Vehicle Component Health Prognosis Using Aging Model and Usage Data
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Solution Overview
Problem
Current component prognosis techniques in the automotive industry are one-dimensional, failing to integrate multiple factors that contribute to the age and remaining life of vehicle components, such as batteries and alternators, beyond operating parameters.
Innovation Solution
A multi-dimensional component prognosis system that utilizes an observer to integrate component health signatures, usage information, and a degradation model, including an aging model, comparison module, and age correction module to estimate and correct component age and calculate remaining life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional one-dimensional prognosis techniques are used, then the system is simple to implement, but the prognosis accuracy is insufficient because multiple contributing factors are not integrated
Solution Approach 1:
The system segments the prognosis task into distinct functional modules: an aging model module that processes usage information, a health signature extraction module, a comparison module, and an age correction module. Each module handles a specific aspect of the multi-dimensional integration, making the complex system manageable and maintainable while achieving accurate prognosis through coordinated operation of these segmented components
Solution Approach 2:
The aging model serves as an intermediary that bridges usage information and health signatures. It processes usage variables (temperature, humidity, power cycles) and generates expected health signature trajectories, which are then compared with actual measurements. This intermediary structure enables the integration of multiple factors without requiring direct complex interactions between all components
2Reliability
If multiple factors are integrated for multi-dimensional prognosis, then the prognosis accuracy improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The aging model is designed as a universal framework that can process multiple types of usage information (environmental conditions, power cycles, operational parameters) and generate predictions for different health signatures. This multi-functional approach allows the system to integrate various factors through a single cohesive model rather than requiring separate processing paths for each factor, reducing integration complexity while maintaining comprehensive analysis
Solution Approach 2:
The system implements feedback through the comparison module that continuously compares actual health signatures with predicted signatures from the aging model. The age correction module uses this feedback to adjust and refine the age estimation, creating a closed-loop system that improves reliability through iterative refinement while managing computational complexity through efficient error correction algorithms
Data Source
AI summary
A system and method for determining the health of a component includes retrieving measured health signatures from the component, retrieving component usage variables, estimating component health signatures using an aging model, determining an aging derivative using the aging model and calculating an aging error based on the estimated component health signatures, the aging derivative and the measured health signatures.