Off-Road Tire Maintenance Prediction Using Sensor History
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Solution Overview
Problem
It is challenging to determine when and how to maintain tires properly, especially in varying environmental conditions, leading to premature damage or failure due to lack of data and history associated with each tire.
Innovation Solution
A system and method using a machine learning engine that assigns unique identifiers to tires, collects sensor data, and compares it with historical data to predict maintenance needs, including determining the type of maintenance and timing, thereby improving inventory management, longevity, and operational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional tire maintenance methods are used without data tracking, then the system is simple and easy to operate, but tire condition monitoring is insufficient leading to premature damage or failure
Solution Approach 1:
The system performs preliminary actions by assigning unique identifiers to tires and continuously collecting sensor data (pressure, temperature, location) before actual tire failure occurs. This proactive data collection enables predictive maintenance by analyzing trends and comparing against historical data from similar tires, allowing maintenance to be scheduled before premature damage or failure happens.
Solution Approach 2:
The patent introduces a data processing system as an intermediary between the physical tires and the maintenance decision-making process. This intermediary collects sensor data, retrieves historical inspection data, compares conditions across multiple tires, and generates maintenance recommendations. The intermediary handles the complexity of data analysis, leaving the actual maintenance decisions simpler and more data-driven.
2Loss of time
If tire maintenance is performed without historical data comparison, then the process is quick and simple, but maintenance timing is inaccurate leading to either premature service or missed maintenance
Solution Approach 1:
The system implements feedback by continuously monitoring tire sensor data and comparing it against historical inspection data and data from other tires with similar usage patterns. This feedback loop enables the system to learn from past maintenance outcomes and adjust maintenance timing predictions, reducing both premature maintenance and missed maintenance events.
Solution Approach 2:
By analyzing historical data and current sensor readings, the system performs preliminary assessment of tire condition trends before actual failure or excessive wear occurs. This allows maintenance to be scheduled at the optimal time based on predicted remaining useful life, minimizing downtime while avoiding premature service.
3Loss of information
If unique identifiers and sensor data collection are implemented for each tire, then tire tracking and condition monitoring improve, but data management complexity and storage requirements increase
Solution Approach 1:
The system creates data copies by storing tire information, sensor readings, and inspection history in a centralized data structure that can be accessed and analyzed without physically handling the tires. Historical inspection data is copied and stored for comparison with current sensor data, enabling comprehensive analysis without increasing physical complexity at the tire level.
Solution Approach 2:
The data processing system performs multiple functions: collecting sensor data, retrieving historical inspection data, comparing conditions across tires, predicting maintenance needs, and generating recommendations. This multi-functional approach consolidates data management complexity into a single system that handles all aspects of tire monitoring and analysis.
4Productivity
If predictive maintenance is implemented using machine learning, then maintenance optimization improves, but computational requirements and model training complexity increase
Solution Approach 1:
The system applies partial machine learning by using trained models to predict maintenance needs only for tires with sufficient historical data and similar usage patterns. Rather than applying complex ML to every single tire immediately, the system gradually builds models as more data becomes available, balancing computational requirements with productivity gains.
Data Source
AI summary
Systems and methods of for tire maintenance using machine learning are provided. The system receives one or more values comprising sensor data and a unique identifier associated with the tire. The system can retrieve historical inspection data associated with the unique identifier of the tire. The system can generate a matrix comprising a first dimension based on timestamps and a second dimension based on the one or more values and the historical inspection data. The system can predict, via input of the matrix into a machine learning model constructed, an output matrix comprising an indication to perform a type of maintenance and at least one tire maintenance category. The system can provide the indication to perform the type of maintenance for the tire during the time interval and the at least one tire maintenance category.