Component Failure Prediction Model Using Aggregated Usage Data
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Solution Overview
Problem
Existing techniques for predicting component failures require extensive data accumulation, making it difficult to accurately predict failure rates for components that do not frequently fail, leading to inaccurate predictions without sufficient failure records.
Innovation Solution
A model generation apparatus and prediction method that group components based on usage status and failure records, generating prediction models for each group to estimate total and individual component failures, using sub-models for parameter estimation and distribution analysis to enhance prediction accuracy from sparse data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If failure rate is decided in advance for individual components using Poisson distribution, then prediction model can be established, but prediction accuracy deteriorates when failure data is insufficient
Solution Approach 1:
The patent combines multiple individual component prediction models into a unified prediction model that aggregates failure data across all components. This merging approach allows the system to leverage collective failure data from multiple components to improve prediction accuracy for individual components, even when individual component data is scarce. The unified model learns patterns from aggregated data and applies them to predict failures of specific components.
2Measurement precision
If failure records are accumulated for long periods to ensure sufficient data, then prediction accuracy improves, but response time to predict failures deteriorates
Solution Approach 1:
The patent performs preliminary actions by continuously collecting and preprocessing failure data from multiple components in real-time, maintaining an up-to-date aggregated dataset. This preliminary data aggregation and model training enable the system to provide accurate predictions immediately when needed, without requiring additional data accumulation time. The system is prepared in advance with a robust unified model that can predict failures based on current data patterns.
3Adaptability or versatility
If prediction model is generated for each individual component, then component-specific prediction is achieved, but data sparsity worsens due to limited failure records per component
Solution Approach 1:
The patent creates a universal prediction model that serves multiple functions: it aggregates data from all components to learn general failure patterns, while simultaneously providing component-specific predictions. The unified model is trained on aggregated failure data from multiple components and can predict failures for any individual component by incorporating that component's specific usage status and characteristics. This multi-functional approach allows the system to overcome data sparsity while maintaining component-specific prediction capability.
Data Source
AI summary
A model generation apparatus (2000) acquires component failure data in which a usage status is associated with a failure record of a component. The model generation apparatus (2000) generates, for each of a plurality of component groups, a prediction model for predicting the number of failures of each component included in the component group by using the component failure data relating to the component belonging to the component group. The prediction model computes a prediction value of the total number of failures of the components belonging to a corresponding component group from the usage status, and computes a prediction value of the number of failures of each component belonging to the component group from the computed prediction value of the total number of failures.