Vehicle Part RUL Forecasting From Actual Usage Data
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Solution Overview
Problem
Existing prognostic tools for determining the Remaining Useful Life (RUL) of automotive parts are unreliable due to their reliance on predetermined schedules and imprecise performance metrics, and they often fail to account for the actual usage of vehicle parts, especially those not communicatively connected with an ECU.
Innovation Solution
A system and method that utilize sensors and a compiler connected through a CAN bus or dealer network to determine the RUL of vehicle parts by analyzing actual usage data from various operating modes, allowing for accurate forecasting of reliability and useful operation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If predetermined replacement schedules are used, then maintenance can be performed systematically, but unnecessary replacements occur due to not accounting for actual usage
Solution Approach 1:
The system changes the parameter basis for maintenance scheduling from fixed time intervals to actual usage metrics collected by sensors. By monitoring parameters such as operating hours, temperature, vibration, and load conditions specific to each vehicle part, the system dynamically adjusts maintenance timing based on real-world usage patterns rather than predetermined schedules, thereby eliminating premature replacements while maintaining systematic maintenance.
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor vehicle part conditions in real-time and transmit data to the compiler. The compiler processes this feedback data, compares it against reliability models, and adjusts maintenance predictions accordingly. This closed-loop feedback mechanism enables the system to adapt to actual usage patterns and provide accurate RUL predictions, resolving the contradiction between systematic maintenance and accuracy.
2Adaptability or versatility
If performance based prognostic models are used, then RUL estimates can be obtained without ECU connectivity, but the estimates are imprecise as they are a proxy for actual use
Solution Approach 1:
The system replaces the imprecise software-based performance metrics with direct physical sensing using sensors that actually measure mechanical and environmental parameters of vehicle parts. By substituting the proxy performance data with direct physical measurements of temperature, vibration, pressure, and other relevant parameters, the system achieves precise RUL estimates for non-ECU connected parts without relying on imprecise software proxies.
Solution Approach 2:
The system introduces sensors as intermediary devices between the vehicle parts and the compiler. These sensors act as mediators that directly measure the physical state of vehicle parts and transmit this information to the compiler, enabling accurate monitoring of non-ECU connected parts without requiring direct ECU connectivity. This intermediary sensing layer bridges the gap between physical part conditions and digital analysis.
3Measurement precision
If sensors are installed on all vehicle parts, then actual usage data can be collected, but system complexity and cost increase
Solution Approach 1:
The system employs universal multi-functional sensors that can measure multiple parameters (temperature, vibration, pressure, humidity) simultaneously. This allows a single sensor unit to provide comprehensive usage data for various vehicle parts, reducing the overall number of sensors needed while maintaining measurement precision. The compiler is designed to handle diverse sensor inputs from different parts of the vehicle through a unified processing architecture, managing complexity through standardization.
Solution Approach 2:
The system segments the vehicle into modular zones or subsystems, each monitored by dedicated sensor groups. The compiler processes data in segmented batches corresponding to different vehicle subsystems (engine, transmission, suspension, etc.). This segmentation approach allows incremental implementation and reduces the complexity burden by breaking down the monolithic sensing and processing system into manageable modular units that can be deployed and maintained independently.
Data Source
AI summary
Described herein are embodiments of systems and methods for determining remaining useful life (RUL) of an electro-mechanical device. The methods may include providing a compiler and providing an external network communicatively connected with the compiler. The compiler determines forecasted reliability of the part, determines actual reliability of the part, divides forecasted reliability by actual reliability to determine forecasted useful operation time, Tx, and subtracts total operation time T from forecasted useful operation time Tx to determine RUL.