Intelligent Condition Monitoring of Railway Vehicle Suspensions

Intelligent condition monitoring of railway vehicle suspensions integrates advanced sensing, signal processing and computational modelling to assess the health of suspension components in real time. Suspensions comprise primary elements (springs and dampers connecting wheelsets to bogie frames) and secondary elements (air springs or linkages between bogie and carbody) that together ensure ride comfort, stability and track friendliness. Traditional approaches have relied on scheduled inspections and threshold-based alarms, whereas contemporary methods employ data-driven algorithms and physics-based models to detect subtle degradations. Multibody dynamics simulations provide baseline predictions of dynamic response, while machine learning techniques—most notably convolutional neural networks—extract diagnostic features from vibration and acceleration signals. By fusing these approaches into onboard or trackside systems, rail operators can shift from reactive maintenance to condition-based strategies, reducing life-cycle costs, enhancing safety and optimising fleet availability across global rail networks.

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Intelligent Condition Monitoring of Railway Vehicle Suspensions publication trend

The graph below shows the total number of articles in intelligent condition monitoring of railway vehicle suspensions across all publications each year (not limited to Nature Index journals).

Technical terms

Condition monitoring: Continuous assessment of component health through sensor data and analytics to predict faults before failure.

Bogie: The undercarriage assembly of wheelsets, axles, suspension elements and frame that supports a rail vehicle’s body.

Primary suspension: The first stage of suspension, typically comprising springs and dampers between wheelsets and bogie frame to absorb track irregularities.

Secondary suspension: The stage between bogie and carbody, often using air springs or linkages, to enhance ride comfort and stability.

Convolutional Neural Network (CNN): A deep learning architecture employing convolutional filters to hierarchically extract features from input signals.

Multibody dynamics model: A mathematical framework representing interconnected rigid and flexible bodies to simulate dynamic behaviour under operational loads.

References

1. Suspension Parameter Estimation Method for Heavy-Duty Freight Trains Based on Deep Learning. Big Data and Cognitive Computing (2024).
2. Deep learning-based fault diagnostic network of high-speed train secondary suspension systems for immunity to track irregularities and wheel wear. Railway Engineering Science (2021).
3. Condition Monitoring of the Dampers in the Railway Vehicle Suspension Based on the Vibrations Response Analysis of the Bogie. Sensors (2022).