Device Health Monitoring From Electrical Usage in Structures
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Solution Overview
Problem
Conventional techniques for maintaining and monitoring structures and devices within them are inadequate, often leading to unnoticed issues that result in catastrophic damage due to lack of real-time data analysis and predictive maintenance, especially during extreme weather conditions.
Innovation Solution
A system and method that utilize sensors and machine learning models to monitor usage data from devices within a structure, determining health statuses and generating recommended usage adjustments to prevent damage, and providing users with proactive maintenance recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional manual inspection techniques are used to monitor structure health, then implementation cost and complexity are low, but detection reliability and timeliness are insufficient leading to catastrophic damage
Solution Approach 1:
The system segments the structure into multiple monitoring zones with different sensor types (vibration sensors, acoustic sensors, strain gauges, temperature sensors) deployed at critical locations. Each sensor monitors specific parameters, and data is aggregated by a central processing system to provide comprehensive health assessment without requiring a single complex sensor system.
Solution Approach 2:
The patent introduces an intermediary processing system that collects data from multiple simple sensors and applies machine learning algorithms to detect anomalies. This intermediary layer transforms raw sensor data into actionable insights, improving detection reliability while keeping individual sensor components simple and manageable.
2Loss of time
If frequent manual inspections are performed to detect issues early, then detection timeliness improves, but loss of time and labor resources increase
Solution Approach 1:
The system implements continuous automated monitoring that operates 24/7 without interruption, continuously collecting sensor data and analyzing structure health. This eliminates the gaps between manual inspections and provides real-time detection of degradation trends, reducing maintenance response time while improving overall inspection efficiency through automation.
Solution Approach 2:
The system performs self-diagnosis by automatically analyzing sensor data and generating maintenance alerts without requiring human intervention for routine monitoring. The machine learning model continuously assesses structure health and identifies anomalies, freeing up human resources for more complex tasks while maintaining high detection efficiency.
3Measurement precision
If comprehensive sensor deployment is implemented to monitor all critical parameters, then measurement precision and detection capability improve, but device complexity and implementation cost increase
Solution Approach 1:
The system applies different sensor types and monitoring densities to different critical zones based on their specific risk profiles. High-risk areas receive more sophisticated sensor arrays, while lower-risk areas use simpler monitoring. This localized approach achieves high measurement precision where needed without uniformly increasing system complexity across the entire structure.
Solution Approach 2:
The patent employs multi-functional sensor nodes that can detect multiple parameters (vibration, acoustic signals, strain, temperature) using integrated sensor arrays. This universal approach allows a single sensor deployment to provide comprehensive monitoring across multiple dimensions, improving measurement precision without proportionally increasing system complexity.
4Reliability
If real-time data analysis is performed to enable predictive maintenance, then maintenance effectiveness improves, but use of energy and computational resources increase
Solution Approach 1:
The system performs partial real-time analysis by continuously monitoring for critical anomalies while using less frequent batch processing for trend analysis. The machine learning model triggers intensive computation only when anomalies are detected, rather than continuously analyzing all data at maximum processing power. This approach maintains high predictive maintenance effectiveness while reducing overall computational energy consumption.
Data Source
AI summary
Techniques for determining health statuses of devices within a structure are disclosed herein. An exemplary computer-implemented method includes receiving, at one or more processors, usage data corresponding to a device associated with the structure. The usage data may include at least electrical consumption data of the device. The exemplary method may further include determining, by the one or more processors, a usage level of the device based upon the usage data, and determining, by the one or more processors utilizing a device health model, a health status of the device based upon the usage level. The exemplary method may further include generating, by the one or more processors, a recommended usage adjustment for the device based upon the health status; and causing, by the one or more processors, the recommended usage adjustment to be displayed to a user.