Reliability Model Augmentation for Component Pairing Compatibility
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Solution Overview
Problem
Current reliability models for manufactured products are inadequate as they fail to account for the unique compatibility of individual components, leading to inaccurate predictions of product lifetime and maintenance needs, resulting in premature failure or excessive maintenance costs.
Innovation Solution
A method that uses manufacturing data and compatibility rules to determine the pairing compatibility of components, augmenting reliability models to provide customized maintenance plans and accurate product lifetime predictions tailored to each product.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional reliability models based on manufacturing specifications and engineering expertise are used, then the maintenance plan can be designed for the entire product population, but the model accuracy for individual products is insufficient
Solution Approach 1:
The patent segments the reliability model into two components: a base model derived from manufacturing specifications and engineering expertise that applies to the entire product population, and an augmentation component that incorporates individual product-specific data (such as sensor data, operational history, and actual performance metrics) to refine predictions for each specific product. This segmentation allows the system to maintain a manageable base model while adding individualized accuracy through incremental data-driven adjustments.
Solution Approach 2:
The patent performs preliminary action by pre-establishing the base reliability model using manufacturing specifications and engineering expertise before deployment. This pre-computed model serves as a foundation that can be quickly augmented with individual product data in the field, eliminating the need to build complex individual models from scratch for each product while still achieving high accuracy.
2Reliability
If individualized maintenance plans are created for each product, then the maintenance accuracy is improved, but the data processing complexity increases
Solution Approach 1:
The patent segments the maintenance planning process into a population-level component based on the base reliability model and a product-specific component based on individual augmentation data. This allows maintenance plans to be individualized without requiring complete re-analysis of all products, as the population-level insights are reused and adjusted only with product-specific information.
Solution Approach 2:
The patent implements feedback mechanisms where actual product performance data, sensor readings, and operational history are continuously fed back into the reliability model augmentation process. This feedback loop allows the system to learn from actual product behavior and refine individualized maintenance predictions over time, improving accuracy while using iterative rather than exhaustive processing.
3Measurement precision
If component pairing compatibility is analyzed using manufacturing data, then the product lifetime prediction accuracy is improved, but the analysis time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-analyzing component pairing compatibility using manufacturing data during the manufacturing or setup phase. Compatibility metrics and pairing quality scores are computed in advance and stored as part of the product's initial profile. When predicting product lifetime in the field, the system retrieves these pre-computed compatibility metrics rather than re-analyzing all component interactions, significantly reducing analysis time while maintaining accuracy.
4Reliability
If detailed manufacturing data from each component is collected and analyzed, then the customized maintenance plan accuracy is improved, but the data collection and storage requirements increase
Solution Approach 1:
The patent extracts only the most relevant manufacturing data and compatibility metrics from the complete set of component information. Rather than storing and processing all available manufacturing data, the system identifies and extracts key parameters such as component compatibility scores, critical manufacturing variations, and pairing quality metrics that have the greatest impact on product lifetime and maintenance needs. This extraction approach maintains prediction accuracy while minimizing data volume.
Solution Approach 2:
The patent inverts the traditional approach by not starting with complete manufacturing data and filtering down, but rather by identifying the specific compatibility metrics and data elements needed for accurate lifetime prediction and working backward to collect only those essential data points. This inversion reduces data collection requirements from the outset while maintaining the ability to create accurate customized maintenance plans.
Data Source
AI summary
A method of augmenting a reliability model of products including a particular product in use by an end user. The particular product includes a first component and a second component each having component attributes. The method also includes receiving first data of the first component and second data of the second component generated during use of the particular product by the end user and comparing the first data to the second data. The method also includes identifying a measure of compatibility between the components, determining pairing data based on the measure of compatibility, and applying a set of pairing rules to the pairing data. The method further includes modifying the reliability model based at least in part on the application of the pairing rules to the pairing data to generate an augmented reliability model and initiating one or more actions in association with the augmented reliability model.