scholarly journals A Novel Bearing Fault Diagnosis Method Based on GL-mRMR-SVM

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 784
Author(s):  
Xianghong Tang ◽  
Qiang He ◽  
Xin Gu ◽  
Chuanjiang Li ◽  
Huan Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Feature Fusion ◽  
Global Features ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Recognition ◽  
Fault Features ◽  
Svm Model ◽  
Diagnosis Method ◽  
The Time Domain

A convolutional neural network (CNN) has been used to successfully realize end-to-end bearing fault diagnosis due to its powerful feature extraction ability. However, the CNN is prone to focus on local information, ignoring the relationship between the whole and the part of the signal due to its unique structure. In addition, it extracts some fault features with poor robustness under noisy environment. A novel diagnosis model based on feature fusion and feature selection, GL-mRMR-SVM, is proposed to address this problem in this paper. First, the model combines the global features in the time-domain and frequency-domain of the raw data with the local features extracted by CNN to make full use of the signal information and overcome the weakness of traditional CNNs neglecting the overall signal. Then, the max-relevance min-redundancy (mRMR) algorithm is used to automatically extract the discriminative features from the fused features without any prior knowledge. Finally, the extracted discriminative features are input into the SVM for training and output the fault recognition results. The proposed GL-mRMR-SVM model was evaluated through experiments on bearing data of Case Western Reserve University (CWRU) and CUT-2 platform. The experimental results show that the proposed method is more effective than other intelligent diagnosis methods.

scholarly journals Bearing Fault Feature Extraction and Fault Diagnosis Method Based on Feature Fusion

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2524
Author(s):  
Huibin Zhu ◽  
Zhangming He ◽  
Juhui Wei ◽  
Jiongqi Wang ◽  
Haiyin Zhou
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Feature Fusion ◽  
Singular Value ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Fault Feature Extraction ◽  
Frequency Feature

Bearing is one of the most important parts of rotating machinery with high failure rate, and its working state directly affects the performance of the entire equipment. Hence, it is of great significance to diagnose bearing faults, which can contribute to guaranteeing running stability and maintenance, thus promoting production efficiency and economic benefits. Usually, the bearing fault features are difficult to extract effectively, which results in low diagnosis performance. To solve the problem, this paper proposes a bearing fault feature extraction method and it establishes a bearing fault diagnosis method that is based on feature fusion. The basic idea of the method is as follows: firstly, the time-frequency feature of the bearing signal is extracted through Wavelet Packet Transform (WPT) to form the time-frequency characteristic matrix of the signal; secondly, the Multi-Weight Singular Value Decomposition (MWSVD) is constructed by singular value contribution rate and entropy weight. The features of the time-frequency feature matrix obtained by WPT are further extracted, and the features that are sensitive to fault in the time-frequency feature matrix are retained while the insensitive features are removed; finally, the extracted feature matrix is used as the input of the Support Vector Machine (SVM) classifier for bearing fault diagnosis. The proposed method is validated by data sets from the time-varying bearing data from the University of Ottawa and Case Western Reserve University Bearing Data Center. The results show that the algorithm can effectively diagnose the bearing under the steady-state and unsteady state. This paper proposes that the algorithm has better fault diagnosis capabilities and feature extraction capabilities when compared with methods that aree based on traditional feature technology.

Rolling Bearing Fault Diagnosis Method Based On LMD Entropy Feature Fusion

2021 ◽  
Author(s):  
Bo Deng ◽  
Jingchao Li ◽  
Haijun Wang ◽  
Cheng Cong ◽  
Yulong Ying ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Feature Fusion ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

scholarly journals A Bearing Fault Diagnosis Method Based on PAVME and MEDE

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1402
Author(s):  
Xiaoan Yan ◽  
Yadong Xu ◽  
Daoming She ◽  
Wan Zhang
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Whale Optimization ◽  
Diagnosis Method ◽  
Bearing Vibration ◽  
Mode Extraction ◽  
Parameter Adaptive

When rolling bearings have a local fault, the real bearing vibration signal related to the local fault is characterized by the properties of nonlinear and nonstationary. To extract the useful fault features from the collected nonlinear and nonstationary bearing vibration signals and improve diagnostic accuracy, this paper proposes a new bearing fault diagnosis method based on parameter adaptive variational mode extraction (PAVME) and multiscale envelope dispersion entropy (MEDE). Firstly, a new method hailed as parameter adaptive variational mode extraction (PAVME) is presented to process the collected original bearing vibration signal and obtain the frequency components related to bearing faults, where its two important parameters (i.e., the penalty factor and mode center-frequency) are automatically determined by whale optimization algorithm. Subsequently, based on the processed bearing vibration signal, an effective complexity evaluation approach named multiscale envelope dispersion entropy (MEDE) is calculated for conducting bearing fault feature extraction. Finally, the extracted fault features are fed into the k-nearest neighbor (KNN) to automatically identify different health conditions of rolling bearing. Case studies and contrastive analysis are performed to validate the effectiveness and superiority of the proposed method. Experimental results show that the proposed method can not only effectively extract bearing fault features, but also obtain a high identification accuracy for bearing fault patterns under single or variable speed.

scholarly journals Rolling bearing fault diagnosis method based on VMD and LSSVM

2021 ◽  
Vol 1792 (1) ◽  
pp. 012035
Author(s):  
Xingtong Zhu ◽  
Zhiling Huang ◽  
Jinfeng Chen ◽  
Junhao Lu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method
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