Remote Device Diagnosis Using Usage Data and Environmental Normalization
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Solution Overview
Problem
Smart home and IoT devices often malfunction or operate inefficiently for extended periods due to lack of effective diagnostic tools, leading to energy waste and resource inefficiency.
Innovation Solution
A computing system is configured to analyze energy or resource usage data from devices, incorporating environmental and facility properties to identify efficiency issues, using data analysis models like random forest and support vector machines to provide diagnostic insights and recommendations for repair, maintenance, or replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If devices are equipped with limited diagnostic tools for detecting malfunctioning, then users can identify device problems, but users rarely use these tools leading to devices operating under suboptimal conditions for long periods
Solution Approach 1:
The system enables automatic self-diagnosis of devices by collecting and analyzing operational data without requiring user intervention. The computing system autonomously monitors device performance, detects inefficiencies, and generates maintenance recommendations, allowing the device to serve its own diagnostic needs without user action.
Solution Approach 2:
The system implements continuous feedback loops by collecting device operational data, analyzing it through machine learning models, and providing actionable insights back to users. This automated feedback mechanism keeps users informed about device health status and recommended actions without requiring them to manually check diagnostic tools.
2Loss of energy
If devices operate without continuous monitoring, then energy consumption is reduced during normal operation, but devices malfunction or operate inefficiently for extended periods leading to energy waste
Solution Approach 1:
The system replaces manual device monitoring and diagnostic checking with automated computational analysis. Machine learning models and algorithms automatically process device operational data to detect inefficiencies and predict failures, substituting human effort and simple mechanical diagnostic tools with intelligent software-based monitoring.
Solution Approach 2:
The system performs preliminary diagnostic analysis continuously in the background, identifying potential issues before they lead to significant energy waste or device failure. By proactively detecting inefficiencies early, the system enables timely maintenance actions that prevent energy loss from prolonged suboptimal operation.
3Measurement precision
If users manually collect and analyze device data to identify efficiency issues, then diagnostic accuracy can be improved, but the enormous amounts of data present a great challenge to users
Solution Approach 1:
The system extracts and isolates only the most relevant diagnostic features and metrics from the enormous volume of raw device data. Machine learning models automatically identify and extract key performance indicators and anomaly patterns, presenting only the most significant findings to users rather than requiring them to analyze complete raw datasets.
Solution Approach 2:
The computing system acts as an intermediary between raw device data and user interpretation. Automated machine learning models process and interpret complex operational data, translating it into actionable diagnostic insights and maintenance recommendations that users can easily understand and act upon without needing to analyze the underlying raw data.
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.