Edge Gateway Integration for BACNet and IoT Predictive Monitoring
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Solution Overview
Problem
Existing predictive monitoring systems in commercial environments face challenges in integrating with both IoT and BACNet protocols, leading to incompatibility issues that hinder seamless data communication and analysis across different sensor systems.
Innovation Solution
A predictive monitoring system that includes an edge gateway and processing module capable of reconciling differences in communication protocols, allowing it to communicate with both IoT and BACNet devices, and integrating them into a unified data analysis platform using AWS architecture and IoT protocols like MQTT.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system integrates both IoT and BACNet protocols to communicate with diverse sensor systems, then the adaptability and versatility of the monitoring system is improved, but the device complexity increases due to protocol reconciliation requirements
Solution Approach 1:
The patent employs protocol translation services or gateway devices as intermediaries between IoT and BACNet networks. These intermediaries receive data from one protocol type, translate it into the other protocol format, and forward it to the appropriate destination. This mediator approach enables seamless communication between diverse sensor systems without requiring the entire system to handle both protocols simultaneously, thus improving adaptability while managing complexity through localized translation points.
Solution Approach 2:
The monitoring system is designed with universal communication capabilities that can interface with multiple protocol types (IoT, BACNet, and others) through a unified architecture. The system incorporates multi-functional communication modules that can adapt to different protocol requirements, allowing a single system to serve diverse sensor networks. This universal design improves versatility while consolidating complexity into standardized interface components.
2Reliability
If passive monitoring is used until failure detection, then the system simplicity is maintained, but the reliability deteriorates due to delayed failure prediction and potential secondary damage
Solution Approach 1:
The system performs preliminary actions by continuously analyzing sensor data to detect early signs of equipment degradation before actual failure occurs. It establishes baseline performance metrics and monitors for deviations, enabling predictive maintenance scheduling. This preliminary detection and response capability improves reliability by preventing catastrophic failures while maintaining manageable complexity through automated analysis algorithms.
Solution Approach 2:
The monitoring system implements continuous feedback loops where sensor data is constantly analyzed, compared against expected performance parameters, and used to adjust maintenance schedules and operational parameters. When anomalies are detected, the system provides feedback alerts to operators and can automatically trigger diagnostic routines or maintenance workflows. This feedback mechanism enhances reliability by enabling proactive responses while keeping the system structure organized and manageable.
3Productivity
If real-time data analysis is implemented across all sensor systems, then the productivity of maintenance operations is improved through proactive maintenance, but the energy consumption increases due to continuous processing requirements
Solution Approach 1:
The system implements selective real-time analysis rather than universal continuous processing. It prioritizes critical sensors and parameters that have the highest impact on equipment reliability and safety, applying intensive real-time analysis only to these key data streams. Less critical parameters are monitored at reduced frequencies or using lighter analysis methods. This partial action approach maintains high maintenance productivity for critical functions while reducing overall energy consumption.
Solution Approach 2:
The monitoring system employs periodic analysis cycles with varying intensities based on operational conditions. During normal operation, it uses lighter periodic checks at standard intervals. When anomalies are detected or during critical operational phases, it intensifies analysis frequency and depth. This periodic action with dynamic adjustment maintains productivity when needed while conserving energy during stable periods, balancing maintenance efficiency with energy consumption.
Data Source
AI summary
A predictive monitoring system and an integrative subsystem installed in a BACNet environment, wherein the subsystem communicates with BACNet devices and other components of the predictive monitoring system to allow the predictive monitoring system to both communicate with IoT devices and also integrate with existing BACNet devices operating on the different BACNet communications protocol. The predictive monitoring system receives data streams from all of these devices and reconciles differences in the communications protocols for subsequent use of different data streams by the predictive monitoring system.