Edge Intelligence & TinyML

This document defines the edge intelligence capabilities, machine learning models, and algorithmic processing used by the EsoCore system for real-time anomaly detection and predictive maintenance.

Why Edge Intelligence?

Process data locally for faster response, reduced bandwidth, enhanced privacy, and reliable operation during connectivity outages. Critical for mission-critical and high-security environments.

Algorithmic Processing

Traditional Algorithms

Statistical Analysis: Rolling averages, standard deviation, trend analysis for baseline establishment
Signal Processing: FFT for frequency domain analysis, bandpass filtering, peak detection
Rule-Based Logic: Threshold monitoring, rate-of-change detection, pattern matching
Trend Analysis: Drift detection, degradation curves, usage pattern recognition

TinyML Models

Anomaly Detection Models

Vibration Anomaly Detection

Vibration Anomaly Detection:
├── Input: 3-axis accelerometer data (1-3 kHz windows)
├── Model: Lightweight autoencoder (8KB model size)
├── Output: Anomaly score (0-100%), confidence level
└── Triggers: Real-time alerts for scores >85%

Acoustic Pattern Recognition:
├── Input: MEMS microphone (audible + ultrasonic)
├── Model: 1D CNN for spectral analysis (12KB model size)
├── Output: Event classification (normal, bearing fault, spring break)
└── Triggers: Immediate alerts for catastrophic events

Model Specifications

Framework: TensorFlow Lite Micro, optimized for ARM Cortex-M
Model Size: <16KB per model (fits in STM32 flash)
Inference Time: <100ms for real-time processing
Memory Usage: <32KB RAM during inference
Update Mechanism: OTA model deployment with A/B testing

On-Device Intelligence Benefits

Faster Response

<1 second anomaly detection vs. cloud processing latency
Immediate safety alerts without network dependency
Real-time pattern recognition during operation

Privacy Preservation

Sensitive data never leaves the device
Compliance with data residency requirements
GDPR/HIPAA friendly for healthcare/lab environments

Bandwidth Optimization

90% reduction in data transmission through intelligent filtering
Priority queuing - send anomalies first, routine data later
Adaptive sampling - increase rates during detected anomalies

Reliability

Offline operation - intelligence works without cloud connectivity
Edge autonomy - critical decisions made locally
Fault tolerance - graceful degradation if ML models fail

Model Training & Deployment

Training Pipeline

Data Collection: Aggregate anonymized data from fleet for model training
Feature Engineering: Extract relevant features from sensor streams
Model Training: Use cloud-based training with AutoML optimization
Model Validation: Test on real door data across different environments
Model Compression: Quantization and pruning for edge deployment
OTA Deployment: Staged rollout with performance monitoring

Continuous Learning

Federated Learning: Improve models without raw data sharing
Model Versioning: A/B test new models against baseline performance
Performance Monitoring: Track inference accuracy and alert quality

Firmware Architecture

Edge Intelligence Task

The firmware includes a dedicated RTOS task for ML processing:

TinyML inference for vibration/acoustic anomaly detection
Pattern recognition algorithms
Predictive algorithms for maintenance scheduling
Real-time processing with <100ms latency

Integration with Other Tasks

Sensor Task: Provides preprocessed data to ML models
Event Logger: Records ML predictions and confidence scores
Sync Task: Prioritizes anomaly data for cloud transmission
Safety I/O: ML can trigger safety responses for detected anomalies

Model Types & Applications

Vibration Analysis Models

Bearing Fault Detection

Input: 3-axis accelerometer data, FFT coefficients
Model: 1D CNN with frequency domain features
Output: Bearing condition classification (good, degraded, failing)
Training: Historical bearing failure data from industrial applications

Motor Imbalance Detection

Input: Motor vibration signatures
Model: Autoencoder for baseline learning
Output: Imbalance severity score
Trigger: Maintenance alert when score exceeds threshold

Acoustic Analysis Models

Normal Operation Classification

Input: Microphone data in audible range
Model: Lightweight neural network
Output: Operation state (normal, strained, obstructed)
Applications: Door movement quality assessment

Catastrophic Event Detection

Input: Ultrasonic frequency analysis
Model: Event detection classifier
Output: Event type (spring break, mechanical failure)
Response: Immediate emergency alerts

Usage Pattern Analysis

Cycle Prediction

Input: Historical door usage patterns
Model: Time series forecasting
Output: Predicted usage for maintenance planning
Benefits: Proactive maintenance scheduling

Anomalous Usage Detection

Input: Door cycle frequency and timing
Model: Statistical outlier detection
Output: Unusual usage pattern alerts
Applications: Security monitoring, unauthorized access

Performance Requirements

Real-Time Constraints

Inference latency: <100ms for safety-critical decisions
Sampling rate: Support up to 3kHz for vibration analysis
Response time: <1 second for anomaly detection
Memory efficiency: <32KB RAM usage during inference

Accuracy Targets

False positive rate: <5% for maintenance alerts
False negative rate: <1% for safety-critical events
Confidence threshold: 85% for automated actions
Model validation: 95% accuracy on test datasets

Edge Computing Advantages

Reduced Latency

Local processing eliminates cloud round-trip time
Real-time decisions for safety-critical situations
Immediate response to equipment failures

Enhanced Privacy

Data sovereignty sensitive data never leaves premises
Regulatory compliance for healthcare and government facilities
Customer data protection for competitive advantages

Operational Resilience

Network independence for critical decisions
Continued operation during connectivity outages
Autonomous monitoring without cloud dependency

Cost Optimization

Reduced bandwidth costs through intelligent filtering
Lower cloud processing costs
Optimized maintenance scheduling reduces operational costs