Diagnostic Test Sequence Optimization via Probabilistic Analysis
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Solution Overview
Problem
Diagnostic test sequences in existing systems are highly dependent on the expertise of technical experts, often lacking complete information on historical outcomes and statistical data, leading to inefficient and costly diagnostic processes.
Innovation Solution
A computer-implemented method and apparatus that optimizes diagnostic test sequences by determining a group of diagnostic tests related to a symptom using probabilistic failure mode analysis, incorporating vehicle history, and user preferences to select the most effective tests in sequence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If diagnostic test sequences are developed based on technical expert expertise, then diagnostic accuracy is improved, but diagnostic time and cost increase due to lack of statistical optimization
Solution Approach 1:
The system implements feedback by collecting historical diagnostic test outcome data and using it to iteratively optimize test sequences. The probabilistic model is continuously refined based on actual diagnostic results, allowing the system to learn from past experiences and improve future diagnostic efficiency while maintaining accuracy.
Solution Approach 2:
The system changes the parameter of test sequence ordering from expert-defined static sequences to statistically-optimized dynamic sequences. By using probabilistic failure mode analysis and historical outcome data, the system dynamically adjusts the order and selection of diagnostic tests to minimize diagnostic time while maintaining reliability.
2Reliability
If diagnostic test sequences are developed by technical experts, then diagnostic procedures are effective, but device complexity increases due to manual procedure development
Solution Approach 1:
The system enables self-service by automatically generating and optimizing diagnostic test sequences without requiring manual intervention from technical experts. The probabilistic model autonomously analyzes historical data, determines failure mode probabilities, and constructs optimized test sequences, reducing the complexity of procedure development while maintaining effectiveness.
Solution Approach 2:
The system replaces the mechanical process of manual expert-driven procedure development with an automated computational system. Instead of experts manually designing test sequences based on experience, the system uses probabilistic algorithms and historical data analysis to automatically generate optimized sequences, reducing human involvement and procedural complexity.
3Productivity
If complete historical outcome information is collected for diagnostic optimization, then diagnostic efficiency is improved, but information processing requirements increase
Solution Approach 1:
The system extracts only the essential features from historical diagnostic data that are relevant to failure mode probability calculation. Instead of processing all raw diagnostic data, the system selectively extracts outcome information needed for probabilistic analysis, reducing data processing requirements while maintaining diagnostic efficiency improvements.
Data Source
AI summary
A diagnostic test sequence optimizer includes a diagnostic test selector that determines a group of diagnostic test procedures related to a specific symptom and vehicle type from a pool of diagnostic procedures. A failure mode analyzer then selects one or more factors that can affect resolution of a vehicle operational problem and performs a failure mode analysis to quantify a comparative utility of the individual tests, and a factor weighter assigns a weight to each of the factors. A vehicle receiver receives information regarding the history of the test subject vehicle, and a sequence optimizer places the diagnostic test procedures in an optimized sequence in accordance with the comparative utilities of the individual diagnostic procedures, user preferences and a Failure Mode and Effects Analysis compiled by the manufacturer of the vehicle.