Fault Diagnosis Decision Tree for Electronic Devices
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Solution Overview
Problem
Current fault diagnosis methods for electronic devices, such as mobile phones, are inefficient and time-consuming as they require manual analysis of each failure, making it difficult to quickly identify the root cause of issues.
Innovation Solution
A fault diagnosing system with a calculating module to determine probability of fault reasons, a constructing module to build a decision tree based on these probabilities, and a solving module to display the most likely fault reasons, utilizing a database to dynamically update and prioritize fault types and reasons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of each failure is performed, then thorough fault diagnosis can be achieved, but time consumption increases significantly
Solution Approach 1:
The patent segments the fault diagnosis process into three distinct modules: a calculating module that computes fault probabilities, a constructing module that builds decision tree structures, and a solving module that identifies root causes. This segmentation allows each module to specialize in specific tasks, improving overall diagnosis efficiency while maintaining accuracy through systematic breakdown of the diagnostic workflow.
Solution Approach 2:
The patent introduces a decision tree as an intermediary structure that mediates between raw fault data and final diagnosis results. The decision tree organizes fault reasons hierarchically with probability weights, serving as a bridge that transforms complex manual analysis into structured, automated reasoning processes that reduce diagnosis time while preserving diagnostic accuracy.
2Measurement precision
If comprehensive fault analysis is performed manually, then root cause identification can be achieved, but production efficiency decreases
Solution Approach 1:
The fault diagnosis system performs self-service by automatically calculating fault probabilities, constructing decision trees, and identifying root causes without requiring manual intervention. The calculating module autonomously computes probabilities based on historical data, the constructing module automatically builds the decision structure, and the solving module independently determines root causes, thereby eliminating time-consuming manual analysis and improving production efficiency.
Solution Approach 2:
The patent transforms the fault diagnosis process by changing parameters from qualitative manual assessment to quantitative probability-based analysis. By introducing probability weights for different fault reasons and using numerical calculations to determine root causes, the system achieves both accurate root cause identification and improved productivity through automated parameter-driven diagnosis.
3Measurement precision
If detailed fault checking is performed, then accurate diagnosis can be achieved, but time and effort requirements increase
Solution Approach 1:
The patent performs preliminary action by pre-calculating fault probabilities and pre-constructing decision tree structures based on historical fault data. The calculating module prepares probability values in advance, and the constructing module creates the decision framework beforehand, so that when actual fault diagnosis is needed, the solving module can quickly query and analyze pre-prepared information, reducing both time and effort requirements while maintaining diagnostic accuracy.
Data Source
AI summary
A method for diagnosing faults in products of one type includes determining a fault type of the products and all of the underlying reasons for the fault which correspond to the fault type. A probability of each underlying reason is calculated and a decision tree is constructed, a root node of the decision tree being the determined fault type and all the underlying reasons being child nodes of the root node. When the decision tree is solved, the underlying reasons for the fault of that type of product can be presented.