Telemetry-Based Hardware Failure Prediction With Local Diagnostics
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Solution Overview
Problem
Existing methods for predicting hardware component failures in devices are not definitive, leading to premature replacements and unnecessary downtime, as telemetry data collected may not accurately predict imminent failures, and deep diagnostics can inconvenience users.
Innovation Solution
A hardware replacement prediction system that collects telemetry data and uses a prediction engine to identify potential failures, requesting a deep diagnostic confirmation from the device, which includes a communication interface, prediction engine, diagnostic evaluator, and reporter to assess component health and reduce unnecessary replacements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If telemetry data is collected and used for prediction, then prediction capability is improved, but prediction accuracy deteriorates
Solution Approach 1:
The system segments the diagnostic process into two distinct stages: (1) telemetry-based screening for potential failures, and (2) deep diagnostic confirmation for verification. This segmentation allows automated monitoring while maintaining accuracy through selective human/expert intervention only when needed.
Solution Approach 2:
The system introduces an intermediary deep diagnostic process that acts as a mediator between telemetry data and final replacement decisions. This intermediary layer verifies predictions before action is taken, ensuring accuracy while maintaining automation efficiency.
2Measurement precision
If deep diagnostics are performed to confirm predictions, then prediction accuracy is improved, but user convenience deteriorates
Solution Approach 1:
The system performs deep diagnostics only partially - specifically, only when telemetry data indicates a potential failure. This partial action approach maintains high accuracy for critical cases while avoiding unnecessary user inconvenience for normal operations.
Solution Approach 2:
Deep diagnostics are performed periodically based on predicted risk levels rather than continuously. This periodic execution ensures accuracy is maintained when needed while minimizing disruption to normal device usage during low-risk periods.
3Loss of energy
If premature replacement is avoided, then cost efficiency is improved, but device reliability deteriorates
Solution Approach 1:
The system uses feedback from deep diagnostic results to continuously refine prediction accuracy. By learning from confirmed failures and false positives, the system improves its ability to distinguish actual failures from false alarms, thereby maintaining reliability while reducing unnecessary replacements.
Solution Approach 2:
The system performs preliminary deep diagnostics before final replacement decisions are made. This preliminary verification action ensures that replacements are only performed when truly necessary, improving cost efficiency without compromising device reliability.
Data Source
AI summary
An example of a server including a communication interface to receive telemetry data from a plurality of client devices. The telemetry data is to indicate a health of a client device from the plurality of client devices. The server further includes a prediction engine to process the telemetry data to determine the health of the client device with a prediction model to identify a hardware issue at the client device. The server also includes a diagnostic evaluator in communication with the prediction engine. The diagnostic evaluator is to request a local confirmation of the hardware issue from the client device upon identification of the hardware issue by the prediction engine. The local confirmation is determined at the client device via a diagnostic engine. The server also includes a reporter to report the hardware issue upon receipt of the local confirmation.