首页|基于MFCC-IMFCC混合倒谱的托辊轴承故障诊断

基于MFCC-IMFCC混合倒谱的托辊轴承故障诊断

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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.
Fault diagnosis of idler bearings based on MFCC-IMFCC hybrid cepstral coefficients
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 reaches96.72% .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.

idler bearingsbearing fault acoustic signalhigh frequency informationMel-frequency cepstral coefficient(MFCC)inverse-Mel-frequency cepstral coefficient(IMFCC)hybrid cepstral coefficientslong short-term memory(LSTM)networks

陶瀚宇、陈换过、彭程程、高祥冲、杨磊

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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

杭州聆为信息技术有限公司 技术部,浙江 杭州 311215

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

国家自然科学基金资助项目国家重点研发计划项目

519755352021YFB3301601

2024

10.3969/j.issn.1001-4551.2024.07.009

机电工程
浙江大学　浙江省机电集团有限公司

机电工程

CSTPCD北大核心
影响因子：0.785
ISSN：1001-4551
年,卷(期)：2024.41(7)
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