AI Troubleshooting Workflow Adaptation for Mechanical Diagnostics
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Solution Overview
Problem
Existing troubleshooting systems for mechanical products are inefficient due to their sequential nature, requiring users to follow each step regardless of relevance, leading to repetition and inconsistent results.
Innovation Solution
A system that uses artificial intelligence and machine learning to streamline and customize the troubleshooting workflow, allowing users to interact through natural language, voice, text, touch, and click interfaces, and learns from multiple users' actions to recommend the most relevant tests and repair steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequential troubleshooting guides are used, then comprehensive diagnostic coverage is achieved, but troubleshooting time and user effort increase significantly
Solution Approach 1:
The troubleshooting system dynamically adapts the diagnostic path based on user inputs, test results, and historical data. Instead of following a fixed sequential guide, the system adjusts the troubleshooting workflow in real-time to prioritize the most likely causes and skip already-performed tests, thereby reducing time while maintaining comprehensive diagnostic coverage.
Solution Approach 2:
The system incorporates feedback loops where user inputs, test results, and historical troubleshooting data are continuously processed to refine and personalize the diagnostic path. This feedback mechanism allows the system to learn from past interactions and optimize future troubleshooting sequences, reducing redundant steps while ensuring thorough diagnosis.
2Reliability
If comprehensive troubleshooting documentation is provided, then all possible issues are covered, but information overload and user confusion occur
Solution Approach 1:
The system provides customized troubleshooting information tailored to each user's specific situation, device type, and diagnosed issues. Instead of presenting all possible troubleshooting steps uniformly, it delivers localized, relevant information at each step, making the process easier to follow while maintaining comprehensive issue coverage through adaptive questioning and targeted guidance.
Solution Approach 2:
The comprehensive troubleshooting documentation is segmented into modular, context-specific steps based on diagnostic results and user inputs. The system divides the vast knowledge base into manageable segments that are presented only when relevant, reducing information overload while ensuring all necessary diagnostic steps are covered through progressive disclosure.
3Reliability
If standardized troubleshooting procedures are used, then consistency is achieved, but adaptability to individual user situations is reduced
Solution Approach 1:
The system maintains standardized diagnostic protocols and decision trees for consistency, but dynamically adapts the execution path based on individual user inputs, device configurations, and historical data. This allows the system to follow consistent diagnostic logic while customizing the specific steps and information presented to each user, achieving both reliability and adaptability.
Solution Approach 2:
The troubleshooting system uses a universal platform that handles multiple device types and issues through standardized procedures, while simultaneously adapting to individual user situations through personalized interfaces and context-aware guidance. The same core diagnostic engine serves diverse scenarios, providing both consistency across cases and customization for each user.
Data Source
AI summary
The system obtains a first input indicating a machine experiencing an issue and one or more issues with the machine. Based on the first input, the system provides a first plurality of relevance indicators and a first plurality of troubleshooting procedures. The system receives a second input including a performed troubleshooting procedure among the first plurality of troubleshooting procedures. The system receives an indication of a result associated with the performed troubleshooting procedure. Based on the indication of the result associated with the performed troubleshooting procedure, the system generates a second plurality of relevance indicators associated with a second plurality of troubleshooting procedures, where the second plurality of relevance indicators indicates a second troubleshooting procedure to perform next.