Tire Damage Progress Tracking for Predictive Maintenance Timing
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Solution Overview
Problem
Existing methods struggle to accurately estimate the progress speed of damage on the outer surface of tires, particularly on construction vehicles traveling on rough terrain, which is crucial for timely maintenance and preventing operational interruptions.
Innovation Solution
A tire state management system and program that acquires and compares image data at different times to detect damage, such as cracks, and uses vehicle operation information to estimate the progress speed of damage and predict maintenance timing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data is acquired at different timings to detect damage, then damage detection capability is improved, but the ability to estimate damage progress speed deteriorates due to inability to track temporal changes of the object itself
Solution Approach 1:
The patent segments the image processing into two distinct components: background alignment (relative changes) and object feature tracking (temporal changes of the object itself). By calculating feature points for both the background and the tire object separately, and aligning them with different weights, the system can simultaneously detect damage while tracking its temporal progression.
Solution Approach 2:
The patent changes the weighting parameters of feature points based on their source (background vs. object). By adjusting the weights dynamically, the system emphasizes object features when tracking damage progression and background features when aligning images, thus resolving the contradiction between detection accuracy and temporal tracking capability.
2Reliability
If conventional damage detection methods are used, then damage can be detected, but accurate estimation of damage progress speed and maintenance timing prediction cannot be achieved
Solution Approach 1:
The patent implements a feedback mechanism where damage detection results from previous time points are fed into the progress speed estimation process. By continuously tracking the same damage features across multiple images and calculating their temporal evolution, the system provides feedback on damage progression rates, enabling accurate maintenance timing predictions.
Solution Approach 2:
The patent performs preliminary alignment of images using background features before conducting damage detection. This preliminary action removes the influence of relative background changes, creating a stable reference frame that enables more accurate measurement of damage progression in subsequent processing steps.
3Stability of the object's composition
If only relative changes to background are detected, then alignment between images is improved, but temporal changes of the tire object itself cannot be accurately tracked
Solution Approach 1:
The patent applies different processing qualities to different parts of the image: background regions are processed with high emphasis on stability and alignment, while tire object regions are processed with high emphasis on tracking temporal changes. This local differentiation of processing quality allows simultaneous achievement of image alignment and damage progression tracking.
Solution Approach 2:
The patent dynamically adjusts the weight given to background features versus object features based on the processing stage. During alignment phases, background features are weighted higher for stability; during damage tracking phases, object features are weighted higher to capture temporal changes. This dynamic weighting resolves the contradiction between alignment stability and temporal tracking.
Data Source
AI summary
A tire state management system (10) is provided with an image data acquisition unit (210) for acquiring 1st image data acquired by imaging the outer surface at a first timing and second image data acquired by imaging the outer surface at a second timing later than the first timing, a damage portion acquisition unit (220) for acquiring a damage included in the 1st image data and a damage included in the 2nd image data, a vehicle operation information acquisition unit (230) for acquiring operation information of a vehicle on which a tire is mounted, and a maintenance prediction unit (240) for estimating a progress speed based on the damage included in the 1st image data and the progress speed included in the 2nd image data and predicting a maintenance timing for the tire based on the progress speed and the operation information.