Autonomous Device Remediation Using Failure Aggregation
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Solution Overview
Problem
Existing computing device deployments require human intervention for maintenance and remediation when devices fail, disrupting remote operation and efficiency.
Innovation Solution
Implement a failure aggregator and digital twin system with machine learning models to identify and prioritize impaired devices, enabling autonomous remediation and replacement or repair based on operational and performance costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human intervention is used for device maintenance and remediation, then devices can be repaired when they fail, but remote operation integrity is disrupted and efficiency is reduced
Solution Approach 1:
The system enables devices to self-diagnose and self-report their status through automated health checks and failure notifications. The failure aggregator automatically collects, prioritizes, and manages device failures without human intervention, allowing the system to serve itself in terms of monitoring and initial response to device impairments.
Solution Approach 2:
The failure aggregator acts as an intermediary between devices and human operators. It collects failure data from multiple devices, prioritizes them based on impact, and manages the remediation process automatically. This intermediary layer eliminates the need for direct human intervention in routine device monitoring and failure management, maintaining operational integrity while preserving remote efficiency.
2Productivity
If automated failure aggregation and prioritization systems are implemented, then remote operation efficiency is maintained, but system complexity increases
Solution Approach 1:
The failure aggregator is designed as a universal system that can handle multiple device types, failure modes, and prioritization criteria through a single platform. It performs multiple functions including data collection, failure detection, prioritization, and remediation coordination, eliminating the need for separate specialized systems for each function and reducing overall system complexity despite the advanced capabilities provided.
Data Source
AI summary
Methods and systems for remediating impaired devices in a deployment are disclosed. The devices may be remediated by repairing or replacing the devices. Whether to repair or replace the devices may depend on the operational and performance costs of running the impaired devices in the deployment. Operational costs may include the retail price of the device and any energy requirements to run the impaired device. Performance costs may include effects of the output of the impaired device on other devices along the pathway of devices. If the integrated costs of the operational costs and performance costs outweigh the sum of the individual operational costs and performance costs, then the device may be replaced; otherwise, the device may be repaired.