural regime shifts and anomalous load patterns

Multi-joint predictive maintenance and anomaly detection for industrial and collaborative robots — evaluated on CASPER UR3e 6-axis robot data (1.76 million sensor rows); auto-configuration selected the L1-only path and did not deploy engine stacking. Handles high-dimensional, correlated multi-axis signals natively.

• Evaluation dataset: CASPER UR3e 6-axis collaborative robot
• 1.76 million rows of real joint-sensor data processed
• Multi-axis correlated signal processing without feature engineering
• Deployable on robot controller hardware — no cloud required

Regime-aware quality monitoring across the full video processing pipeline — from encoding and transcoding to streaming delivery. Detects encoding anomalies, bitrate shifts, and delivery degradation in real time on edge hardware. Validated on diverse streaming content.

• End-to-end coverage: encoding → transcoding → delivery
• Real-time regime detection with microsecond latency
• No retraining across different codec profiles or content types

Continuous behavioral regime detection from wearable and embedded motion sensors — validated across UCI HAR (6 activities) and WISDM v2 (18 activities, 160K samples). Operates entirely on-device with zero labeled training data.

• Validated: UCI HAR and WISDM v2 (160K real samples)
• Up to 18 concurrent activity regimes detected simultaneously
• Zero labeled training data required
• Runs on wearable and embedded sensor hardware

Acoustic regime detection for environmental monitoring, industrial audio analysis, and acoustic scene classification — validated on ESC-50 and DCASE benchmark datasets. The engine adapts online to distinguish acoustic environments without sound-specific feature engineering.

• Validated on ESC-50 and DCASE benchmark datasets
• No audio-specific feature engineering required
• Same engine architecture as industrial and video applications
• Deployable on low-power edge microphones and IoT devices