Edge ML Anomaly Detection for Smart Building Sensors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Smart building systems face challenges in accurately detecting anomalies due to their complex and dynamic nature, leading to false positives and missed detections, which complicates response efforts and undermines operational efficiency and safety.
Innovation Solution
A smart building system integrates anomaly detection sensors with on-device processing chips that execute machine learning models to identify anomalies in real-time, automatically identify malfunctioning devices, and initiate remedial responses, such as reconfiguring access permissions, without relying on centralized servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized server processing is used for anomaly detection, then comprehensive data analysis can be performed, but network latency and dependency on centralized infrastructure increase
Solution Approach 1:
The patent divides the anomaly detection system into distributed edge devices, each equipped with its own processing chip and machine learning model. Each device independently processes its own sensor data locally, segmenting the centralized processing function into multiple autonomous units. This eliminates network latency for real-time detection while maintaining comprehensive analysis capabilities through local model execution.
Solution Approach 2:
The patent transitions from a single centralized processing dimension to a multi-dimensional distributed architecture. By deploying processing chips across multiple edge devices throughout the building, the system creates spatial redundancy and parallel processing paths, enabling simultaneous local detection without network dependency while preserving analytical comprehensiveness.
2Power
If centralized server processing is used for anomaly detection, then data can be processed using powerful computing resources, but data privacy and network failure vulnerability increase
Solution Approach 1:
The patent segments computing power distribution by embedding processing chips directly in edge devices rather than concentrating all computing resources in a central server. Each device maintains local processing capability, ensuring operational autonomy and reliability during network failures while still providing comprehensive building-wide monitoring through distributed intelligence.
Solution Approach 2:
The patent enables self-service by allowing each edge device to independently execute machine learning models and make anomaly detection decisions using its own processing chip without requiring external server assistance. This self-sufficient local processing ensures continuous operation during network failures while maintaining data privacy through localized data handling.
3Adaptability or versatility
If dynamic building environments are monitored using traditional methods, then system adaptability is maintained, but false positives and missed detections increase
Solution Approach 1:
The patent implements dynamics by making the machine learning models adaptive through continuous learning from incoming sensor data. The models evolve and adjust their parameters based on changing building conditions, enabling the system to maintain high detection accuracy while adapting to dynamic environments such as varying occupancy patterns, seasonal changes, and equipment lifecycle variations.
Solution Approach 2:
The patent incorporates feedback mechanisms where detection results and sensor data continuously flow back into the machine learning models for refinement. This closed-loop system allows the models to learn from actual operating conditions and adjust their detection thresholds and parameters, reducing false positives and missed detections while maintaining adaptability to environmental changes.
Data Source
AI summary
Embodiments feature a device with a processing chip and embedded logic executing instructions. This chip utilizes a trained machine learning model for real-time analysis of sensor data streams (e.g., temperature, vibration, pressure), generating anomaly scores. Anomalies are identified, and upon detection, the chip autonomously initiates remedial actions such as deactivating compromised equipment, dispatching detailed notifications via email/SMS to personnel, interfacing with maintenance scheduling systems, or dynamically reconfiguring access credentials for authorized service entities. The device architecture supports over-the-air (OTA) updates for its machine learning models, and the processing chip can be integrated within the sensor assembly or an associated smart device.