Root Cause Analysis Model from AR Peer Sessions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Inefficient root cause analysis in field technician troubleshooting due to lack of expertise, leading to increased time spent on troubleshooting or seeking expert advice, as less experienced technicians struggle to identify and locate the root cause of problems.
Innovation Solution
Development of a root cause analysis model trained using labeled data from augmented reality peer assistance sessions, classifying components into root causes, symptoms, and instructions, and continuously fine-tuning the model with multi-modality data from these sessions to automatically extract and generate RCA models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If less experienced technicians perform troubleshooting independently, then expert assistance time is reduced, but troubleshooting time and efficiency deteriorate
Solution Approach 1:
An automated root cause analysis system acts as an intermediary between technicians and expert knowledge. The system processes multi-modality data from augmented reality peer assistance sessions, classifies components using trained classifiers, and generates RCA models that guide technicians through troubleshooting steps, reducing direct expert involvement while maintaining high troubleshooting efficiency
Solution Approach 2:
The system performs preliminary analysis of troubleshooting data by training classifiers on labeled data from previous peer assistance sessions. This preliminary action creates reusable RCA models that contain classified components and relationships, enabling technicians to independently troubleshoot without requiring real-time expert intervention
2Reliability
If manual root cause analysis is performed by technicians, then expertise development occurs, but time consumption and complexity increase
Solution Approach 1:
The system creates simplified copies of expert troubleshooting knowledge by training classifiers on labeled data from peer assistance sessions. These classifiers generate RCA models that replicate expert analysis capabilities, allowing automated identification of root causes with high accuracy while significantly reducing the time technicians need to spend on manual analysis
3Productivity
If automated classification of components is implemented, then troubleshooting speed increases, but model complexity and training requirements worsen
Solution Approach 1:
The automated classification system is segmented into modular components: data collection from augmented reality sessions, labeled data preparation, classifier training on specific component types, and model generation. This segmentation allows the system to achieve high troubleshooting speed through specialized classifiers for different component categories while managing overall system complexity through modular architecture
Data Source
AI summary
In an approach for deducing a root cause analysis model, a processor trains a classifier based on labeled data to identify entities. A processor trains the classifier with first taxonomy and ontology. A processor uses the classifier to classify each component from one or more augmented reality peer assistance sessions into a class. A processor generates a root cause analysis model based on the identified entities and the classified components.