Part Utilization Inference Across Aeronautical Vehicle Configurations
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Solution Overview
Problem
Determining part utilization across diverse machine configurations is challenging due to partial or incomplete knowledge of part applicability and operational usage rates, particularly in complex machines like aeronautical vehicles.
Innovation Solution
A computing system and method that utilize an inference model to infer part applicability based on known priors and relationships between different machine configurations, providing probabilistic predictions on part utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If part applicability is determined for diverse machine configurations using traditional methods, then part availability can be ensured for known configurations, but part utilization cannot be accurately determined for configurations with incomplete knowledge
Solution Approach 1:
The patent introduces an inference model as an intermediary between known part applicability data and unknown configurations. This model probabilistically infers part applicability for machine configurations where direct knowledge is incomplete or unavailable, enabling reliable part availability determination across diverse configurations without requiring exhaustive empirical data for each specific configuration
Solution Approach 2:
The patent creates a probabilistic model that copies and adapts part applicability patterns from known machine configurations to unknown configurations. By learning from training data representing various machine configurations and their part usage patterns, the system can predict part applicability for new configurations based on similarities to known configurations, effectively copying knowledge across different scenarios
2Measurement precision
If traditional data collection methods are used to determine part utilization, then accurate data can be obtained for specific machine configurations, but the process becomes inefficient and incomplete for diverse machine populations
Solution Approach 1:
The patent performs preliminary actions by training the inference model on comprehensive part applicability data from multiple machine configurations before actual utilization determination is needed. This pre-processing of data and model training enables rapid, accurate queries about part utilization for any configuration without requiring time-consuming data collection at the point of need
Solution Approach 2:
The patent replaces traditional mechanical data collection methods (physical tracking, manual recording, direct observation) with an information-based inference system. The probabilistic model substitutes for physical data gathering mechanisms, using computational inference to determine part utilization patterns across diverse machine populations efficiently
3Adaptability or versatility
If part applicability is inferred probabilistically for unknown configurations, then part utilization can be determined for diverse machine populations, but uncertainty increases compared to known configurations
Solution Approach 1:
The patent implements a dynamic inference system where part applicability probabilities are not fixed but adapt based on the specific query and available training data. The model dynamically adjusts its predictions based on the relationship between the training configuration and the queried configuration, providing higher certainty for similar configurations and lower certainty for dissimilar ones, allowing the system to balance adaptability with reliability
Data Source
AI summary
A computing system and method for determining part utilization by machines, such as aeronautical vehicles, are disclosed. According to an example, a first data set is obtained that identifies a physical parameter defining a configuration of a first population of aeronautical vehicles and a part applicability of an identified part for the first population. A second data set is obtained that identifies a physical parameter defining a configuration of a second population of aeronautical vehicles that differs from the physical parameter of the first population. An inferred part applicability is determined for the second population that describes a probability that the identified part is utilized by the second population. A part utilization value is output that identifies a measurement of part utilization of the identified part for the second population that is based on the inferred part applicability.