Fault Detection and Diagnosis in High-Speed Train Systems

Fault detection and diagnosis (FDD) in high-speed train systems is central to ensuring operational safety, reliability and cost-effective maintenance. These systems encompass electric traction drives, braking subsystems, pantographs, signalling interfaces and a multitude of sensors operating under severe environmental and dynamic loading conditions. Faults may manifest as sensor drifts, actuator degradations, electrical imbalances or control anomalies, each with the potential to compromise ride comfort and jeopardise safety. Traditional model-based schemes rely on analytical representations of subsystem dynamics, generating residuals through observers or parity checks, whereas data-driven methodologies exploit large volumes of onboard monitoring data to learn normal and abnormal patterns. Hybrid approaches combine the robustness of physical-law models with the adaptability of machine learning. Recent advances have focused on early detection of incipient faults—tiny deviations prior to overt failures—through enhancement of signal-to-noise ratios, multi-sensor information fusion and application of deep architectures. The integration of FDD within condition-based maintenance frameworks permits real-time monitoring, automated decision support and adaptive reconfiguration strategies to prolong component lifespan, reduce unplanned downtime and optimise lifecycle costs.

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Fault Detection and Diagnosis in High-Speed Train Systems publication trend

The graph below shows the total number of articles in fault detection and diagnosis in high-speed train systems across all publications each year (not limited to Nature Index journals).

Technical terms

Incipient fault: A fault in its earliest stage, characterised by subtle deviations from normal operation before full manifestation.

Observer-based approach: A model-based technique using an internal system replica to generate residuals for fault detection.

Data-driven approach: A diagnostic method relying on statistical or machine-learning models built from historical monitoring data.

Condition-based maintenance: A maintenance strategy triggered by real-time assessment of equipment health rather than fixed schedules.

Principal component analysis (PCA): A statistical tool that transforms correlated variables into uncorrelated components to highlight variance.

Sliding mode observer: A robust state estimator that uses discontinuous control laws to reject disturbances and detect faults.

Broad Learning System (BLS): A neural network architecture that expands feature maps laterally rather than through deep layers, reducing computational complexity for online anomaly detection.

References

1. Deep PCA-Based Incipient Fault Diagnosis and Diagnosability Analysis of High-Speed Railway Traction System via FNR Enhancement. Machines (2023).
2. A Hybrid Sensor Fault Diagnosis for Maintenance in Railway Traction Drives. Sensors (2020).
3. An Efficient Anomaly Detection for High-Speed Train Braking System Using Broad Learning System. IEEE Access (2021).