Statistical Configuration Analysis for Digital System Failure Prediction
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Solution Overview
Problem
The challenge in the digital systems manufacturing process is identifying latent defects that may cause 'infant mortality' failures, which are not detected by standard functional tests, leading to increased warranty costs and customer dissatisfaction. Existing methods require specialized and costly testing for each system configuration, increasing production time and expense.
Innovation Solution
Collecting and processing operational configuration parameter values from multiple systems to establish a statistical norm, identifying statistical outliers that correlate with future failures, and using these to predict and prevent latent defects without invasive testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized testing systems and programs are developed for each system configuration, then detection precision of latent defects is improved, but device complexity and production cost increase
Solution Approach 1:
The patent applies universality by developing a single statistical testing framework that can be applied across multiple system configurations. Instead of creating unique testing systems for each platform, the invention uses universal statistical methods (mean and standard deviation calculations) that work across different system types, thereby reducing testing system complexity while maintaining detection precision for latent defects.
Solution Approach 2:
The patent changes the approach from fixed, configuration-specific testing parameters to statistical parameters (mean and standard deviation) that adapt to different system configurations. By using statistical norms derived from operational configuration parameters, the testing method can accommodate various system designs without requiring customized testing programs for each configuration.
2Reliability
If highly specialized testing is performed on each system, then reliability of failure prediction is improved, but productivity decreases due to increased production time
Solution Approach 1:
The patent applies preliminary action by collecting and storing operational configuration parameters during the normal manufacturing process before systems are shipped. This preliminary data collection enables later statistical analysis to predict failures without requiring time-consuming specialized testing during production, thereby maintaining high productivity while improving failure prediction reliability.
Solution Approach 2:
The patent substitutes mechanical/invasive testing systems with a statistical analysis approach. Instead of using complex hardware testing systems that slow down production, the invention uses software-based statistical methods (comparing operational parameters against mean and standard deviation) to predict failures, thereby maintaining high productivity while improving reliability.
3Measurement precision
If unique testing programs are developed for each platform, then measurement precision for platform-specific defects is improved, but loss of time in product development increases
Solution Approach 1:
The patent applies universality by creating a single statistical testing framework that can be adapted to any platform. The same mean and standard deviation calculation methodology works across different system types, eliminating the need to develop unique testing programs for each platform while maintaining the ability to detect platform-specific defects through configuration-specific parameter analysis.
Data Source
AI summary
The use of operational configuration parameters to predict digital system failures is described herein. At least some illustrative embodiments include a method that includes initializing a digital system (the initializing comprising determining an operational configuration of at least part of the digital system), saving the operational configuration to a database stored on the digital system, reading the operational configuration from the database and comparing the operational configuration to a reference configuration, and identifying the digital system as being at risk of a future failure if at least one parameter of the operational configuration differs from the at least one same parameter of the reference configuration by more than a tolerance value.