Remote Usage Diagnostics for Energy-Wasting IoT Equipment
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Solution Overview
Problem
Smart home and IoT devices often operate under suboptimal conditions due to lack of effective diagnostic tools, leading to energy wastage and premature failure, as users rarely utilize available diagnostic tools to detect malfunctions.
Innovation Solution
A computing system is configured to retrieve and analyze energy or resource usage data from devices, incorporating environmental and facility properties to determine device efficiency, using data analysis models like random forest and support vector machines to identify features for diagnosis and provide recommendations for repair, maintenance, or replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If diagnostic tools are provided for devices, then device malfunction detection capability is improved, but user operation complexity increases
Solution Approach 1:
The system automatically performs diagnostic functions without requiring user intervention. The computing system retrieves usage data from devices, analyzes it using machine learning models, and generates diagnostic reports autonomously, allowing the system to serve itself in detecting malfunctions rather than requiring users to manually operate diagnostic tools
Solution Approach 2:
The system continuously monitors device usage data and provides feedback through automated diagnostic analysis. By comparing actual usage patterns against learned normal patterns, the system feedbacks diagnostic information to users and maintenance teams, enabling continuous improvement of detection accuracy while maintaining ease of operation
2Loss of energy
If automated diagnostic analysis is implemented, then energy wastage is reduced, but system complexity increases
Solution Approach 1:
The system performs preliminary diagnostic analysis by continuously analyzing usage data to detect early signs of malfunction before they lead to complete device failure. This preliminary detection allows for timely maintenance actions that prevent energy wastage from inefficient device operation, addressing the energy loss issue before it becomes severe
Solution Approach 2:
The system changes the parameter of analysis depth dynamically. It uses machine learning models that can adjust the complexity of analysis based on device type, usage patterns, and detected anomaly severity, allowing the system to optimize between analysis thoroughness and computational complexity for different diagnostic scenarios
3Measurement precision
If usage data is collected and analyzed, then device efficiency determination is improved, but data processing time increases
Solution Approach 1:
The system segments the data analysis process into distinct components: data retrieval from devices, preprocessing of usage data, feature extraction, machine learning model analysis, and diagnostic report generation. This segmentation allows parallel processing of different data streams and enables optimization of each segment independently, reducing overall processing time while maintaining analysis accuracy
Solution Approach 2:
The system performs partial analysis by focusing computational resources on the most critical diagnostic features and using machine learning models that can provide probabilistic results rather than exhaustive deterministic analysis. This approach achieves sufficient diagnostic accuracy for practical purposes while significantly reducing processing time compared to complete exhaustive analysis
Data Source
AI summary
Systems and methods are provided to retrieve or analyze usage data collected from a device or a facility where the device, optionally with devices are located, and identify useful features for making a diagnosis of the device. The diagnosis can be made before a system failure to reduce down time and inefficient use of the device, or after the system failure to expedite and facilitate diagnosis and repair. In addition to the usage data, such as energy and resource consumption, the system can also obtain information relating to the facility and the device's external environment which can be used for normalizing the usage data. Further, based on the diagnosis, the system can make suitable recommendations for repair, replacement, maintenance and upgrade.