Vehicle Component Deterioration Prediction Using Reference Data Comparison
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Solution Overview
Problem
Existing methods for predicting the deterioration tendency of vehicle components lack accuracy, as they do not account for the unique usage conditions of individual vehicles, leading to insufficient maintenance timing and potential over- or under-maintenance.
Innovation Solution
A data processing system that compares component deterioration data from a group of reference vehicles with a target vehicle to predict deterioration tendencies, incorporating an estimator to identify specific deterioration factors and their contribution rates, and provides personalized maintenance recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If component deterioration prediction is performed using general methods without considering individual vehicle usage conditions, then the prediction process is simple, but the prediction accuracy is insufficient
Solution Approach 1:
The patent applies local quality by tailoring the prediction model to individual vehicle characteristics and usage conditions. Instead of using a generic prediction model, the system collects and analyzes vehicle-specific data including usage patterns, environmental conditions, and component history to create a customized prediction for each vehicle, thereby improving accuracy while managing complexity through targeted data collection.
Solution Approach 2:
The patent implements preliminary action by pre-collecting and storing various types of data related to vehicle usage, component performance, and environmental factors before the prediction is needed. This includes gathering operational data, maintenance history, and sensor information in advance, which then feeds into the prediction model to provide accurate forecasts without requiring complex real-time processing.
2Reliability
If maintenance is performed based on generic deterioration models, then maintenance scheduling is straightforward, but maintenance timing is insufficient or excessive
Solution Approach 1:
The patent employs feedback mechanisms by continuously monitoring actual component performance and comparing it against predicted deterioration trends. The system uses real-time sensor data and maintenance outcomes to refine future predictions, creating a closed-loop system that adapts to actual vehicle conditions and improves maintenance timing accuracy over time.
Solution Approach 2:
The patent applies dynamics by making maintenance recommendations adaptive rather than static. The maintenance schedule is dynamically adjusted based on changing vehicle conditions, usage patterns, and predicted deterioration rates, allowing the system to optimize maintenance timing for each individual vehicle rather than following a fixed schedule.
3Measurement precision
If individual vehicle usage conditions are considered in deterioration prediction, then prediction accuracy improves, but data collection and processing requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the complex data collection and processing task into distinct manageable modules. The system separates data collection from analysis, organizes data by type (operational, environmental, maintenance history), and processes different data streams independently before integrating them into the final prediction, thereby improving overall processing efficiency.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting the scope and depth of data collection based on the specific vehicle type, component being analyzed, and available data quality. The system adapts its data processing requirements by prioritizing the most impactful parameters for each prediction scenario, reducing unnecessary data processing while maintaining high accuracy.
Data Source
AI summary
A data processing system includes reference vehicles, a target vehicle, and a data processing apparatus. The data processing apparatus is configured to predict a deterioration tendency over time of a component included in the target vehicle. The data processing apparatus includes a first acquirer, a second acquirer, a predictor, and an estimator. The predictor is configured to the predict the deterioration tendency of the component included in the target vehicle by comparing reference deterioration data acquired by the first acquirer and target deterioration data acquired by the second acquirer with each other. The estimator is configured to estimate, with respect to the component included in the target vehicle, one or more deterioration factors and a contribution rate for each of the one or more deterioration factors.