Vehicle Telemetry Maintenance Prediction Under Variable Operating Conditions
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Solution Overview
Problem
Existing methods for predicting vehicle component failure are unreliable due to variations in operating conditions and interactions with other components, leading to unnecessary maintenance costs and inefficiencies in fleet management.
Innovation Solution
A vehicular telemetry system that collects and analyzes real-time operational data from vehicles to generate predictions specific to each vehicle, using statistical analysis and contextual information to monitor component deterioration and failure, accounting for environmental and operational conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Mean Time Between Failure engineering data is used to predict component failure, then a general prediction framework is established, but the predictions are unreliable due to variations in operating conditions and interactions with other components
Solution Approach 1:
The patent applies local quality by transitioning from general fleet-wide Mean Time Between Failure data to vehicle-specific predictive indicators. The system analyzes individual vehicle operational data, telemetry information, and maintenance records to generate customized predictions for each vehicle, accounting for unique operating conditions, component interactions, and usage patterns that general data cannot capture.
Solution Approach 2:
The patent employs parameter changes by incorporating multiple dynamic variables into the prediction model, including vehicle-specific operational parameters, environmental conditions, component age, usage intensity, and interaction effects between components. This multi-parameter approach allows the system to adapt predictions to varying operating conditions rather than relying on static historical averages.
2Ease of manufacture
If manufacturer's recommended vehicle maintenance schedule is applied, then a standardized maintenance approach is implemented, but simple comparisons of numbers are limited and inconclusive for accurately predicting component failure
Solution Approach 1:
The patent implements feedback by continuously monitoring vehicle operational data, component performance metrics, and actual failure occurrences. The system uses this feedback to refine and update predictive models for each vehicle, adjusting maintenance recommendations based on real-world performance rather than relying solely on manufacturer schedules. This creates a closed-loop system where predictions improve over time based on actual vehicle behavior.
Solution Approach 2:
The patent applies preliminary action by identifying and flagging components at risk of failure before actual failure occurs. The system analyzes trends in operational data and predictive indicators to anticipate potential failures, allowing maintenance to be performed proactively based on actual component condition rather than waiting for scheduled intervals or actual breakdowns.
3Productivity
If fleet-wide maintenance approaches are used, then operational efficiency is maintained across the fleet, but individual vehicle conditions and component interactions are not accounted for
Solution Approach 1:
The patent applies segmentation by dividing the fleet into individual vehicle units, each with its own predictive maintenance profile. The system processes and analyzes data at the vehicle level, generating specific predictions for each vehicle's components based on its unique operational characteristics. This segmented approach allows maintenance decisions to be optimized for each vehicle while still providing fleet-wide oversight and coordination.
Data Source
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AI summary
Apparatus, device, methods and system relating to a vehicular telemetry environment for monitoring vehicle components and providing indications towards the condition of the vehicle components and providing optimal indications towards replacement or maintenance of vehicle components before vehicle component failure.