Malfunction Prediction for Printers Using Category-Based History Copying
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Solution Overview
Problem
Current technologies for anticipating abnormal states in printers and similar equipment rely on collecting and analyzing history logs but fail to effectively predict and notify about potential malfunctions, especially for newly installed or condition-changing devices.
Innovation Solution
An information processing apparatus with an acquisition unit, classification unit, and notification unit that collects and classifies history and attribute information to calculate the likelihood of malfunctions for categorized devices, sending notifications when the calculated risk exceeds a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If history logs are collected and analyzed for all apparatuses, then the ability to detect abnormal states is improved, but the system cannot effectively predict malfunctions for newly installed or condition-changing devices that lack sufficient history data
Solution Approach 1:
The patent creates virtual copies of apparatus profiles by transferring history information from similar apparatuses within the same category. When a new or condition-changing device lacks sufficient operational history, the system generates a virtual history profile by copying relevant data from other apparatuses in the same category, enabling effective malfunction prediction without requiring extensive actual operational data from the target device itself
Solution Approach 2:
The system performs preliminary classification of apparatuses into categories based on attributes before malfunction detection. By pre-organizing apparatuses into categories with similar characteristics, the system prepares the foundation for copying history information among similar devices, enabling rapid and accurate malfunction prediction when needed without waiting for sufficient history data to accumulate
2Measurement precision
If malfunction prediction is performed for all apparatuses individually, then prediction accuracy is improved, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the apparatus population into distinct categories based on shared attributes. By dividing the overall apparatus set into smaller category groups, the system performs malfunction prediction and history copying within each category rather than treating all apparatuses uniformly. This segmentation maintains prediction precision for each category while significantly reducing computational complexity by limiting the scope of data processing to category-specific apparatuses
Data Source
AI summary
An information processing apparatus includes an acquisition unit, a classification unit, a calculation unit, and a notification unit. The acquisition unit acquires history information and attribute information on plural apparatuses. The classification unit classifies the apparatuses into plural categories in accordance with the attribute information on the apparatuses. The calculation unit calculates degrees of occurrence of malfunctions for apparatuses of the categories in accordance with the acquired history information, the degrees of occurrence of malfunctions being calculated for the respective categories, into which classification has been performed. The notification unit notifies an apparatus of a possibility of occurrence of malfunctions, the apparatus belonging to a category for which the calculated degree of occurrence of malfunctions for an apparatus exceeds a threshold.