Adaptive Maintenance Recommendation System for Dynamic Failure Identification
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Solution Overview
Problem
Current maintenance systems fail to efficiently identify failure modes in devices during inspections, leading to prolonged investigation times and delayed device resets, as they do not adapt inspection strategies mid-process based on evolving inspection results.
Innovation Solution
A maintenance recommendation system that includes a primary storage unit for inspection results, a failure mode probability calculation unit, and an inspection item search unit to timely present relevant inspection items, reducing the time required to identify failure modes and expedite device resets by narrowing down failure and inspection item candidates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If inspection items are presented in a fixed sequence from the beginning, then the inspection process is simple to manage, but the time required to identify failure mode is prolonged and accuracy is reduced
Solution Approach 1:
The inspection item presentation is made dynamic by recalculating failure mode probabilities and re-ranking inspection items based on current inspection results. The system adapts the inspection sequence in real-time rather than following a fixed predetermined order, allowing the most likely failure modes to be investigated first while maintaining manageable complexity through automated recalculation.
Solution Approach 2:
The system implements feedback by continuously updating failure mode probabilities based on inspection results and using this feedback to重新 prioritize inspection items. Each inspection result feeds back into the probability calculation, which then feeds back into the inspection item ranking, creating a closed-loop system that improves efficiency.
2Measurement precision
If all inspection items are investigated completely, then comprehensive failure mode identification is achieved, but the investigation time is excessive
Solution Approach 1:
The system applies partial action by investigating only the most probable failure modes first rather than all possible inspection items. By ranking inspection items based on calculated probabilities and presenting them in order of likelihood, the system achieves sufficient accuracy for practical purposes without the excessive time investment required for complete investigation of all items.
Solution Approach 2:
The system changes the parameter of inspection priority dynamically by recalculating failure mode probabilities based on inspection results. This parameter change allows the system to adapt to new information and adjust the inspection sequence accordingly, achieving high accuracy by focusing on the most relevant inspection items at each stage rather than following a static comprehensive list.
3Measurement precision
If inspection strategy is adapted mid-inspection based on results, then accuracy of failure mode identification is improved, but the complexity of managing inspection process increases
Solution Approach 1:
The system applies self-service by automatically recalculating failure mode probabilities and re-ranking inspection items based on current results without requiring manual intervention. The automated recalculation and re-presentation of inspection items enables adaptive mid-inspection strategy while keeping management complexity low through computer automation rather than human decision-making.
4Loss of time
If inspection items are re-presented based on updated probabilities, then failure mode is identified at an early stage, but the calculation and management overhead increases
Solution Approach 1:
The system maintains continuity of useful action by continuously updating failure mode probabilities and re-presenting inspection items throughout the inspection process. This continuous adaptation ensures that the most relevant inspection items are always presented first, enabling early failure mode identification while managing overhead through automated continuous calculation rather than intermittent manual review.
Data Source
AI summary
The invention provides a maintenance recommendation system in which an inspection item is presented timely in the halfway of an inspection, accuracy of failure mode identification is improved, a failure mode is identified at an early stage, meanwhile, a time required for investigating a content of the failure is reduced, and a time from device failure to reset is shortened. The maintenance recommendation system includes: a primary storage unit that stores an input inspection result; a failure mode probability calculation unit that is configured to calculate a probability of a failure mode based on the inspection result stored in the primary storage unit; an inspection item search unit that is configured to extract an inspection item with the minimum inspection score from uninspected inspection items; and a main routine operation unit that is configured to narrow down a failure mode candidate and an inspection item candidate from all inspection items.