Making EEMD more effective in extracting bearing fault features for intelligent bearing fault diagnosis by using blind fault component separation

2018 ◽  
Vol 34 (6) ◽  
pp. 3429-3441 ◽  
Author(s):  
Dong Wang ◽  
Cai Yi ◽  
Kwok Leung Tsui
Keyword(s):  
Fault Diagnosis ◽  
Component Separation ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Blind Fault

scholarly journals Bearing Fault Dominant Symptom Parameters Selection Based on Canonical Discriminant Analysis and False Nearest Neighbor Using GA Filtering Signal

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Shilun Zuo ◽  
Zhiqiang Liao
Keyword(s):  
Fault Diagnosis ◽  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
Canonical Discriminant Analysis ◽  
Selection Scheme ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Parameters Selection ◽  
Fault Features ◽  
Diagnosis Model

Symptom parameter is a popular method for bearing fault diagnosis, and it plays a crucial role in the process of building a diagnosis model. Many symptom parameters have been performed to extract signal fault features in time and frequency domains, and the improper selection of parameter will significantly influence the diagnosis result. For dealing with the problem, this paper proposes a novel dominant symptom parameters selection scheme for bearing fault diagnosis based on canonical discriminant analysis and false nearest neighbor using GA filtered signal. The original signal was filtered by a genetic algorithm (GA) at first and then mapped to the new characteristic subspace through the canonical discriminant analysis (CDA) algorithm. The map distance in the new characteristic subspace is calculated by the false nearest neighbor (FNN) method to interpret the dominance of symptom parameters. The dominant symptom parameters brought to the bearing diagnosis system can improve the diagnosis result. The effectiveness of the proposed method has been demonstrated by the diagnosis model and by comparison with other methods.

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.

A clustering K-SVD-based sparse representation method for rolling bearing fault diagnosis

2021 ◽  
Vol 63 (3) ◽  
pp. 160-167
Author(s):  
Qingwen Yu ◽  
Jimeng Li ◽  
Zhixin Li ◽  
Jinfeng Zhang
Keyword(s):  
Fault Diagnosis ◽  
Sparse Representation ◽  
Rolling Bearing ◽  
Vibration Signals ◽  
Redundant Dictionary ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Representation Method ◽  
The Impact

It is challenging to extract weak impulse features from vibration signals corrupted by strong noise in mechanical fault diagnosis. Due to its simple calculation, fast convergence and easy implementation, K-singular value decomposition (K-SVD) has been widely used in rolling bearing fault diagnosis. However, it fails to consider the influence of noise and harmonics on atoms learning from impulse characteristics, which results in many irrelevant atoms, and then increases the difficulty of extracting the impulse features in bearing fault signals. Therefore, a clustering K-SVD-based sparse representation method is proposed in this paper and it is combined with the particle swarm optimisation (PSO)-based time-varying filter empirical mode decomposition (TVF-EMD) for rolling bearing fault diagnosis. The PSO-based TVF-EMD is developed to automatically decompose the original signal to eliminate the impact of noise and harmonics on the impulses in the signal. Then, the clustering K-SVD method is applied to perform dictionary learning on the sensitive component containing impulses to obtain a redundant dictionary of atoms with obvious impulse patterns. Finally, the orthogonal matching pursuit (OMP) algorithm is introduced to extract the fault features from rolling bearing vibration signals. The experimental results show that the proposed method can improve the robustness of the dictionary atoms to noise and achieve the extraction of rolling bearing fault features.

scholarly journals Bearing Fault Diagnosis Using Grad-CAM and Acoustic Emission Signals

2020 ◽  
Vol 10 (6) ◽  
pp. 2050 ◽  
Author(s):  
JaeYoung Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Bearing Failure ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Effective Form ◽  
Fault Features ◽  
Rolling Elements ◽  
Bearing Characteristic

Bearing failure generates impulses when the rolling elements pass the cracked surface of the bearing. Over the past decade, acoustic emission (AE) techniques have been used to detect bearing failures operated in low-rotating speeds. However, since the high sampling rates of the AE signals make it difficult to design and extract discriminative fault features, deep neural network-based approaches have been proposed in several recent studies. This paper proposes a convolutional neural network (CNN)-based bearing fault diagnosis technique. In this work, the normalized bearing characteristic component (NBCC) is used as the input of CNN, which is an effective form of representing bearing failure symptoms. In addition, importance-weight is extracted using gradient-weighted class activation mapping (Grad-CAM) for visual explanation of CNN. In the experiment result, the proposed approach achieves high classification accuracy with reasonable visualization, which shows that CNN successfully learned the components of bearing characteristic frequency for each type of bearing failure.

scholarly journals Few-Shot Rolling Bearing Fault Diagnosis with Metric-Based Meta Learning

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6437
Author(s):  
Sihan Wang ◽  
Dazhi Wang ◽  
Deshan Kong ◽  
Jiaxing Wang ◽  
Wenhui Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Learning Method ◽  
Safety Issues ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Damage Data ◽  
Meta Learning

Fault diagnosis methods based on deep learning and big data have achieved good results on rotating machinery. However, the conventional deep learning method of bearing fault diagnosis is mostly based on laboratory artificial simulation data, and there is an error with actual fault data, which will reduce the generalization performance of the deep learning method. In addition, labeled data are very precious in real industrial environment. Due to expensive equipment and personnel safety issues, it is difficult to obtain a large amount of high-quality fault labeling data. Therefore, in this paper, we propose a metric-based meta-learning method named Reinforce Relation Network (RRN) for diagnosing bearing faults with few-shot samples. In the proposed method, a 1D convolution neural network is used to extract fault features, and a metric learner is used to predict the similarity between samples under different transfer conditions. Label smoothing and the Adabound algorithm are utilized to further improve the performance of network classification. The performance of the proposed method is verified on a dataset which contains artificial damage and natural damage data. The comparison studies with other methods demonstrate the superiority of the proposed method in the few-shot scenario.

scholarly journals Self-Adaptive Spectrum Analysis Based Bearing Fault Diagnosis

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3312 ◽  
Author(s):  
Jie Wu ◽  
Tang Tang ◽  
Ming Chen ◽  
Tianhao Hu
Keyword(s):  
Fault Diagnosis ◽  
Working Conditions ◽  
Spectrum Analysis ◽  
Support Vector ◽  
Bearing Fault ◽  
Feature Vectors ◽  
Bearing Fault Diagnosis ◽  
Fixed Dimension ◽  
Fault Features ◽  
Self Adaptive

Bearings are critical parts of rotating machines, making bearing fault diagnosis based on signals a research hotspot through the ages. In real application scenarios, bearing signals are normally non-linear and unstable, and thus difficult to analyze in the time or frequency domain only. Meanwhile, fault feature vectors extracted conventionally with fixed dimensions may cause insufficiency or redundancy of diagnostic information and result in poor diagnostic performance. In this paper, Self-adaptive Spectrum Analysis (SSA) and a SSA-based diagnosis framework are proposed to solve these problems. Firstly, signals are decomposed into components with better analyzability. Then, SSA is developed to extract fault features adaptively and construct non-fixed dimension feature vectors. Finally, Support Vector Machine (SVM) is applied to classify different fault features. Data collected under different working conditions are selected for experiments. Results show that the diagnosis method based on the proposed diagnostic framework has better performance. In conclusion, combined with signal decomposition methods, the SSA method proposed in this paper achieves higher reliability and robustness than other tested feature extraction methods. Simultaneously, the diagnosis methods based on SSA achieve higher accuracy and stability under different working conditions with different sample division schemes.

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 Correlation Coefficient of Simplified Neutrosophic Sets for Bearing Fault Diagnosis

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Lilian Shi
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Wavelet Packet ◽  
Energy Eigenvalue ◽  
Rolling Bearing ◽  
Neutrosophic Sets ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Features

In order to process the vagueness in vibration fault diagnosis of rolling bearing, a new correlation coefficient of simplified neutrosophic sets (SNSs) is proposed. Vibration signals of rolling bearings are acquired by an acceleration sensor, and a morphological filter is used to reduce the noise effect. Wavelet packet is applied to decompose the vibration signals into eight subfrequency bands, and the eigenvectors associated with energy eigenvalue of each frequency are extracted for fault features. The SNSs of each fault types are established according to energy eigenvectors. Finally, a correlation coefficient of two SNSs is proposed to diagnose the bearing fault types. The experimental results show that the proposed method can effectively diagnose the bearing faults.

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