Vehicle Component Telemetry for Predictive Maintenance Timing
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Solution Overview
Problem
Current fleet management systems lack reliable methods to predict component failures in vehicles, leading to unnecessary maintenance costs and vehicle breakdowns due to reliance on unreliable manufacturer estimates and simplistic mileage-based schedules.
Innovation Solution
A vehicular telemetry system that processes historical operational data to derive predictive indicators of component status, using statistical analysis and real-time monitoring to identify when a component is likely to fail, allowing for timely maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manufacturer's recommended maintenance schedule based on running total of mileage or operational time is used, then maintenance can be performed regularly, but it leads to unnecessary maintenance costs and cannot accurately predict component failure
Solution Approach 1:
The patent transforms the maintenance approach by changing from fixed parameters (mileage intervals, time schedules) to dynamic parameters based on actual component condition indicators. It monitors operational parameters such as vibration, temperature, and performance metrics to detect degradation trends, allowing maintenance to be triggered by actual component state rather than predetermined schedules, thereby avoiding unnecessary maintenance while preventing failures
Solution Approach 2:
The system implements continuous feedback monitoring of component operational status by collecting real-time data from sensors and vehicle systems. This feedback loop compares actual component performance against baseline thresholds and degradation patterns, enabling dynamic adjustment of maintenance timing based on actual component health rather than static schedules, thus optimizing the balance between reliability and maintenance costs
2Measurement precision
If Mean Time Between Failure engineering data is used to predict failures, then a theoretical failure timeline can be established, but it cannot accurately predict actual component failure due to variations in operating conditions
Solution Approach 1:
The patent applies local quality by tailoring the failure prediction to each specific component's actual operating conditions rather than using a universal Mean Time Between Failure approach. It monitors local component-specific parameters such as vibration patterns, temperature profiles, and load characteristics to detect degradation unique to each component's operational context, enabling precise prediction that adapts to varying operating conditions
Solution Approach 2:
The system transitions from static failure prediction based on average MTBF data to dynamic prediction that continuously adapts to changing operating conditions. It monitors real-time operational parameters and adjusts failure probability assessments based on current component stress levels, environmental conditions, and usage patterns, making the prediction system flexible and responsive to actual operating variations
3Reliability
If simple comparison of current value with previous value is used, then the monitoring process is simple, but it cannot accurately predict component failure
Solution Approach 1:
The system performs preliminary action by establishing baseline component performance characteristics and degradation patterns during normal operation. It pre-defines threshold values and degradation trends that indicate impending failure, allowing the system to predict failure based on whether current measurements exceed these pre-established criteria, rather than requiring complex real-time analysis of every data point
Solution Approach 2:
The patent introduces an intermediary layer of analysis that processes raw operational data through defined algorithms and thresholds to generate interpretable component health indicators. This intermediary transformation converts complex sensor data into simplified failure probability assessments using predetermined criteria, maintaining prediction reliability while keeping the analysis process manageable and interpretable
Data Source
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.