Computer System RUL Prediction via Telemetry Metrics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for assessing computer system reliability, such as mean-time-between-failure (MTBF) estimation, provide limited insight into the remaining useful life (RUL) of a computer system, making it difficult to accurately predict its operational lifespan, especially in safety-critical and eCommerce applications where timely decision-making is crucial.
Innovation Solution
A system that collects telemetry metrics from computer systems, uses non-linear, non-parametric regression models, and sequential probability ratio tests (SPRT) to generate RUL predictions, incorporating multivariate state estimation techniques and logistic regression models to calculate the remaining useful life based on historical failure data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If MTBF estimation is used to assess computer system reliability, then the system can provide a simple reliability metric, but the insight into remaining useful life is limited and inaccurate
Solution Approach 1:
The patent transforms the reliability assessment from static MTBF parameters to dynamic RUL predictions by continuously monitoring telemetry metrics (temperature, voltage, current, usage rating) and updating predictions based on real-time system state changes and degradation patterns
Solution Approach 2:
The patent replaces traditional mechanical reliability models with data-driven machine learning models (random forests, neural networks) that process telemetry data to predict RUL, substituting simple statistical models with intelligent systems capable of capturing complex degradation patterns
2Measurement precision
If telemetry metrics are collected and processed through complex models to generate RUL predictions, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the prediction system into distinct functional modules: telemetry data collection, data preprocessing, machine learning model inference, and RUL calculation. This modular architecture allows each component to be optimized independently and simplifies maintenance
Solution Approach 2:
The patent introduces an intermediary data processing layer that transforms raw telemetry metrics into normalized features, then feeds them to machine learning models. This intermediary layer abstracts the complexity from the user interface, providing simple RUL predictions while handling complex data processing internally
Data Source
AI summary
One embodiment of the present invention provides a system for predicting a remaining useful life (RUL) for a computer system. The system starts by collecting values for at least one telemetry metric from the computer system while the computer system is operating. The system then uses the collected values to generate a RUL prediction for the computer system or a component within the computer system.