Estimating Internal Computer Humidity via Performance Parameters
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Solution Overview
Problem
Maintaining uniform relative humidity inside computer systems is challenging due to dynamic workloads and environmental changes, leading to reliability issues, and existing measurement methods are costly and complex.
Innovation Solution
A system that estimates relative humidity inside a computer system by monitoring performance parameters and using a relative humidity model trained with both internal and external humidity data, employing pattern-recognition and multivariate state estimation techniques to predict internal humidity levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If relative humidity sensors are placed inside the computer system to measure humidity, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses existing performance parameters (temperature, power consumption, fan speed) as intermediaries to indirectly estimate relative humidity. Instead of directly measuring humidity with sensors, the system uses these readily available performance metrics as mediators that correlate with humidity conditions inside the computer system, thereby avoiding the need for additional humidity sensors.
Solution Approach 2:
The patent creates a virtual model (copy) of the humidity measurement function using machine learning algorithms. Rather than physically placing humidity sensors inside the system, the system trains a model using external humidity sensor data and performance parameters, then uses this learned model to estimate internal humidity conditions, effectively copying the measurement capability through software rather than hardware.
2Measurement precision
If relative humidity sensors are placed inside the computer system to measure humidity, then measurement precision is improved, but cost increases
Solution Approach 1:
The patent replaces expensive, delicate humidity sensors with a software-based estimation model that uses inexpensive, readily available performance data. The solution treats the humidity measurement function as something that can be achieved through computation rather than through expensive physical sensing hardware, dramatically reducing system cost while maintaining adequate measurement capability.
Solution Approach 2:
The system uses existing performance parameters as intermediaries to obtain humidity information without requiring expensive humidity sensors. By leveraging temperature, power consumption, and fan speed data that are already being collected for other purposes, the system obtains humidity estimates at minimal additional cost.
3Device complexity
If external relative humidity is monitored and used for estimation, then device complexity is reduced, but measurement precision may worsen due to spatial variations
Solution Approach 1:
The patent transforms the estimation problem by changing from direct humidity measurement to inferring humidity from multiple performance parameters. The machine learning model learns the complex relationships between performance metrics (temperature, power, fan speed) and humidity, effectively changing the measurement approach from direct sensing to indirect inference through multiple correlated variables.
Solution Approach 2:
The system uses feedback from multiple performance parameters to continuously refine humidity estimates. The machine learning model processes ongoing data from temperature sensors, power management systems, and fan control to dynamically adjust humidity estimates, compensating for spatial variations through the combined information from multiple feedback sources.
Data Source
AI summary
One embodiment of the present invention provides a system that estimates the relative humidity inside a computer system. During operation, a set of performance parameters of the computer system and an external relative humidity outside of the computer system are monitored. Then, the relative humidity inside the computer system is estimated based on the set of performance parameters, the external relative humidity, and a relative humidity model, wherein training of the relative humidity model includes measuring an external training relative humidity outside of the computer system and a training relative humidity inside the computer system while monitoring the set of performance parameters of the computer system.