Dynamic Vehicle Maintenance Scheduling via Sensor Data
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Solution Overview
Problem
There is a lack of reliable methods to accurately determine the useful life and maintenance needs of a vehicle, affecting its resale value and maintenance timing.
Innovation Solution
A computer-implemented method that combines static vehicular data, dynamic vehicular data, and environmental data to compute a maintenance score, adjust maintenance intervals, and provide recommendations using a machine learning system for vehicle maintenance, unscheduled maintenance data, and vehicle life estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fixed maintenance schedules are used, then maintenance timing is simple to determine, but vehicle reliability and maintenance accuracy deteriorate
Solution Approach 1:
The patent applies dynamics by transitioning from fixed, static maintenance schedules to dynamic, condition-based scheduling. The system continuously monitors vehicle parameters (engine temperature, mileage, driving conditions) and adjusts maintenance timing dynamically based on actual vehicle state, thereby improving reliability without requiring overly complex manual intervention.
Solution Approach 2:
The patent implements feedback mechanisms by monitoring vehicle operating parameters and using this information to adjust maintenance schedules. The system receives feedback from sensors and vehicle data, processes this information to assess vehicle condition, and modifies maintenance timing accordingly, creating a closed-loop system that improves reliability through continuous adaptation.
2Loss of energy
If maintenance intervals are extended to reduce operating costs, then cost efficiency improves, but vehicle reliability deteriorates
Solution Approach 1:
The patent applies parameter changes by adjusting maintenance interval parameters based on actual vehicle conditions. Instead of using fixed time-based intervals, the system changes maintenance timing parameters according to monitored parameters such as engine temperature, mileage, and driving patterns, optimizing the balance between cost efficiency and reliability for each individual vehicle state.
Solution Approach 2:
The patent implements preliminary action by performing maintenance before actual failures occur. The system predicts potential vehicle issues based on monitored parameters and schedules maintenance proactively, preventing costly failures while optimizing maintenance timing to reduce unnecessary service costs, thereby balancing reliability and operating cost.
3Measurement precision
If detailed vehicle monitoring is implemented to improve maintenance accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent applies universality by using a multi-functional vehicle data collection system that serves multiple purposes. The same sensors and data collection mechanisms monitor various vehicle parameters (engine temperature, mileage, driving conditions) for both maintenance scheduling and broader vehicle health assessment, reducing the need for separate specialized systems while maintaining high measurement precision.
Solution Approach 2:
The patent merges multiple data collection functions into a unified system. Instead of separate systems for monitoring different vehicle parameters, the patent combines engine sensors, mileage tracking, and driving condition monitors into an integrated data collection framework, processing all inputs through a single analysis mechanism to improve accuracy without proportionally increasing complexity.
Data Source
AI summary
Disclosed embodiments provide techniques for providing vehicular recommendations based on driver habits. Embodiments utilize a variety of input data, including, but not limited to, static vehicular data, dynamic vehicular data, and/or environmental data. In embodiments, empirical rules are used to adjust recommended maintenance schedules based on the input conditions. Additionally, the adjusted recommendations along with unscheduled maintenance data are input to a machine learning system, such as a neural network. The machine learning system is used to further revise the maintenance schedule, estimate end of life of the vehicle, and issue recommendations for when to sell a vehicle and recommendations on attributes of a new vehicle for acquisition.