Pattern Analyzer for Vehicle Rollout Defect Decisions
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Solution Overview
Problem
Fleet supervisors' decisions on vehicle rollout can lead to vehicle deterioration due to unknown gaps or flaws in their experience-based approaches, resulting in increased maintenance costs and safety risks.
Innovation Solution
A decision assistance system that analyzes historical data to generate defect indicator groupings and confidence scores, using a pattern analyzer to recommend whether a vehicle should be deployed or maintained, thereby aiding supervisors in making informed rollout decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fleet supervisors make rollout decisions based on their experience, then decisions can be made quickly, but the decisions may contain gaps or flaws leading to vehicle deterioration
Solution Approach 1:
The patent introduces a pattern analyzer system as an intermediary between the historical defect data and the supervisory decision-making process. This automated system analyzes defect indicator groupings and generates confidence scores to assist supervisors, combining the speed of automated analysis with human judgment to improve decision reliability without sacrificing productivity
Solution Approach 2:
The system implements feedback by continuously learning from historical supervisory decisions and their outcomes. The pattern analyzer uses historical defect data and supervisory actions to refine its defect indicator groupings and confidence score calculations, creating a closed-loop system that improves decision accuracy over time while maintaining rapid decision-making capability
2Reliability
If more defect analysis is performed to improve decision accuracy, then vehicle deterioration can be prevented, but system complexity increases
Solution Approach 1:
The patent segments the complex defect analysis task into manageable components: defect indicators are grouped into specific combinations, each with associated confidence scores. The pattern analyzer processes these segmented defect groups independently, making the overall system more manageable and less complex while maintaining high decision accuracy through systematic analysis of each defect combination
3Measurement precision
If supervisory decisions to remove vehicles from service are assumed correct, then the pattern analyzer can be trained effectively, but false positives may occur
Solution Approach 1:
The patent applies partial action by assuming supervisory decisions are correct only for the specific case of removing vehicles from service (not for all decision types). This selective assumption allows effective training on rollback prevention while the system remains cautious about false positives through confidence score thresholds that can be adjusted to balance accuracy and false alarm rates
Data Source
AI summary
A historical task database relating vehicle rollout decisions, vehicle maintenance states and subsequent deteriorations is created. A pattern analyzer may use an item-set mining algorithm on the task database to recommend whether a vehicle with its current maintenance state should be deployed. A supervisor uses this recommendation to make a rollout decision. These decisions are added to the database. Heuristic rules are defined to determine if the rollout decision was correct. The system to learns when a supervisor continues to make costly rollout errors. The system also discovers combinations of defects that lead to a rapid deterioration and makes recommendations that the vehicle be sent for maintenance rather than being rolled out.