摘要详情

ID / 提交时间

69 / 2024-08-15 20:50:01

标题

Acoustic Detection Algorithm of Train Bearings Based on Phased Derivative of CFD and the Fallow-GSC

关键字

trackside acoustic detection system,train bearings,microphone array,fault diagnosis,general sidelobe canceller

主题及专题

Special Sessions > SS02 Artificial Intelligence in Condition Monitoring, Fault Diagnosis and Maintenance of Industrial Equipment

状态

终稿

作者

BaoXuewen / Anhui University

LiuFang / Anhui University；ZhiHuan Science and Technology Co.,Ltd；MaiPu Intelligent and Technology Co.,Ltd

RenGuangwen / Anhui University

HanXianghong / Anhui University

ZhangBo / Anhui University

LiuYongbin / Anhui University

摘要

Trackside acoustic detection system (TADS) is one of the high-speed train online detection technologies, which has the advantages of early fault detection, low cost and non-contact measurement. In order to extract bearing fault characteristics from a strong noise environment effectively, an acoustic fault detection algorithm for train bearings is proposed based on the motion parameter estimation and noise cancellation with microphone array in this paper. This method uses the phase center finite difference (CFD) based on phase derivative to estimate to motion parameters of the sound source, and constructs the Fallow-generalized sidelobe canceller (GSC) through the motion parameters to perform beamforming (BF) to achieve noise elimination. Finally, a resampled times series is constructed to perform Doppler correction on the output signal after BF. The Fallow-GSC consists of variable fractional delay (VDF) digital filters, a fixed beamformer (FBF), a blocking matrix and an adaptive noise canceller (ANC) based on the recursive least squares (RLS) method. This method solves the problem of mutual cancellation between noise signal and the upper branch expected speech in the process of adaptive filtering of moving sound source faced by the traditional GSC algorithm, avoiding the distortion of expected speech. The effectiveness of the method is verified by practical experiments. Compared to the traditional GSC algorithm, it has a higher diagnostic accuracy and improves the signal-to-noise ratio.