Vehicle Analytics Model for Hidden Failures and Remaining Useful Life
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Solution Overview
Problem
Prospective buyers of pre-owned vehicles lack a mechanism to determine incipient failures or damage that are not visible during a test drive, and they have no objective metric to assess the remaining useful life and condition of the vehicle, which affects the vehicle's monetary value.
Innovation Solution
A system and method for vehicle analytics that receives data from condition and usage indicator sensors, updates a vehicle-specific model based on a master model, identifies usage trends, and determines the remaining useful life of vehicle components, providing a report that includes hidden value-reducing failures and estimated monetary value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vehicle inspection methods are used during test drives, then buyers can assess visible condition, but they cannot detect incipient failures or hidden damage
Solution Approach 1:
The system performs preliminary monitoring and assessment of vehicle components before the buyer needs to evaluate them. Sensors continuously collect data on component health, usage patterns, and potential failures, updating the digital twin in advance so that hidden issues are detected before they become visible problems during test drives.
Solution Approach 2:
The patent introduces a digital twin as an intermediary between the physical vehicle and the buyer's assessment. This virtual replica serves as a mediator that translates complex sensor data into actionable insights about component health, usage trends, and remaining useful life, making hidden information accessible without requiring specialized inspection equipment.
2Loss of information
If no objective metrics are provided for vehicle condition, then buyers lack information for valuation, but implementing comprehensive monitoring increases system complexity
Solution Approach 1:
The system creates a digital copy (digital twin) of the physical vehicle that consolidates all monitoring data, usage information, and component status. This virtual replica provides comprehensive vehicle valuation data without requiring the buyer to directly interact with complex sensor systems, as all information is aggregated and presented in the digital model.
Solution Approach 2:
The monitoring system is designed to serve multiple functions simultaneously: it tracks component health, monitors usage patterns, predicts failures, calculates remaining useful life, and supports vehicle valuation. By making the system multi-functional, the patent reduces the need for separate systems for each purpose, thereby managing complexity while providing comprehensive information.
3Measurement precision
If comprehensive sensor data is collected from the vehicle, then accurate remaining useful life estimation is possible, but data processing and model updating become more complex
Solution Approach 1:
The digital twin serves as a virtual processing environment where comprehensive sensor data is consolidated, analyzed, and transformed into meaningful metrics. Instead of processing raw data directly, the system updates the digital model which then provides processed information about component health and remaining useful life, simplifying the data processing architecture.
Solution Approach 2:
The system performs preliminary data processing and analysis by continuously updating the digital twin with sensor data and usage information. This advance processing ensures that when valuation or assessment is needed, the data is already organized and analyzed, reducing the complexity of real-time processing requirements.
Data Source
AI summary
A method for vehicle analytics includes receiving data from at least one condition indicator sensor of a vehicle and receiving data from at least one usage indicator sensor of the vehicle. The method also includes updating a vehicle specific model corresponding to a vehicle master model, based on the data from the at least one condition indicator sensor and the at least one usage indicator sensor. The method also includes identifying, using the vehicle specific model, at least one usage trend of the vehicle and determining an estimate of a remaining useful life of at least one aspect of the vehicle based on the at least one usage trend of the vehicle.