Image Forming Error Diagnosis Using Historical Sheet Counts
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Solution Overview
Problem
Existing failure diagnosis methods for image forming apparatuses require pre-determined correspondence relationships between sensor output changes and failure causes, limiting their effectiveness in identifying failures without prior knowledge.
Innovation Solution
An information processing apparatus that acquires cumulative sheet counts at error occurrences and related errors to determine the causal part of the failure, using a database to analyze past error information and output the cause of the error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a failure diagnosis method using pre-determined correspondence relationships between sensor output changes and failure causes is used, then the diagnosis can be performed systematically, but the cause of failure cannot be diagnosed unless the correspondence relationship is determined in advance
Solution Approach 1:
The system performs preliminary actions by collecting and storing sensor data and failure information in a database before actual failure diagnosis occurs. Historical sensor data, error logs, and maintenance records are accumulated in advance, enabling the system to analyze patterns and diagnose failures without requiring pre-determined correspondence relationships for every possible failure mode.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring sensor outputs, comparing current readings with historical data, and using the results to refine failure diagnosis. The feedback loop allows the system to learn from past failures and improve its diagnostic accuracy over time, adapting to new failure modes without requiring manual reconfiguration of correspondence relationships.
2Measurement precision
If detailed analysis of all sensor data is performed to identify failure causes, then diagnostic accuracy is improved, but the time required for repair increases
Solution Approach 1:
The system extracts only the most relevant and critical sensor data and failure information needed for diagnosis, rather than analyzing all available data in detail. By selectively extracting key parameters and using pattern matching against historical failure cases, the system achieves accurate failure identification while minimizing the time required for analysis and repair.
3Measurement precision
If comprehensive error information is stored in the database, then the accuracy of causal part determination is improved, but the data processing load increases
Solution Approach 1:
The system extracts and stores only the essential error information, sensor data, and maintenance records that are most relevant for failure diagnosis. By filtering and selecting only the critical data elements needed for causal part determination, the system maintains high diagnostic accuracy while reducing the overall data processing load and energy consumption associated with managing comprehensive error information.
Data Source
AI summary
An information processing apparatus for communicating to and from a database having stored therein information on errors which have occurred in an image forming apparatus, the information processing apparatus includes at least one processor configured to acquire, from the database, information on a certain error and information on a related error which has occurred in past before the certain error, acquire first information related to a cumulative number of printed sheets of the image forming apparatus at a time of occurrence of the certain error, and second information related to a cumulative number of printed sheets of the image forming apparatus at a time of occurrence of the related error, and determine a causal part of a cause of the certain error based on the first information and the second information, and an output unit configured to output the causal part of the cause of the certain error.