Device Shutdown via Redundancy Analysis and Qualification
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Solution Overview
Problem
Existing device management systems face challenges in efficiently managing device failures and maintaining remote operation in deployments, often requiring human intervention and leading to increased maintenance and remediation costs.
Innovation Solution
The system employs a failure aggregator to catalog device failures, utilize redundant devices or digital twins to maintain operation, and prioritize remediation using machine learning models to quantify uncertainty and allocate resources effectively. Additionally, it performs redundancy and qualification analyses to identify devices for shutdown, thereby conserving energy and extending device lifespan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of repair
If human intervention is used for device failure management, then device maintenance and remediation can be performed, but maintenance costs and time consumption increase
Solution Approach 1:
The system enables autonomous management of device failures through machine learning models that automatically detect anomalies, predict failures, and prioritize remediation actions without requiring human intervention. The failure aggregator and ML models work together to self-manage the device deployment.
Solution Approach 2:
The system continuously monitors device performance and uses feedback from failure data to train machine learning models. This feedback loop enables the system to improve its failure prediction and remediation prioritization capabilities over time, reducing the need for manual intervention.
2Reliability
If all devices are kept operational, then service availability is maintained, but energy consumption increases
Solution Approach 1:
The system performs redundancy analysis to identify devices that can be safely shut down before failures occur. By predicting which devices are likely to fail and using digital twins as backups, the system can proactively shut down non-critical devices to save energy while maintaining service availability.
Solution Approach 2:
The system creates digital twins of physical devices to simulate their behavior and predict failures. These virtual copies allow the system to test shutdown scenarios and determine which physical devices can be turned off without impacting service availability, thereby reducing energy consumption.
3Reliability
If redundant devices are deployed, then device failure impact is reduced, but device complexity increases
Solution Approach 1:
The failure aggregator serves multiple functions: it catalogs failures, analyzes redundancy, predicts failures using machine learning, and prioritizes remediation actions. This multi-functionality reduces the need for separate specialized components, thereby managing complexity while improving reliability.
Solution Approach 2:
The system combines redundancy analysis with machine learning failure prediction and digital twin technology into a unified approach. By merging these capabilities, the system achieves enhanced failure resilience without proportionally increasing complexity, as the same infrastructure supports multiple objectives.
4Loss of energy
If frequent device shutdowns are performed, then energy efficiency is improved, but device lifespan may be reduced
Solution Approach 1:
The system uses machine learning models to analyze device performance parameters and predict when shutdowns are safe to perform without impacting lifespan. By changing operational parameters based on predicted failure modes, the system can optimize the timing and duration of shutdowns to balance energy efficiency with device longevity.
Data Source
AI summary
Methods and systems for device shutdown in a deployment are disclosed. Device shutdown may be considered to conserve energy and simplify processes in a deployment. To conserve energy and simplify processes, all devices within a deployment may undergo a redundancy analysis and qualification analysis. The redundancy analysis may produce lists of redundant and non-redundant devices. All redundant devices may be candidates for device shutdown. Next, qualification analysis may qualify devices for shutdown by energy consumption and output data accuracy and uncertainty qualification. Devices that may not meet prescribed qualifiers may also be candidates for shutdown. With all devices that may be candidates for shutdown assembled in a list, device shutdown may commence in the deployment.