Hardware Failure Prediction With Local Diagnostic Confirmation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems fail to accurately predict hardware failures in devices, leading to unnecessary downtime and potential damage from unexpected component failures, as they rely on estimated life expectancies that may be shorter than actual lifespans, and telemetry data that is not definitive.
Innovation Solution
A system that collects telemetry data from devices and uses a prediction engine to identify potential hardware issues, followed by a local deep diagnostic process to confirm failures, minimizing user disruption and improving prediction accuracy through machine learning and rules-based models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If estimated life expectancies are used to determine replacement timing, then hardware can be replaced proactively, but replacements may occur prematurely reducing system availability
Solution Approach 1:
The system performs preliminary deep diagnostics on selected components before predicting failure, using telemetry data to identify candidates for enhanced monitoring. This preliminary action allows the system to distinguish between components that truly need replacement versus those that can continue operating, reducing premature replacements and associated downtime.
Solution Approach 2:
The system implements feedback loops where deep diagnostic results are fed back into the prediction model to continuously refine replacement predictions. Telemetry data and diagnostic outcomes are used to adjust prediction algorithms, improving accuracy over time and reducing both premature replacements and missed failures.
2Measurement precision
If deep diagnostics are performed on all components, then prediction accuracy improves, but device performance and user experience deteriorate
Solution Approach 1:
The system applies different levels of diagnostic scrutiny to different components based on their individual risk profiles. Components with telemetry indicators suggesting potential issues undergo deep diagnostics, while healthy components continue normal operation. This localized approach maintains high diagnostic accuracy for at-risk components without impacting overall device performance.
Solution Approach 2:
Instead of performing deep diagnostics on all components continuously, the system applies partial action by selecting only those components that meet specific telemetry-based criteria for enhanced monitoring. This selective approach provides sufficient diagnostic depth where needed while avoiding unnecessary performance impact on the overall system.
3Loss of information
If telemetry data is collected continuously, then more prediction information is available, but energy consumption and device complexity increase
Solution Approach 1:
The system implements periodic telemetry collection and analysis cycles rather than continuous monitoring. Telemetry data is gathered at scheduled intervals and processed in batches, allowing the system to maintain comprehensive prediction information while managing energy consumption through periodic rather than continuous operation of sensing and processing systems.
Solution Approach 2:
The system collects telemetry data at varying levels of detail based on component risk status. High-risk components monitored by the prediction model receive more frequent and detailed telemetry collection, while low-risk components use reduced monitoring. This partial action approach ensures sufficient data completeness for accurate predictions while minimizing overall energy consumption across the system.
Data Source
Figure 1
Figure 2
Figure 3
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.