基于MFCC-IMFCC混合倒谱的托辊轴承故障诊断  

Fault diagnosis of idler bearings based on MFCC-IMFCC hybrid cepstral coefficients

作　　者：陶瀚宇 陈换过[1] 彭程程 高祥冲 杨磊[1] TAO Hanyu;CHEN Huanguo;PENG Chengcheng;GAO Xiangchong;YANG Lei(Zhejiang Province's Key Laboratory of Reliability Technology for Mechanical and Electronic Product,Zhejiang Sci-Tech University Hangzhou 310018,China;Technical Department,Hangzhou Lingwei Information Technology Co.,Ltd.,Hangzhou 311215,China)

机构地区：[1]浙江理工大学浙江省机电产品可靠性技术研究重点实验室,浙江杭州310018 [2]杭州聆为信息技术有限公司技术部,浙江杭州311215

出　　处：《机电工程》2024年第7期1215-1222,共8页Journal of Mechanical & Electrical Engineering

基　　金：国家自然科学基金资助项目(51975535);国家重点研发计划项目(2021YFB3301601)。

摘　　要：针对梅尔倒谱系数(MFCC)对托辊轴承高频特征提取能力不足的问题,提出了一种基于梅尔倒谱系数和翻转梅尔倒谱系数(MFCC-IMFCC)的混合倒谱以及长短时记忆(LSTM)网络的托辊轴承故障诊断方法。首先,分析了三种状态下的托辊声音信号,明确了托辊轴承故障信息主要分布在中高频区域;然后,为有效保留高频信息,提取了MFCC-IMFCC,以帧级串联的方式组成了混合倒谱特征;最后,将混合倒谱特征输入到双层LSTM模型中进行了训练,建立了托辊轴承故障诊断模型。研究结果表明:针对托辊正常、滚动体故障和偏心旋转故障三种状态,LSTM结合混合倒谱特征的平均识别准确率达到96.72%,相比于单一的MFCC和IMFCC特征,准确率分别提升3.94%和7.41%,凸显了混合倒谱特征在表征托辊轴承故障信息方面的显著优势。Addressing the insufficient capability of Mel-frequency cepstral coefficient(MFCC)in extracting high-frequency features of idler bearing faults,a novel fault diagnosis method for idler bearings based on Mel-frequency cepstral coefficient and inverse-Mel-frequency cepstral coefficient(MFCC-IMFCC)hybrid cepstral coefficients and long short-term memory(LSTM)networks was proposed.Firstly,the acoustic signals of idler under three states were analyzed,revealing that the bearing fault information mainly resided in the mid-to-high-frequency range.Then,to effectively retain high-frequency information,MFCC-IMFCC were extracted and combined in a frame-level concatenation to form hybrid cepstral features.Finally,the hybrid cepstral features were input into a two-layer LSTM model for training,establishing a diagnostic model for idler bearing faults.The research results indicate that,for normal state,rolling element fault state,and eccentric rotation fault state,the average recognition accuracy of LSTM combined with hybrid cepstral features reaches 96.72%.Comparing to using individual MFCC and IMFCC features,the accuracy is improved by 3.94%and 7.41%,highlighting the significant advantage of hybrid cepstral features in representing information about idler bearing faults.

关 键 词：托辊轴承 轴承故障声音信号 高频信息 梅尔倒谱系数 翻转梅尔倒谱系数 混合倒谱系数 长短时记忆网络 

分 类 号：TH222[机械工程—机械制造及自动化] TH133.3