Deep Recurrent Neural Network for Predicting Information Handling Resource Failures
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Solution Overview
Problem
Information handling systems face downtime due to failures in resources like hard disk drives and batteries, necessitating predictive maintenance to avoid costly replacements.
Innovation Solution
A deep recurrent neural network (RNN/LSTM) pattern recognition engine is trained with telemetry and failure statistics to predict resource failures, enabling proactive replacement and minimizing system downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If replacement of failed devices is performed after failure occurs, then system reliability is maintained through replacement, but system downtime increases and productivity is lost
Solution Approach 1:
The system performs preliminary actions by training the neural network model with historical telemetry data and failure statistics before actual failures occur. The model continuously analyzes operational data to predict potential failures in advance, enabling proactive replacement scheduling that minimizes system downtime while maintaining reliability.
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting telemetry data from information handling resources, comparing actual performance against predicted failure patterns, and updating the neural network model with new failure statistics. This closed-loop feedback enables the system to improve prediction accuracy over time and adjust replacement strategies accordingly.
2Loss of time
If predictive maintenance is implemented using deep recurrent neural networks, then system downtime is reduced through early failure detection, but device complexity and computational requirements increase
Solution Approach 1:
The patent introduces a neural network model as an intermediary between raw telemetry data and failure predictions. This intermediary component processes complex patterns in operational data that would be difficult to detect using traditional methods, enabling accurate failure prediction while maintaining a modular system architecture that can be integrated into existing infrastructure.
Solution Approach 2:
The system replaces traditional mechanical monitoring and manual analysis methods with computational intelligence. Instead of using conventional threshold-based monitoring or human experts to analyze telemetry data, the patent employs deep recurrent neural networks that automatically learn failure patterns from historical data, reducing the need for complex manual intervention systems.
3Device complexity
If traditional monitoring methods are used without predictive analytics, then device complexity remains low, but failure detection precision and reliability prediction capability are insufficient
Solution Approach 1:
The system transforms the monitoring approach by changing key parameters from simple threshold-based alerts to multi-dimensional pattern recognition. The neural network analyzes multiple telemetry parameters simultaneously (temperature, vibration, power consumption, etc.) and their temporal relationships, detecting subtle patterns that indicate impending failures long before traditional monitoring methods would trigger alerts.
Data Source
AI summary
In accordance with embodiments of the present disclosure, an information handling system may include a processor and a non-transitory computer-readable medium having stored thereon a program of instructions executable by the processor. The program of instructions may be configured to, when read and executed by the processor, receive telemetry data associated with one or more information handling resources, receive failure statistics associated with the one or more information handling resources, and correlate the telemetry data and the failure statistics to create training data for a pattern recognition engine configured to predict a failure status of an information handling resource from operational data associated with the information handling resource.