Federated Vehicle Maintenance Prediction via Peer Data Sharing
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Solution Overview
Problem
Traditional predictive maintenance systems are not scalable for individual automobiles, lacking the ability to provide cost-effective maintenance recommendations based on real-time component monitoring and condition analysis, unlike larger mechanical systems.
Innovation Solution
A system where a vehicle's information handling system receives and analyzes data transmissions from similar vehicles in proximity, using peer-to-peer communication and statistical analysis to generate predictive maintenance recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional predictive maintenance systems are implemented for individual automobiles, then maintenance accuracy and reliability are improved, but system complexity and cost increase making it non-scalable
Solution Approach 1:
The patent combines multiple vehicles into a federated learning network where each vehicle's information handling system contributes to a collective model. By merging data from multiple sources while maintaining individual system autonomy, the system achieves high prediction accuracy without requiring complex centralized infrastructure, thus resolving the contradiction between reliability and device complexity.
Solution Approach 2:
The federated learning model serves multiple vehicles simultaneously, making the system universally applicable across different vehicle types and owners. This multi-functionality allows the same predictive maintenance system to benefit numerous individual automobiles without proportionally increasing complexity for each user, enabling scalability.
2Measurement precision
If real-time data collection from multiple vehicles is implemented, then predictive maintenance accuracy is improved, but data transmission and processing requirements increase
Solution Approach 1:
The patent extracts only the necessary maintenance-related features and parameters from raw vehicle data through the federated learning model. Instead of transmitting and processing all raw data, the system extracts relevant condition indicators, significantly reducing data transmission and processing energy requirements while maintaining high measurement precision for predictive maintenance.
3Reliability
If preventive maintenance is performed based on fixed intervals, then component reliability is maintained, but unnecessary maintenance costs and downtime increase
Solution Approach 1:
The patent transitions from static fixed-interval maintenance schedules to dynamic predictive maintenance scheduling. The federated learning model continuously updates maintenance predictions based on real-time vehicle condition data, allowing maintenance to be performed only when actually needed. This dynamic approach maintains component reliability while eliminating unnecessary maintenance downtime and costs.
Data Source
AI summary
An approach is provided to automate predictive vehicle maintenance. In the approach, a vehicle's information handling system receives vehicle data transmissions from a number of other vehicles in geographic proximity to the vehicle. Both the vehicle and the other vehicles correspond to various vehicle types that are used to identify those other vehicles that are similar to the vehicle. The sets of received vehicle data transmissions that are received to similar vehicles are analyzed with respect to a plurality of vehicle maintenance data corresponding to the vehicle. The analysis of the vehicle data transmissions resulting in predictive vehicle maintenance recommendations pertaining to the first vehicle.