IHS Accident Diagnosis via Fan and Thermal Data Comparison
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Solution Overview
Problem
Mobile Information Handling Systems (IHSs) face challenges in diagnosing accidents, such as drops or impacts, which can result in hidden damage that manifests as difficult-to-diagnose errors over time, increasing support burdens on Original Equipment Manufacturers (OEMs).
Innovation Solution
The implementation of systems and methods within IHSs that include processors and memories with program instructions to collect fan data, thermal data, or acoustic data in response to accidents. These data collections are compared to stored baseline data to perform accident remediation or mitigation operations, which may include notifying users or IT decision-makers, initiating data backups, or dispatching replacement parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If mobile IHSs are designed for increased mobility and versatility, then user convenience is improved, but the chances of accidents and hidden damage increase
Solution Approach 1:
The system performs preliminary diagnostic actions by collecting baseline fan data, thermal data, and acoustic data during normal operation before accidents occur. This baseline data is stored and later compared against post-accident measurements to detect hidden damage, enabling early detection before failures manifest.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring fan performance, thermal characteristics, and acoustic emissions, then comparing real-time data against baseline values. When deviations indicate potential damage from accidents, the system provides feedback through notifications to users or IT decision-makers, enabling proactive response.
2Measurement precision
If comprehensive accident diagnosis is implemented through data collection and comparison, then diagnostic accuracy is improved, but device complexity increases
Solution Approach 1:
The system uses a universal diagnostic framework that collects multiple types of data (fan performance, thermal characteristics, acoustic emissions) through a single integrated approach. The same baseline data collection and comparison mechanism applies across different components, simplifying the overall system architecture while maintaining comprehensive diagnostic capability.
Solution Approach 2:
The system performs self-diagnosis by automatically collecting diagnostic data, comparing it against stored baselines, and generating notifications without requiring external technician intervention. This self-service capability reduces the need for complex manual troubleshooting procedures while maintaining high diagnostic accuracy.
3Difficulty of detecting and measuring
If manual troubleshooting and root cause analysis are performed by technicians, then diagnostic depth is improved, but support burden and time required increase
Solution Approach 1:
The system automatically performs diagnostic operations by collecting baseline data during normal operation, comparing post-accident measurements against these baselines, and generating notifications without requiring technician intervention. This eliminates the need for manual troubleshooting while maintaining comprehensive diagnostic capability.
Solution Approach 2:
The system performs preliminary diagnostic actions by pre-collecting and storing baseline fan data, thermal data, and acoustic data during normal operation. This baseline information is ready for immediate comparison against post-accident measurements, enabling rapid automated diagnosis without requiring technicians to perform initial data collection or analysis.
4Reliability
If continuous monitoring of fan data, thermal data, and acoustic data is implemented, then early detection of hidden damage is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic monitoring by collecting baseline data at defined intervals during normal operation and performing comparisons at specific trigger events such as accidents. This periodic approach maintains the ability to detect hidden damage while reducing continuous energy consumption compared to constant real-time monitoring.
Data Source
AI summary
Systems and methods for diagnosing accidents in Information Handling Systems (IHSs) are described. In some embodiments, an IHS may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause an Operating System (OS) to: in response an accident, collect fan data and, based at least in part upon a comparison between collected fan data and stored fan data, perform an accident remediation or mitigation operation.