Vehicle Component Life Prediction for Spare Parts Planning
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Solution Overview
Problem
Current methods for predicting the life expectancy of vehicle components are complex and costly, often requiring real-time measurements and extensive data, which is not feasible for vehicles like those in public transport where spare parts storage is limited and economically constrained.
Innovation Solution
A method that uses status data and operating parameters from similar vehicles to determine a function that predicts the life expectancy of a component, employing text analysis and mining to extract relevant information from maintenance data, allowing for the selection of the most appropriate function for the observed vehicle, thereby optimizing spare parts management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automatic prediction based on machine learning is used to predict component life expectancy, then prediction accuracy is improved, but system complexity and data requirements increase significantly
Solution Approach 1:
The patent uses maintenance data and status data from multiple similar vehicles as copies to create prediction models for the observed vehicle. Instead of requiring extensive real-time measurements from the target vehicle, the system leverages historical data from comparable vehicles to derive life expectancy predictions, thereby reducing the complexity and data requirements for the prediction system.
2Reliability
If real-time measurements and extensive data are collected for prediction, then prediction reliability is improved, but storage requirements and operational costs increase
Solution Approach 1:
The patent extracts only the essential features and parameters needed for prediction from the maintenance data and status data of multiple vehicles. Instead of storing and processing all available real-time measurement data, the system identifies and utilizes key operating parameters and status indicators that most significantly influence component life expectancy, thereby reducing storage requirements while maintaining prediction reliability.
3Reliability
If spare parts are stored in limited depot space, then immediate replacement capability is improved, but storage capacity and economic efficiency worsen
Solution Approach 1:
The patent performs preliminary prediction of component failures by analyzing maintenance data and status data from multiple similar vehicles. By predicting which components are likely to fail soon, the system enables workshops to order spare parts in advance before actual failures occur. This allows for optimized inventory levels - storing only the predicted necessary spare parts rather than maintaining large stocks of all possible components, thus improving replacement capability while reducing storage space requirements.
Data Source
AI summary
The invention relates to a method for predicting the life expectancy (24) of a component of an observed vehicle. A method for predicting the life expectancy (24) of a component may be achieved in that a processing unit is fed with status data (8) of components of selected vehicles. Moreover, the processing unit is fed with an operating parameter (10) for each of the components of the selected vehicles, which operating parameter (10) influences the status data (8) of the respective selected component. By the processing unit a function (16) between the operating parameter (10) and the status data (8) is determined for each of the selected components. One function (16), which fits best for the component of the observed vehicle, is selected by means of an algorithm. Further, the processing unit is fed with an operating parameter (22) of the component of the observed vehicle. Additionally, by the processing unit the life expectancy (24) of the component of the observed vehicle is predicted using the selected function (16) and the operating parameter (22) of the component of the observed vehicle.