CNC Machine Tool Fault Diagnosis Using User Feedback Loops
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Solution Overview
Problem
Current fault diagnosis methods for CNC machine tools are inefficient due to the complexity and uncertainty of signals, making it difficult to accurately diagnose faults in these machines.
Innovation Solution
A fault diagnosis method and apparatus that utilizes user feedback to adjust the diagnosis policy and update the fault diagnosis database, allowing for improved accuracy and efficiency by sending unclear faults to experts and updating the database with confirmed fault information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fault diagnosis methods (fault tree analysis, fault propagation model analysis, case-based reasoning analysis) are used for CNC machine tools, then the diagnosis can be performed with existing methods, but the diagnosis accuracy is low due to the complexity and uncertainty of signals and fault symptoms
Solution Approach 1:
The patent implements a feedback mechanism where user feedback on diagnosis results is collected and used to iteratively optimize the diagnosis policy. The system receives feedback whether the diagnosed fault was correct, and uses this feedback to adjust and improve future diagnosis decisions, thereby progressively improving diagnosis accuracy while managing system complexity through learned optimization rather than complex static models
Solution Approach 2:
The patent changes the parameters of the diagnosis system by using a reinforcement learning agent that dynamically adjusts diagnosis policies based on learned patterns from feedback. Instead of using fixed complex models like fault tree analysis, the system learns optimal diagnosis parameters and strategies through continuous interaction and feedback, improving accuracy while keeping the underlying system architecture relatively simple
2Measurement precision
If expert diagnosis is used for unclear faults, then the diagnosis accuracy can be improved, but the dependency on experts increases and the diagnosis time increases
Solution Approach 1:
The patent implements a self-service mechanism where the system automatically handles routine and clear faults through its own diagnosis capabilities without requiring expert intervention. The reinforcement learning agent independently optimizes diagnosis policies for common fault patterns, allowing the system to serve itself for straightforward cases and reducing both time loss and expert dependency
Solution Approach 2:
The patent introduces an intermediary reinforcement learning agent that acts as a mediator between automatic diagnosis and expert diagnosis. This agent first attempts to diagnose faults automatically, and only escalates to expert diagnosis when uncertain or when the confidence threshold is not met. This intermediary layer reduces unnecessary expert involvement while maintaining high accuracy for complex cases
3Productivity
If the fault diagnosis database is continuously updated with user feedback, then the diagnosis policy can be optimized for recurring faults, but the system complexity increases
Solution Approach 1:
The system automatically updates its diagnosis policy using reinforcement learning based on user feedback, without requiring manual intervention to maintain or optimize the database. The reinforcement learning agent self-service optimizes the diagnosis policy by learning from feedback patterns, improving diagnosis efficiency for recurring faults while keeping the maintenance process simple and automated
Solution Approach 2:
The patent implements a feedback loop where user feedback on diagnosis results is continuously collected and used to optimize the diagnosis policy through reinforcement learning. This feedback mechanism allows the system to automatically learn and adapt to recurring fault patterns, improving diagnosis efficiency without requiring complex manual database management or expert intervention for each update
Data Source
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AI summary
The present invention relates to the technical field of industrial automation, and in particular relates to a fault diagnosis method and apparatus for a numerical control machine tool. The present invention can improve the efficiency of diagnosing faults of numerical control machine tools by utilizing feedback information provided by users. In a fault diagnosis method provided in the embodiments of the present invention, the fault diagnosis apparatus receives a fault symptom to be diagnosed from a user of a user terminal, diagnoses the fault for the fault symptom to be diagnosed, returns a fault diagnosis result to the user terminal, receives feedback on the fault diagnosis result from the user of the user terminal, and adjusts the diagnosis policy for the fault symptom to be diagnosed according to the fault diagnosis result if the feedback of the user on the fault diagnosis result indicates that the fault has been cleared.