Fault-Finding Tree for Medical Imaging Diagnostics
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Solution Overview
Problem
Medical imaging systems require efficient fault-finding methods to quickly identify and resolve system failures, as they are complex and often require unplanned downtime, leading to increased costs and patient scheduling challenges during reactive maintenance.
Innovation Solution
A fault-finding tree system is developed, comprising nodes and edges labeled with root causes, solutions, actions, and time-to-complete values, which iteratively recommends actions based on historical fault-finding sequences, allowing field service engineers to efficiently diagnose and resolve issues using a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a field service engineer manually diagnoses fault causes in medical imaging systems, then the root cause can be identified through expert judgment and experience, but the diagnostic time is extended and the complexity of the fault-finding process increases
Solution Approach 1:
The system pre-generates fault-finding trees before actual fault diagnosis occurs. Historical fault data is collected and processed in advance to build structured diagnostic pathways that guide engineers through systematic troubleshooting, eliminating the need for engineers to rely solely on experience during urgent repairs.
Solution Approach 2:
The patent introduces an intelligent intermediary system that acts as a bridge between historical fault data and the field service engineer. This intermediary automatically generates and updates fault-finding trees, providing structured diagnostic guidance that combines historical wisdom with real-time decision support, reducing both diagnostic time and complexity.
2Measurement precision
If comprehensive fault-finding procedures are implemented to cover all possible root causes, then the accuracy of fault identification improves, but the complexity of the fault-finding system increases
Solution Approach 1:
The fault-finding system is segmented into hierarchical trees with multiple levels of granularity. Each fault-finding tree is structured with root nodes representing major fault categories and leaf nodes representing specific root causes, allowing engineers to navigate from general to specific diagnoses systematically without being overwhelmed by all possible faults simultaneously.
Solution Approach 2:
The fault-finding trees are dynamically generated and updated based on historical fault data rather than being static comprehensive manuals. The system adapts the complexity of diagnostic pathways to the specific fault scenario encountered, presenting only the relevant diagnostic steps needed for the current situation rather than all possible faults.
3Measurement precision
If historical fault data is extensively collected and analyzed to improve diagnostic recommendations, then the accuracy of recommended actions improves, but the computational resources and processing time required increase
Solution Approach 1:
Historical fault data is collected and analyzed in advance to pre-generate fault-finding trees and statistical information. This preliminary processing converts raw historical data into structured diagnostic knowledge that can be quickly queried during actual fault events, reducing computational burden during time-critical repair scenarios.
Solution Approach 2:
The system extracts only the essential diagnostic information from extensive historical data to build compact fault-finding trees. Rather than processing all historical data during each diagnostic event, the system extracts key patterns and relationships into structured trees that provide high diagnostic accuracy with minimal computational resources during actual use.
Data Source
AI summary
A non-transitory computer readable medium (107, 127) stores instructions executable by at least one electronic processor (101, 113) to perform a method (200, 300) of generating a recommendation engine for recommending actions during performance of a fault-finding task. The method comprises: converting a collection of historical fault-finding process sequences (140) into a fault-finding tree (130) having nodes (132), action edges (135) and outcome edges (137) connecting the nodes. The action edges are labeled with actions of the historical fault-finding process sequences and the outcome edges are labeled with outcomes of the historical fault-finding process sequences. The nodes include terminal nodes (133) labeled with root causes or solutions identified by the historical fault-finding process sequences. A visualization (142) of the fault-finding tree is provided on a user interface (UI) (120) on a service device (102) operable by a field service engineer (FSE).