Echoformer: An echo state-embedded transformer for robust reconstruction of railway trackside noise on urban metro lines

• The first study to propose a railway trackside noise inference strategy utilizing a transformer-based deep surrogate model. • A novel RNN-transformer framework, termed Echoformer, is proposed to achieve the intricate time series mapping from track vibration to trackside noise. This method could facilitate the trackside noise monitoring and the development of noise control devices. • The reconstruction capability of Echoformer is elucidated by comparison with baseline methods in different scenarios. Besides, the robust Echoformer withstands situations when input information is incomplete, and when the input signal is polluted by noise. • A dataset stemmed from another urban metro monitoring project is adopted to verify the generalization ability of the Echoformer. Results show that the proposed method can generally capture the noise features across different metro lines. Railway rolling noise on straight railway lines has become a crucial environmental impact of railway systems. The vibration of the rail tracks is the primary contributor to the formation of rolling noise. Developing a surrogate model to capture the reflectional relationship between track vibrations and trackside noise is desired in two perspectives. Firstly, it offers a solution for noise monitoring with data loss, or when field conditions are restrictive for sensors’ deployment. Secondly, such a model can facilitate the design and optimization of noise control devices in laboratory, where the actual trackside noise is intricate to simulate. However, it is a dauting task to reveal the underlying relationship between track vibration and trackside noise. This work introduces Echoformer, a novel framework that blends echo states with the transformer architecture, designed to perform time series mapping. Comprehensive testing shows that the Echoformer outperforms conventional RNN architectures in reconstructing both near-field and far-field trackside noise. Moreover, the Echoformer exhibits remarkable resilience against information loss and noisy signal scenario, ensuring a robust reconstruction for the task. This study underscores the Echoformer’s potential as a steadfast tool in the realm of railway noise analysis.