Research on airspace filtering algorithm for trackside train bearings based on GSC-MVDR

Abstract Trackside Acoustic Detection Systems (TADS) employ microphone arrays installed on both sides of the railway tracks to collect sound signals emitted by wheelset bearings as trains pass by. These systems facilitate state monitoring and fault diagnosis through signal processing, offering capabilities such as non-contact measurement, low cost, and early warning of potential failures. However, the strong noise characteristics of trackside signals significantly impair diagnostic precision. To address this issue, this paper proposes a spatial filtering algorithm based on the Minimum Variance Distortion-less Response (MVDR) and the Generalized Sidelobe Canceller (GSC), achieving directional denoising. Additionally, the algorithm employs time-domain resampling techniques to correct the Doppler effect in signals.