Vehicle Access Device Simulation for Condition-Based Maintenance
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Solution Overview
Problem
Current condition-based maintenance methods for vehicle access devices, such as door and sliding step systems, are inefficient in terms of time and material, requiring extensive testing and data collection to predict failures accurately, especially in expensive and complex systems.
Innovation Solution
A method utilizing a simulation model based on design data to predict the maintenance status of access devices, where actual status signals are compared to expected status signals generated through a physical simulation model, allowing for reduced testing and more precise maintenance scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive testing and data collection are conducted to predict failures accurately, then measurement precision and reliability improve, but time consumption and material costs increase significantly
Solution Approach 1:
The patent creates a virtual copy (simulation model) of the access device that replicates its physical behavior and degradation patterns. This digital twin allows for virtual testing and data collection without requiring extensive physical testing, thereby maintaining prediction accuracy while dramatically reducing time and material resources needed for physical prototypes and test series
Solution Approach 2:
The simulation model is trained in advance using available data to learn degradation patterns and failure modes before actual failures occur. This preliminary training phase allows the system to predict future failures based on current sensor data, eliminating the need for extensive real-time testing and enabling proactive maintenance scheduling
2Reliability
If extensive testing and data collection are conducted to predict failures accurately, then reliability improves, but material costs and manufacturing resources increase
Solution Approach 1:
By creating a virtual simulation model that replicates the physical access device, the patent eliminates the need for multiple physical prototypes and extensive material consumption during testing phases. The simulation model can be repeatedly tested and validated without consuming additional physical materials, thereby maintaining reliability while reducing material waste
Solution Approach 2:
The patent replaces physical mechanical testing systems with a computational simulation model. Instead of conducting repeated physical tests that consume materials, the system uses software-based simulations to predict device behavior and degradation, substituting physical resource consumption with computational processing
3Reliability
If safety factors are applied when calculating service intervals, then reliability improves, but maintenance frequency increases and operational efficiency decreases
Solution Approach 1:
The system continuously monitors actual sensor data from the access device and compares it against the simulation model's predictions. This feedback loop allows the maintenance intervals to be dynamically adjusted based on actual device condition rather than fixed conservative schedules, maintaining reliability while optimizing operational efficiency by avoiding unnecessary maintenance stops
Solution Approach 2:
The patent transitions from static, predetermined maintenance intervals to dynamic, condition-based maintenance scheduling. The simulation model continuously updates predictions based on actual device behavior and degradation patterns, allowing maintenance intervals to adapt in real-time to the actual condition of the device, thereby optimizing both reliability and productivity
4Measurement precision
If artificial neural networks are trained with extensive test series, then prediction accuracy improves, but time consumption and computational resources increase
Solution Approach 1:
The simulation model is trained in advance using available historical data and test results to learn degradation patterns and failure modes. This preliminary training phase consolidates the learning process before deployment, allowing the model to make accurate predictions with minimal additional training time when deployed in actual operations
Solution Approach 2:
The patent uses the simulation model as a virtual training environment that replicates physical device behavior. This allows for extensive training data generation in the virtual domain without requiring proportional physical testing, thereby improving prediction accuracy while reducing the time and resources needed for physical prototype testing and model training
Data Source
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AI summary
The invention relates to a method for condition-based maintenance of an access device (2a, b) of a vehicle (1), in particular a public transport vehicle (1), wherein the access device (2a, b) comprises a movable element (3a, b) and an electric drive (4a, b) for adjusting the movable element (3a, b) and is attached to the vehicle (1), wherein the drive (4a, b) is controlled by control signals (5a, b), wherein actual condition signals (9a, b) are generated based on a detected condition of the access device (2a, b) to describe the condition, wherein the control signals (5a, b) and the actual condition signals (9a, b) are applied to a physical simulation model (10) for the computational simulation of the access device (1) and for determining expected future condition signals (12a, b), and wherein, based on a comparison between the actual condition signals (9a,b) and associated expected condition signals (12a, b) a maintenance condition of the access device (2a, b) is determined. The method is characterized in that the simulation model (11) was generated from design data (15) to describe the access device (2a, b).