ML Troubleshooting Prediction for Asset Repair
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Solution Overview
Problem
As the number and type of assets in an enterprise system grow, technicians face increasing difficulties in efficiently resolving errors due to the complexity and variety of troubleshooting actions required.
Innovation Solution
The implementation of a machine learning-based system that utilizes a sequence-to-sequence model with an attention mechanism to predict the success of troubleshooting actions, allowing for iterative refinement of recommended actions based on designated criteria and feedback, thereby optimizing the troubleshooting process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the number and type of assets in an enterprise system grow, then the system capacity increases, but the difficulty of troubleshooting increases
Solution Approach 1:
The system enables self-service troubleshooting by automatically analyzing asset information and error codes through machine learning models to generate and execute diagnostic actions without requiring human technician intervention, thereby maintaining high troubleshooting capability as asset numbers increase
Solution Approach 2:
The patent replaces manual mechanical troubleshooting processes with an automated machine learning-based system that uses neural networks to analyze errors, generate diagnostic actions, and execute repairs, substituting human expertise with computational intelligence
2Reliability
If manual troubleshooting is performed on each asset, then troubleshooting accuracy is maintained, but time consumption increases
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning model continuously learns from the outcomes of diagnostic actions and repair results, improving its accuracy over time while maintaining rapid automated operation that reduces time consumption without sacrificing troubleshooting reliability
Solution Approach 2:
The patent changes the operational parameters from manual step-by-step troubleshooting to automated machine learning-based diagnostic actions, transforming the time consumption characteristic while preserving accuracy through intelligent algorithmic decision-making
3Reliability
If multiple troubleshooting actions are attempted, then repair success rate improves, but cost increases
Solution Approach 1:
The machine learning model performs preliminary analysis of asset information and error codes to predict the most likely cause of the problem before executing diagnostic actions, enabling targeted troubleshooting that improves repair success rates while avoiding unnecessary costly interventions by pre-identifying the most probable repair paths
Data Source
AI summary
An apparatus includes at least one processing device configured to obtain information regarding a given asset to be repaired, to generate a recommended troubleshooting action to be performed on the given asset, and to provide the recommended troubleshooting action and the obtained information regarding the given asset as input to an encoder of a machine learning model implementing an attention mechanism. The at least one processing device is also configured to receive, from a decoder of the machine learning model, a predicted success of the recommended troubleshooting action. The at least one processing device is further configured to determine whether the predicted success of the recommended troubleshooting action meets designated criteria, to perform the recommended troubleshooting action responsive to the predicted success meeting the designated criteria, and, to modify the recommended troubleshooting action responsive to the predicted success not meeting the designated criteria.