scholarly journals Bearing Fault Diagnosis Considering the Effect of Imbalance Training Sample

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 386
Author(s):  
Lin Lin ◽  
Bin Wang ◽  
Jiajin Qi ◽  
Da Wang ◽  
Nantian Huang
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Training Sample ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Training Samples ◽  
Empirical Wavelet Transform ◽  
The Impact

To improve the accuracy of the recognition of complicated mechanical faults in bearings, a large number of features containing fault information need to be extracted. In most studies regarding bearing fault diagnosis, the influence of the limitation of fault training samples has not been considered. Furthermore, commonly used multi-classifiers could misidentify the type or severity of faults without using normal samples as training samples. Therefore, a novel bearing fault diagnosis method based on the one-class classification concept and random forest is proposed for reducing the impact of the limitations of the fault training sample. First, the bearing vibration signals are decomposed into numerous intrinsic mode functions using empirical wavelet transform. Then, 284 features including multiple entropy are extracted from the original signal and intrinsic mode functions to construct the initial feature set. Lastly, a hybrid classifier based on one-class support vector machine trained by normal samples and a random forest trained by imbalanced fault data without some specific severities is set up to accurately identify the mechanical state and specific fault type of the bearings. The experimental results show that the proposed method can significantly improve the classification accuracy compared with traditional methods in different diagnostic target.

scholarly journals Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Bo Wu ◽  
Zhe Tong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Optimal Number ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

A sub-domain adaptive transfer learning base on residual network for bearing fault diagnosis

2021 ◽  
pp. 107754632110429
Author(s):  
Chongyu Wang ◽  
Guangya Zhu ◽  
Tianyuan Liu ◽  
Yonghui Xie ◽  
Di Zhang
Keyword(s):  
Big Data ◽  
Fault Diagnosis ◽  
Transfer Learning ◽  
Research Work ◽  
Research Field ◽  
Residual Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Training Samples ◽  
The Impact

Bearing fault diagnosis is an important research field for rotating machinery health monitoring. Recently, many intelligent fault diagnosis methods driven by big data, such as transfer learning, have been studied. However, there are two shortcomings for the prior transfer learning method in industry application. First, it is necessary to design a complex loss function to enhance the similarity between the two domains further. Second, previous studies required big data both in source and target task, without considering the lack of sufficient training samples. Inspired by relevant research work, this article proposes a local joint distribution discrepancy to increase similar features. A sub-domain adaptive transfer learning is designed to detect bearing faults based on the residual network. Two kinds of transfer experiments are designed to verify the method effectiveness. After that, the impact of small training samples and noise on the results is explored. The proposed method reaches high accuracy.

Incipient Bearing Fault Diagnosis Based on Improved Hilbert-Huang Transform and Support Vector Machine

2011 ◽  
Vol 80-81 ◽  
pp. 875-879 ◽  
Author(s):  
Ji Hong Yan ◽  
Lei Lu
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Training Data ◽  
Practical Significance ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Screening Process ◽  
Hilbert Huang Transform ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

The detection and diagnosis of equipment failures are of great practical significance and paramount importance in the sense that an early detection of these faults may help to avoid performance degradation and major damage. In this work, a novel methodology based on improved Hilbert-Huang transform (HHT) and support vector machine (SVM) was proposed for incipient bearing fault diagnosis with insufficient training data. Singular value decomposition (SVD) was employed to detect periodic features, and then extending of the original signal was carried out based on support vector regression (SVR). A screening process was conducted to select the vital intrinsic mode functions (IMFs). Finally, features extracted from the obtained IMFs were applied to identify different bearing faults based on SVM. To investigate the property of proposed method, an experimental test rig was designed such that varying sizes defects of a test bearing could be seeded, and it’s concluded that the effectiveness of the proposed algorithm in early bearing fault diagnosis even with insufficient training data.

scholarly journals A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis

2018 ◽  
Vol 8 (9) ◽  
pp. 1621 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Yong Ren ◽  
Gongbo Zhou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Feature Space ◽  
Support Vector ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Acceleration Sensors ◽  
Reconstructed Signal ◽  
Original Feature

Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings.

scholarly journals A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
HungLinh Ao ◽  
Junsheng Cheng ◽  
Kenli Li ◽  
Tung Khac Truong
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Roller Bearing ◽  
Local Characteristic ◽  
Support Vector ◽  
Svm Classifier ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Energy Entropy

This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

Sign in / Sign up

Export Citation Format

Share Document