Aircraft Maintenance Critical Path Identification
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Solution Overview
Problem
MRO for vehicles, such as aircraft, is logistically challenging due to numerous interconnected jobs with missing dependency and sequencing information, making critical path identification difficult and leading to poor outcomes.
Innovation Solution
A method that selects data elements from a data hierarchy, generates clusters based on dependencies, identifies jobs by start or end dates, and approximates a critical path by generating paths for each cluster, allowing for efficient display and management of maintenance steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data elements are processed individually without clustering, then job sequencing information can be identified directly, but computational resources and processing time increase significantly
Solution Approach 1:
The patent segments the large set of maintenance data elements into multiple clusters based on hierarchical relationships and dependencies. Each cluster represents a group of related jobs that can be processed together, reducing the computational complexity compared to processing all data elements individually while still maintaining the ability to identify critical paths and job sequencing information accurately.
2Loss of information
If all MRO data is processed to identify critical paths, then complete job sequencing information is obtained, but computational complexity and resource requirements increase
Solution Approach 1:
The patent divides the comprehensive MRO data processing task into smaller cluster-based subtasks. By processing data in hierarchical clusters rather than as a single large dataset, the system reduces computational complexity while maintaining completeness of job dependency information through the systematic aggregation of cluster-level results.
Solution Approach 2:
The patent introduces a hierarchical dimension to the data processing approach, organizing data elements into multiple levels of clusters. This dimensional organization allows the system to manage complexity by working with aggregated representations at higher levels while preserving detailed dependency information at lower levels, thereby reducing overall computational requirements.
3Ease of operation
If hierarchical data structures are used to organize MRO information, then data organization and retrieval improve, but processing complexity increases
Solution Approach 1:
The patent segments the hierarchical data structure into manageable clusters at each level of the hierarchy. This segmentation allows the system to leverage the organizational benefits of hierarchical structures for improved data retrieval and access while reducing processing complexity by operating on clustered groups rather than individual data elements throughout the entire hierarchy.
Data Source
AI summary
Techniques relating to vehicle maintenance are disclosed. These techniques include selecting a first data element and generating a plurality of clusters of data elements relating to maintenance of a vehicle, based on clustering data elements in one or more layers in a data hierarchy that depend a the first layer, and identifying one or more jobs relating to maintenance of the vehicle in each of the plurality of clusters, based on: (i) a start date for the respective job or (ii) an end date for the respective job. The techniques further include generating a path for each of the plurality of clusters based on the identified one or more jobs, each respective path comprising a plurality of jobs, approximating a critical path relating to maintenance of the vehicle based on the generated paths for each of the plurality of clusters, and maintaining the vehicle using the approximated critical path.