Dynamic Diagnostic Test Sequencing via Probabilistic FMEA
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Solution Overview
Problem
Existing diagnostic systems rely heavily on the expertise of technical experts for sequencing diagnostic procedures, often lacking complete historical data, which can lead to inefficient and costly diagnostic testing due to incomplete information.
Innovation Solution
A computer-implemented method and apparatus that generates an optimized diagnostic test plan by determining a group of diagnostic tests related to a symptom using probabilistic failure mode analysis, dynamically selecting and ordering tests based on historical data and user preferences, and formatting procedures for display on a device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If diagnostic procedures are sequenced based on technical expert expertise, then diagnostic accuracy is maintained, but diagnostic time and cost increase due to incomplete historical information
Solution Approach 1:
The system performs preliminary analysis by collecting and analyzing historical diagnostic data before executing the diagnostic procedure. Probabilistic failure mode analysis is conducted in advance to determine the most likely failure causes, allowing the system to pre-order diagnostic tests based on statistical probability rather than relying solely on expert sequencing, thus reducing diagnostic time while maintaining accuracy
Solution Approach 2:
The system incorporates feedback loops where historical diagnostic outcomes are continuously collected and used to update the probabilistic failure mode analysis. This feedback mechanism allows the system to learn from past diagnostic cases and improve test sequencing over time, optimizing the balance between diagnostic accuracy and time efficiency
2Reliability
If comprehensive diagnostic tests are performed to ensure accurate diagnosis, then diagnostic reliability improves, but diagnostic cost increases
Solution Approach 1:
The system dynamically changes the parameters of diagnostic testing by adjusting which tests are performed and in what order, based on probabilistic failure mode analysis. Instead of performing all possible tests, the system selects and sequences tests according to their likelihood of detecting the actual failure, thereby reducing diagnostic cost while maintaining reliable diagnosis through statistically optimized test selection
3Productivity
If diagnostic procedures are optimized using probabilistic failure mode analysis, then diagnostic efficiency improves, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer of probabilistic failure mode analysis between the raw diagnostic data and the test sequencing decision. This intermediary component processes historical data and failure mode probabilities to generate optimized test sequences, thereby improving diagnostic efficiency while containing system complexity by modularizing the optimization function rather than embedding it throughout the entire diagnostic system
Data Source
AI summary
A dynamic diagnostic plan generator arranges diagnostic test procedures related to a vehicle symptom or operational problem in a sequence based on a probabilistic Failure Mode and Effects Analysis (FMEA). The diagnostic plan generator also tracks a vehicle state, and provides instructions for test preparation steps and instructions for performing the diagnostic test procedures. The plan generator further generates schematic illustrations of the diagnostic test procedures, and creates a diagnostic data structure containing information related to the diagnostic test procedures. In addition, the diagnostic plan generator sends and receives information regarding actual failure mode occurrences, for example, to and from a central database. Furthermore, the diagnostic plan generator facilitates the creation of failure mode tests by an expert diagnostics author.