NA-MEMD Is Actually What You Need for Computational Pulse Analysis

Xu, Huaxing; Tian, Yunzhi; Qia, Wang; Mao, Xiaobo; Zhao, Yuping; Huang, Luqi

doi:10.1109/access.2022.3204145

Cited by 1 publication

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The noise-assisted multivariate empirical modal decomposition is realized by adding a Gaussian white noise channel as an auxiliary channel, and the Gaussian white noise with spectrally uniform distribution characteristics is used as a reference for MEMD in the process of frequency adaptive decomposition to alleviate the problem of modal aliasing that exists in MEMD [49].The steps of NA-MEMD decomposition are as follows:…”

Section: ) Na-memd Algorithm For the Decomposition Of Multi-scale Sig...mentioning

confidence: 99%

An Intelligent Classification Approach via Multiple Classifier Fusion and Its Application to the Fault Diagnosis

Li,

Sun,

Zeng

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The performance of classifiers plays a crucial role in identifying fault categories under mechanical fault diagnosis task. This paper presents a new intelligent classification approach via NA-MEMD and multiple classifier fusion for fault diagnosis, which comprises four stages. First, NA-MEMD extracts time-domain and frequency-domain features from the original vibration signal. Second, Sensitive feature extraction using improved Fisher's criterion method. Third, the sensitive feature sets (SFS) are input to Depth Gaussian Restricted Boltzmann Machine (DG-RBM), RNN, and CNN classification methods to attain the complementary benefits and substantial fusion of various classifiers. Final, the Bayesian belief approach is utilized for fusing the classification results of multiple classifiers to obtain the diagnosis results. Experiments on rolling bearing datasets reveal that the presented approach can precisely detect the fault conditions and provide a classification efficiency superior to the single classifiers and ensemble of all classifiers. The experimental results have revealed the efficiency, generalization, and robustness of the multiple classifier fusion.INDEX TERMS Multiple classifier fusion, NA-MEMD, Fisher's criterion, Bayesian belief method

show abstract

Section: ) Na-memd Algorithm For the Decomposition Of Multi-scale Sig...mentioning

confidence: 99%

An Intelligent Classification Approach via Multiple Classifier Fusion and Its Application to the Fault Diagnosis

Li,

Sun,

Zeng

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

NA-MEMD Is Actually What You Need for Computational Pulse Analysis

Abstract: The authors also would like to appreciate all volunteers for providing the invaluable pulse waveform datas and doctors providing help in pulse signal collection.

Cited by 1 publication

References 37 publications

An Intelligent Classification Approach via Multiple Classifier Fusion and Its Application to the Fault Diagnosis

An Intelligent Classification Approach via Multiple Classifier Fusion and Its Application to the Fault Diagnosis

Contact Info

Product

Resources

About