Data Recording Triggering Device for Rail Vehicle Condition Monitoring
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Solution Overview
Problem
Existing data recording triggering systems for condition-based maintenance in electrical and electromechanical systems, such as rail vehicles, face challenges with high data transfer and storage costs due to excessive data collection and require complex analytics, limiting their effectiveness.
Innovation Solution
A data recording triggering device that uses a machine learning algorithm to determine motor characteristics and steady-state operation, allowing for selective data sampling based on constant parameters like speed, loading conditions, and geographical location, reducing unnecessary data collection and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If constant data sampling at high frequency is used, then measurement precision is improved, but data transfer and storage costs increase excessively
Solution Approach 1:
The patent applies dynamics by transitioning from static constant sampling to dynamic conditional sampling. The sampling frequency is dynamically adjusted based on detected operational states - high frequency during steady-state conditions for precision measurement, and low or no frequency during transient states to reduce data volume. This resolves the contradiction by making the sampling strategy adaptive rather than fixed.
Solution Approach 2:
The patent changes the sampling parameter (frequency) based on operational conditions. By detecting parameters such as motor current, speed, and load, the system determines whether to sample at high frequency (for precision) or low frequency (to reduce data volume). This parameter adaptation allows the system to optimize between measurement precision and data quantity according to actual system state.
2Device complexity
If predefined static thresholds are used for triggering data recording, then device complexity is reduced, but adaptability to wide range of operational conditions deteriorates
Solution Approach 1:
The patent implements feedback by continuously monitoring operational parameters (current, speed, load) and using this information to dynamically determine sampling triggers. Instead of static thresholds, the system feedback-adjusts its sampling behavior based on real-time operational state, enabling adaptation to wide ranges of conditions while maintaining manageable complexity through automated decision-making.
Solution Approach 2:
The system performs self-service by automatically detecting operational states and autonomously deciding when to sample data without external intervention. The machine learning algorithm self-adjusts sampling triggers based on detected patterns, reducing the need for complex external control while expanding adaptability to various operational conditions.
3Measurement precision
If high frequency data sampling is used, then measurement precision is improved, but loss of energy increases
Solution Approach 1:
The patent applies dynamics by making the sampling frequency adaptive rather than constant. High frequency sampling is dynamically activated only during steady-state operational conditions when measurement precision is most critical, while low frequency or no sampling occurs during transient states. This dynamic adjustment significantly reduces overall energy consumption while maintaining precision when needed.
Solution Approach 2:
The system implements periodic action by sampling data at high frequency only during specific operational periods (steady-state conditions) rather than continuously. This periodic sampling strategy maintains measurement precision during critical phases while reducing energy consumption during non-critical transient phases.
Data Source
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AI summary
A data recording triggering device 1 for a system that has one or more sensors 109 comprising: a data input unit 2 configured to receive measurement data determined by the one or more sensors 109 as input data; a data processing unit 3 configured to process the input data received by the data input unit 2; a data output unit 4 configured to output a data recording signal by which recording of the measurement data is either started or terminated.