噪声与振动控制2024，Vol.44Issue(3)：146-151,164,7.DOI:10.3969/j.issn.1006-1355.2024.03.022
基于改进PNCC-SVM的滚动轴承故障声纹识别方法
Voiceprint Recognition Method for Rolling Bearing Faults Diagnosis Based on Improved PNCC-SVM
摘要
Abstract
Aiming at the problems of low SNR and being prone to be disturbed by environmental noise in the sound signal analysis of rolling bearings,a voiceprint recognition method for rolling bearing faults diagnosis based on improved power-normalized cepstral coefficients(PNCC)and support vector machine(SVM)is proposed.Firstly,the bearing sound signal is preprocessed,and the improved PNCC is extracted as the feature vector.Then,the voiceprint recognition model is established by SVM algorithm to identify the bearing fault type,and the recognition accuracy of the proposed method after superimposing the noise is tested.The results show that the improved PNCC has the advantage of high recognition accuracy.Compared with the original PNCC,the average recognition accuracy is raised by 13.35%under noise interference,and the robustness is stronger.The research results may provide a reference for the application of sound signal feature extraction and fault identification of rolling bearings.关键词
故障诊断/滚动轴承/声纹识别/鲁棒性/功率归一化倒谱系数/支持向量机Key words
fault diagnosis/rolling bearing/voiceprint recognition/robustness/PNCC/SVM分类
机械制造引用本文复制引用
王寅杰,邓艾东,范永胜,占可,高原..基于改进PNCC-SVM的滚动轴承故障声纹识别方法[J].噪声与振动控制,2024,44(3):146-151,164,7.基金项目
江苏省碳达峰碳中和科技创新专项资金资助项目(BA2022214) （BA2022214）
江苏省重点研发计划资助项目(BE2020034) （BE2020034）
中央高校基本科研业务费专项资金资助项目(3203002201C3) （3203002201C3）
