Transformer Vibration Forecasting for Advancing Rail Safety and Maintenance 4.0
This work addresses safety and maintenance efficiency for the railway industry by advancing condition monitoring techniques, though it appears incremental as it builds on existing transformer and spectral methods for a specific domain.
This study tackled the problem of predicting vibration signals in railway axles to prevent mechanical failures, introducing a transformer model called ShaftFormer and an alternative model with spectral methods that simulate signals under various conditions to improve dataset robustness for predictive maintenance.
Maintaining railway axles is critical to preventing severe accidents and financial losses. The railway industry is increasingly interested in advanced condition monitoring techniques to enhance safety and efficiency, moving beyond traditional periodic inspections toward Maintenance 4.0. This study introduces a robust Deep Autoregressive solution that integrates seamlessly with existing systems to avert mechanical failures. Our approach simulates and predicts vibration signals under various conditions and fault scenarios, improving dataset robustness for more effective detection systems. These systems can alert maintenance needs, preventing accidents preemptively. We use experimental vibration signals from accelerometers on train axles. Our primary contributions include a transformer model, ShaftFormer, designed for processing time series data, and an alternative model incorporating spectral methods and enhanced observation models. Simulating vibration signals under diverse conditions mitigates the high cost of obtaining experimental signals for all scenarios. Given the non-stationary nature of railway vibration signals, influenced by speed and load changes, our models address these complexities, offering a powerful tool for predictive maintenance in the rail industry.