Bearing Fault Diagnosis Based on Improved Hilbert-Huang Transform

Fault diagnosis precision for rolling bearings under variable conditions has always been unsatisfactory. To solve this problem, a fault diagnosis method combining Hilbert-Huang transform (HHT), singular value decomposition (SVD), and Elman neural network is proposed in this paper. The method includes three steps. First, instantaneous amplitude matrices were obtained by using HHT from rolling bearing signals. Second, the singular value vector was acquired by applying SVD to the instantaneous amplitude matrices, thus reducing the dimension of the instantaneous amplitude matrix and obtaining the fault feature insensitive to working condition variation. Finally, an Elman neural network was applied to the rolling bearing fault diagnosis under variable working conditions according to the extracted feature vector. The experimental results show that the proposed method can effectively classify rolling bearing fault modes with high precision under different operating conditions. Moreover, the performance of the proposed HHT-SVD-Elman method has an advantage over that of EMD-SVD or WPT-PCA for feature extraction and Support Vector Machine (SVM) or Extreme Learning Machine (ELM) for classification.

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A Research Review of Hilbert-Huang Transform Used for Rolling Bearing Fault Diagnosis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.397-400.2152 ◽

2013 ◽

Vol 397-400 ◽

pp. 2152-2155 ◽

Cited By ~ 1

Author(s):

Qi Li ◽

Hui Wang

Keyword(s):

Fault Diagnosis ◽

Present Situation ◽

Rolling Bearing ◽

Research Direction ◽

Research Review ◽

Future Research ◽

Hilbert Huang Transform ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Stationary Vibration

Non-stationary vibration will appear when the fault rolling bearing is running. This paper summarizes the development present situation of application of Hilbert-Huang Transform solving the problem of rolling bearing fault diagnosis at home and abroad from several aspects, analyzes the current rolling bearing fault diagnosis methods combined with HHT, and sums up the practical applicability of HHT method through the comparison of rolling bearing fault diagnosis with other methods. It points out that HHT used for rolling bearing fault diagnosis still has problems to be solved. At last, it gives the future research direction of HHT.

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Endpoint Effect Suppression Based on Multipoint Extension in Bearing Fault Diagnosis

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001419500150 ◽

2019 ◽

Vol 33 (09) ◽

pp. 1950015 ◽

Cited By ~ 1

Author(s):

Shuang Xia ◽

Guohui Zhou

Keyword(s):

Fault Diagnosis ◽

Fault Detection ◽

Rolling Bearing ◽

Experimental Comparison ◽

End Effect ◽

Hilbert Huang Transform ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Data Distortion ◽

Mode Decomposition

In the rolling bearing fault detection, the Hilbert–Huang transform (HHT) has made remarkable achievements, but at present, the HHT still has the end effect problem, which will cause a lot of data distortion, spectrum confusion that will affect fault diagnosis result and error in the detection of rolling bearing faults in a serious manner. In response to this problem, this paper proposes a method of multi-point continuation at both ends of the signal to suppress the endpoint effectExtend at both ends of the signal, then perform empirical mode decomposition (EMD). The experimental comparison shows that the method has an effect on the endpoint effect.

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Bearing fault diagnosis using Hilbert-Huang transform (HHT) and support vector machine (SVM)

Mechanics & Industry ◽

10.1051/meca/2015067 ◽

2016 ◽

Vol 17 (3) ◽

pp. 308 ◽

Cited By ~ 7

Author(s):

Aida Kabla ◽

Karim Mokrani

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Support Vector ◽

Hilbert Huang Transform ◽

Bearing Fault ◽

Bearing Fault Diagnosis

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.80-81.875 ◽

2011 ◽

Vol 80-81 ◽

pp. 875-879 ◽

Cited By ~ 3

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.

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