Image Forming Apparatus Maintenance Prediction via Dynamic Data Prioritization
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Solution Overview
Problem
Existing image forming apparatus management systems face challenges in accurately predicting maintenance times without significantly increasing costs, often requiring high-performance controllers and large memory, which can deter productivity and increase costs.
Innovation Solution
A management system that prioritizes data collection and transmission based on defined orders, allows for data interpolation when deficiencies occur, and adjusts priorities dynamically to ensure accurate maintenance predictions without compromising productivity or increasing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-performance controllers and large-capacity memory are employed to execute all processes simultaneously, then measurement precision and reliability of maintenance prediction are improved, but device complexity and cost increase remarkably
Solution Approach 1:
The patent segments the data collection process by assigning different priorities to different status data items. The controller collects status data in priority order, ensuring that critical maintenance-related data is captured with high precision while less critical data is collected with lower resource consumption. This segmentation allows accurate maintenance prediction without requiring high-performance hardware for all data collection operations.
Solution Approach 2:
The patent applies local quality by allocating different collection frequencies and priorities to different status data items based on their relevance to maintenance prediction. Critical parameters are collected with higher frequency and priority, while non-critical parameters use lower resource allocation. This localized optimization achieves accurate maintenance prediction without uniformly high device specifications.
2Measurement precision
If data collection and transmission are performed in real time with high priority, then measurement precision is improved, but productivity of the print process deteriorates
Solution Approach 1:
The patent implements dynamic priority adjustment where the collection and transmission priority of status data items changes based on operational context and maintenance criticality. During active print processes, lower-priority status data collection is temporarily reduced to minimize interference with productivity, while critical maintenance data continues to be collected. This dynamic approach maintains measurement precision for essential data without significantly impacting print process productivity.
Solution Approach 2:
The patent employs periodic data collection at different intervals based on priority levels. High-priority maintenance-related status data is collected more frequently, while other status data is collected at lower frequencies. This periodic action ensures adequate measurement precision for maintenance prediction while reducing the overall burden on the print process, thereby maintaining productivity.
3Reliability
If data collection priority is increased to ensure accurate maintenance prediction, then reliability is improved, but loss of time in data collection and transmission increases
Solution Approach 1:
The patent performs preliminary sorting and prioritization of status data items before collection, identifying which data items are most critical for maintenance prediction. This preliminary action allows the system to focus data collection efforts on high-priority items, ensuring reliable maintenance prediction without spending excessive time collecting and transmitting all possible status data. The pre-established priority framework enables efficient time allocation.
Solution Approach 2:
The patent changes the collection parameters (frequency, priority level, detail level) of status data items based on their importance to maintenance prediction. By adjusting these parameters dynamically, the system achieves high reliability for critical maintenance data while minimizing the time spent on less critical data collection. This parameter optimization balances reliability and time efficiency.
Data Source
AI summary
A management system capable of managing an apparatus with high accuracy at low cost without deteriorating productivity. The management system includes an image forming apparatus and a management apparatus that are communicatively connected. The image forming apparatus includes an obtainment unit that obtains data for items to which priorities are given, a first storage unit that stores the data, a transmission unit that transmits the data to the management apparatus, and an update unit that updates the priorities of the items according to a notification from the management apparatus. The management apparatus includes a second storage unit that stores data from the image forming apparatus, a determination unit that determines whether interpolation of defective data is possible for the items, a change unit that changes the priorities of the items according to possibility of the interpolation, and a notification unit that notifies of the priorities of the changed items.