Fault Diagnoser Retraining Interval for Rendering Devices
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Solution Overview
Problem
Existing fault diagnostic systems for rendering devices like printers are static and unable to adapt to changes in rendering characteristics, leading to customer dissatisfaction due to the lack of mechanisms for updating diagnosers with new defects and inefficient re-training processes.
Innovation Solution
A method and system for automatically determining an optimal re-training interval for a fault diagnoser based on online monitoring of a classifier's performance, using a support vector machine to identify outliers and determine when to re-train, thereby enabling the system to handle new defects over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the diagnostic system continuously monitors and re-trains the classifier, then the adaptability to new defects is improved, but the time and computational resources consumed increase
Solution Approach 1:
The system implements a feedback mechanism where the classifier's performance is continuously monitored and compared against predefined thresholds. When performance degradation or new defect patterns are detected through statistical measures like Kullback-Leibler divergence, the system triggers re-training. This feedback loop ensures the system adapts to new defects only when necessary, balancing adaptability with resource efficiency.
Solution Approach 2:
The diagnostic system transitions from a static classifier to a dynamic one that can adapt its training schedule based on actual performance needs. The re-training interval is not fixed but determined dynamically through online monitoring of classification performance and statistical analysis of defect patterns, allowing the system to optimize between adaptability and computational resource consumption.
2Measurement precision
If the diagnostic system re-trains the classifier frequently, then the diagnosis accuracy is improved, but the computational resources and energy consumed increase
Solution Approach 1:
Performance monitoring with feedback thresholds determines when re-training is necessary. The system calculates statistical measures such as Kullback-Leibler divergence between current and historical defect distributions, and only initiates re-training when these measures exceed predefined thresholds, ensuring re-training occurs only when it will meaningfully improve diagnosis accuracy.
Solution Approach 2:
The system changes the operational parameters of the classifier dynamically by adjusting the re-training schedule based on actual performance metrics. Instead of using fixed re-training intervals, the system modifies the training frequency parameter based on detected changes in defect patterns and classification performance, optimizing the balance between accuracy and computational energy consumption.
3Loss of energy
If the diagnostic system uses a static classifier, then the computational resources required are reduced, but the ability to adapt to rendering characteristic changes is worsened
Solution Approach 1:
The classifier performs self-diagnosis through online monitoring of its own performance metrics. The system automatically detects when re-training is needed by monitoring classification confidence levels and statistical measures of defect distribution changes, eliminating the need for external intervention or continuous manual updates while maintaining adaptability to rendering characteristic changes.
Solution Approach 2:
Instead of continuous re-training, the system implements periodic re-training actions triggered by performance thresholds. The classifier operates in a low-energy state during normal operation and only activates re-training periodically when performance degradation or new defect patterns are detected, reducing overall computational energy consumption while maintaining necessary adaptability.
4Reliability
If unnecessary re-training is performed, then the classifier remains up-to-date, but the time and effort wasted increase
Solution Approach 1:
The system uses feedback from performance monitoring to determine when re-training is necessary. By continuously evaluating classification performance and comparing defect distributions using statistical measures, the system only initiates re-training when actual performance degradation is detected, preventing unnecessary re-training while ensuring the classifier remains up-to-date when needed.
Solution Approach 2:
The system performs preliminary analysis of performance metrics and defect patterns before committing to re-training. By pre-calculating statistical measures like Kullback-Leibler divergence and comparing them against thresholds, the system determines in advance whether re-training will be beneficial, avoiding unnecessary training operations while maintaining classifier reliability.
Data Source
AI summary
A method and system for automatically determining an optimal re-training interval for a fault diagnoser based on online monitoring of the performance of a classifier are disclosed. The classifier generates a soft measure of membership in association with a class based on a training data. The output of the classifier can be utilized to assign a label to new data and then the members associated with each class can be clustered into one or more core members and potential outliers. A statistical measure can be utilized to determine if the distribution of the outliers is sufficiently different than the core members after enough outliers have been accumulated. If the outliers are different with respect to the core members, then the diagnoser can be re-trained; otherwise, the output of the classifier can be fed to the fault diagnoser.