Electronic Component State Determination via Hybrid Data Fusion
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Solution Overview
Problem
Existing methods for determining the condition of electronic components in the field are limited in their ability to adaptively assess degradation and predict failures, often relying on static simulations and hardware redundancy, which can lead to resource inefficiencies and untimely maintenance.
Innovation Solution
A hybrid AI-based system that combines static production data with dynamic field data to continuously determine the condition of electronic components, using machine learning algorithms to adaptively assess degradation and predict failures, allowing for state-dependent maintenance and operation mode adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hardware redundancy is implemented to ensure reliability, then the reliability of electronic systems is improved, but the resource usage and device complexity increase
Solution Approach 1:
The patent applies preliminary action by evaluating production data (manufacturing quality, assembly conditions) before the component enters field operation. This allows the system to predict potential failures and schedule maintenance proactively, eliminating the need for redundant hardware while maintaining reliability through informed maintenance decisions.
Solution Approach 2:
The patent implements feedback by continuously monitoring field data (operational parameters, environmental conditions) and comparing it with production data. This feedback loop enables adaptive maintenance scheduling based on actual component condition, replacing static redundancy with dynamic, condition-based resource allocation.
2Manufacturing precision
If static model simulations are used to determine component state, then the manufacturing precision can be controlled, but the adaptability to real-world conditions deteriorates
Solution Approach 1:
The patent merges static production data (manufacturing quality metrics, assembly conditions) with dynamic field data (operational parameters, environmental conditions) into a unified assessment model. This combination allows the system to maintain manufacturing precision while adapting to real-world operating conditions, achieving both accuracy and versatility.
Solution Approach 2:
The patent transitions from static model simulations to a dynamic assessment approach that continuously updates component state evaluation using current field data. This dynamic approach maintains the precision of manufacturing data while adapting to changing operational conditions, enabling accurate real-time assessments.
3Productivity
If condition monitoring methods are used to predict failures, then the productivity can be improved through early maintenance, but the measurement precision requirements increase
Solution Approach 1:
The patent uses production data as an intermediary that bridges manufacturing quality with field operational data. This intermediary allows the system to leverage existing manufacturing information to enhance field monitoring, reducing the burden on measurement systems while maintaining high productivity through accurate failure prediction.
Data Source
AI summary
The invention relates to a method for determining the state (16) of an electronic component (18) using a state determination device (10), comprising the steps of: - providing production data (20) of the electronic component (18) to an electronic computing unit (14) of the state determination device (10); - acquiring at least one parameter (26) currently characterizing the electronic component (18) using a acquiring unit (12) of the state determination device (10); and - determining the state (16) as a function of the provided production data (20) and the at least one parameter (26) currently characterizing the electronic component (18) using the electronic computing unit (14). The invention further relates to a computer program product, a computer-readable storage medium, and a state determination device (10).