Topic Correlation Analysis for Bearing Fault Diagnosis Under Variable Operating Conditions

12th International Conference on Damage Assessment of Structures

Chen, Chao^1 ; Shen, Fei^1 ; Yan, Ruqiang^1

School of Instrument Science and Engineering, Southeast University Nanjing, Jiangsu

210096, China^1

关键词: Bearing fault diagnosis;    Correlation analysis;    Different distributions;    Different domains;    Domain specific;    Latent Semantic Analysis;    Target bearing;    Variable operating condition;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/842/1/012045/pdf
DOI  :  10.1088/1742-6596/842/1/012045

来源: IOP

【 摘 要 】

This paper presents a Topic Correlation Analysis (TCA) based approach for bearing fault diagnosis. In TCA, Joint Mixture Model (JMM), a model which adapts Probability Latent Semantic Analysis (PLSA), is constructed first. Then, JMM models the shared and domain-specific topics using "fault vocabulary" . After that, the correlations between two kinds of topics are computed and used to build a mapping matrix. Furthermore, a new shared space spanned by the shared and mapped domain-specific topics is set up where the distribution gap between different domains is reduced. Finally, a classifier is trained with mapped features which follow a different distribution and then the trained classifier is tested on target bearing data. Experimental results justify the superiority of the proposed approach over the stat-of-the-art baselines and it can diagnose bearing fault efficiently and effectively under variable operating conditions.