Research on blind source separation of operation sounds of metro power transformer through an Adaptive Threshold REPET algorithm  

作　　者：Liang Chen Liyi Xiong Fang Zhao Yanfei Ju An Jin 

机构地区：[1]Railway Science and Technology Research and Development Center,China Academy of Railway Sciences Corporation Limited,Beijing,China [2]Development and Reform Department,China State Railway Group Co.,Ltd.,Beijing,China

出　　处：《Railway Sciences》2024年第5期609-621,共13页铁道科学（英文）

基　　金：the China Academy of Railway Sciences Corporation Limited(2023YJ257).

摘　　要：Purpose–The safe operation of the metro power transformer directly relates to the safety and efficiency of the entire metro system.Through voiceprint technology,the sounds emitted by the transformer can be monitored in real-time,thereby achieving real-time monitoring of the transformer’s operational status.However,the environment surrounding power transformers is filled with various interfering sounds that intertwine with both the normal operational voiceprints and faulty voiceprints of the transformer,severely impacting the accuracy and reliability of voiceprint identification.Therefore,effective preprocessing steps are required to identify and separate the sound signals of transformer operation,which is a prerequisite for subsequent analysis.Design/methodology/approach–This paper proposes an Adaptive Threshold Repeating Pattern Extraction Technique(REPET)algorithm to separate and denoise the transformer operation sound signals.By analyzing the Short-Time Fourier Transform(STFT)amplitude spectrum,the algorithm identifies and utilizes the repeating periodic structures within the signal to automatically adjust the threshold,effectively distinguishing and extracting stable background signals from transient foreground events.The REPET algorithm first calculates the autocorrelation matrix of the signal to determine the repeating period,then constructs a repeating segment model.Through comparison with the amplitude spectrum of the original signal,repeating patterns are extracted and a soft time-frequency mask is generated.Findings–After adaptive thresholding processing,the target signal is separated.Experiments conducted on mixed sounds to separate background sounds from foreground sounds using this algorithm and comparing the results with those obtained using the FastICA algorithm demonstrate that the Adaptive Threshold REPET method achieves good separation effects.Originality/value–A REPET method with adaptive threshold is proposed,which adopts the dynamic threshold adjustment mechanism,adaptively calculat

关 键 词：TRANSFORMER Voiceprint recognition Blind source separation Mel frequency cepstral coefficients(MFCC) Adaptive threshold 

分 类 号：U224[交通运输工程—道路与铁道工程]