A novel collaborative diagnosis approach of incipient faults based on VMD and SCN for rolling bearing

2021 ◽  
Author(s):  
Darong Huang ◽  
Yunqian Li ◽  
Shuyue Guan ◽  
Xu Zhang ◽  
Min Tang
Keyword(s):  
Rolling Bearing ◽  
Incipient Faults ◽  
Collaborative Diagnosis ◽  
Diagnosis Approach

scholarly journals A Classification Algorithm of Fault Modes-Integrated LSSVM and PSO with Parameters’ Optimization of VMD

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yunqian Li ◽  
Darong Huang ◽  
Zixia Qin
Keyword(s):  
Rolling Bearing ◽  
Classification Algorithm ◽  
Parameters Optimization ◽  
Identification Accuracy ◽  
Adaptive Selection ◽  
Failure Data ◽  
Incipient Faults ◽  
Diagnosis Method ◽  
Collaborative Diagnosis ◽  
Application Prospects

To overcome the shortcomings that the early fault characteristics of rolling bearing are not easy to be extracted and the identification accuracy is not high enough, a novel collaborative diagnosis method is presented combined with VMD and LSSVM for incipient faults of rolling bearing. First, the basic concept of VMD was introduced in detail, and then, the adaptive selection principle of parameter K in VMD was constructed by instantaneous frequency mean. Furthermore, we used Lagrangian polynomial and Euclidean norm to verify the value of K accurately. Secondly, we proposed a classification algorithm based on PSO-optimized LSSVM. Meanwhile, the flowchart of the classification algorithm of fault modes may be also designed. Third, the experiment shows that the presented algorithm in this paper is effective by using the existing failure data provided by the laboratory of Guangdong Petrochemical Research Institute. Finally, some conclusions and application prospects were discussed.

scholarly journals A Fault Diagnosis Approach for Rolling Bearing Integrated SGMD, IMSDE and Multiclass Relevance Vector Machine

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4352 ◽  
Author(s):  
Xiaoan Yan ◽  
Ying Liu ◽  
Minping Jia
Keyword(s):  
Fault Diagnosis ◽  
Symplectic Geometry ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Relevance Vector Machine ◽  
Symbolic Dynamic ◽  
Bearing Fault ◽  
Fault Features ◽  
Bearing Vibration ◽  
Diagnosis Approach

The vibration signal induced by bearing local fault has strong nonstationary and nonlinear property, which indicates that the conventional methods are difficult to recognize bearing fault patterns effectively. Hence, to obtain an efficient diagnosis result, the paper proposes an intelligent fault diagnosis approach for rolling bearing integrated symplectic geometry mode decomposition (SGMD), improved multiscale symbolic dynamic entropy (IMSDE) and multiclass relevance vector machine (MRVM). Firstly, SGMD is employed to decompose the original bearing vibration signal into several symplectic geometry components (SGC), which is aimed at reconstructing the original bearing vibration signal and achieving the purpose of noise reduction. Secondly, the bat algorithm (BA)-based optimized IMSDE is presented to evaluate the complexity of reconstruction signal and extract bearing fault features, which can solve the problems of missing of partial fault information existing in the original multiscale symbolic dynamic entropy (MSDE). Finally, IMSDE-based bearing fault features are fed to MRVM for achieving the identification of bearing fault categories. The validity of the proposed method is verified by the experimental and contrastive analysis. The results show that our approach can precisely identify different fault patterns of rolling bearings. Moreover, our approach can achieve higher recognition accuracy than several existing methods involved in this paper. This study provides a new research idea for improvement of bearing fault identification.

scholarly journals Rolling Bearing Diagnosis Based on Composite Multiscale Weighted Permutation Entropy

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 821 ◽  
Author(s):  
Xiong Gan ◽  
Hong Lu ◽  
Guangyou Yang ◽  
Jing Liu
Keyword(s):  
Nearest Neighbor ◽  
Rolling Bearing ◽  
Permutation Entropy ◽  
Features Selection ◽  
K Nearest Neighbor ◽  
Bearing Fault Diagnosis ◽  
Simulated Signal ◽  
Knn Classifier ◽  
Nonlinear Dynamic Characteristics ◽  
Diagnosis Approach

In this paper, composite multiscale weighted permutation entropy (CMWPE) is proposed to evaluate the complexity of nonlinear time series, and the advantage of the CMWPE method is verified through analyzing the simulated signal. Meanwhile, considering the complex nonlinear dynamic characteristics of fault rolling bearing signal, a rolling bearing fault diagnosis approach based on CMWPE, joint mutual information (JMI) feature selection, and k-nearest-neighbor (KNN) classifier (CMWPE-JMI-KNN) is proposed. For CMWPE-JMI-KNN, CMWPE is utilized to extract the fault rolling bearing features, JMI is applied for sensitive features selection, and KNN classifier is employed for identifying different rolling bearing conditions. Finally, the proposed CMWPE-JMI-KNN approach is used to analyze the experimental dataset, the analysis results indicate the proposed approach could effectively identify different fault rolling bearing conditions.

scholarly journals Fault diagnosis approach of rolling bearing based on NA-MEMD and FRCMAC

2019 ◽  
Vol 2019 (13) ◽  
pp. 107-113
Author(s):  
Sheng Liu ◽  
Yue Sun ◽  
Lanyong Zhang
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Diagnosis Approach
Sign in / Sign up

Export Citation Format

Share Document