A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM

Yu, Yang; Yu, Dejie; Cheng, Junsheng

doi:10.1016/j.measurement.2006.10.010

Cited by 289 publications

(162 citation statements)

References 20 publications

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“…IMF energy features [29], IMF envelope spectrum [30] and IMF energy entropy [25] are among the features that can be extracted from IMFs. These features are passed on to some classification method such as neural networks [29] or support vector machines (SVM) [30]. In particular, authors in [25] use IMFs energy entropy to determine whether there exists a failure or not.…”

Section: Extreme Learning Machine Based On Stationary Wavelet Singulamentioning

confidence: 99%

Extreme Learning Machine Based on Stationary Wavelet Singular Values for Bearing Failure Diagnosis

Rodríguez¹,

Lagos²,

Cabrera³

et al. 2017

STUD INFORM CONTROL

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Abstract:Intelligence condition monitoring based on vibration signal analysis plays a key role in improving rolling bearings failure diagnosis in mechanical transmission systems. Unexpected failures in the bearings may cause machine breakdowns that are very expensive for the industry. Hence, this study proposes a method to the rolling element bearing failure diagnosis which is based on an extreme learning machine (ELM) algorithm combined with stationary wavelet transform (SWT) and singular value decomposition (SVD). The SWT is used to separate the vibration signals into a series of wavelet component signals. Then, the obtained wavelet components matrix is decomposed by means of a SVD method to obtain a set of wavelet singular values. Finally, the wavelet singular values are used as input to the extreme learning machine for classification among ten different bearing failure types. Obtained results using the proposed model shown high accuracy of diagnosis under variable speed condition.

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Section: Extreme Learning Machine Based On Stationary Wavelet Singulamentioning

confidence: 99%

Extreme Learning Machine Based on Stationary Wavelet Singular Values for Bearing Failure Diagnosis

Rodríguez¹,

Lagos²,

Cabrera³

et al. 2017

STUD INFORM CONTROL

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show abstract

“…There are many studies of roller bearing fault identification that draw support from SVM [38]. As the development of SVM, LSSVM has been widely used recently for its faster calculation ability than SVM.…”

Section: Application Of Imfcm In Fault Identificationmentioning

confidence: 99%

Fast EEMD Based AM-Correntropy Matrix and Its Application on Roller Bearing Fault Diagnosis

Jia

Qin

et al. 2016

Entropy

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Abstract:Roller bearing plays a significant role in industrial sectors. To improve the ability of roller bearing fault diagnosis under multi-rotating situation, this paper proposes a novel roller bearing fault characteristic: the Amplitude Modulation (AM) based correntropy extracted from the Intrinsic Mode Functions (IMFs), which are decomposed by Fast Ensemble Empirical mode decomposition (FEEMD) and employ Least Square Support Vector Machine (LSSVM) to implement intelligent fault identification. Firstly, the roller bearing vibration acceleration signal is decomposed by FEEMD to extract IMFs. Secondly, IMF correntropy matrix (IMFCM) as the fault feature matrix is calculated from the AM-correntropy model of the primary vibration signal and IMFs. Furthermore, depending on LSSVM, the fault identification results of the roller bearing are obtained. Through the bearing identification experiments in stationary rotating conditions, it was verified that IMFCM generates more stable and higher diagnosis accuracy than conventional fault features such as energy moment, fuzzy entropy, and spectral kurtosis. Additionally, it proves that IMFCM has more diagnosis robustness than conventional fault features under cross-mixed roller bearing operating conditions. The diagnosis accuracy was more than 84% for the cross-mixed operating condition, which is much higher than the traditional features. In conclusion, it was proven that FEEMD-IMFCM-LSSVM is a reliable technology for roller bearing fault diagnosis under the constant or multi-positioned operating conditions, and as such, it possesses potential prospects for a broad application of uses.

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“…In particular, the HHT method, as a time-frequency domain-based method, can clearly show what is happening transiently. Yang et al [14,15] sought to reduce some of the disadvantages of the envelope method by using the IMF method and reducing the result in defect identification and status monitoring. One of the main disadvantages of the envelope method was the determination of the central frequency during filtering, which requires the use of information and experience.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2017

IJMECH

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Today the maintenance and repair based on condition monitoring techniques of rotating equipment is one of the most important tools to prevent stopping of production and reduce the cost of maintenance and repair and the vibration analysis according to the width of identifiable defects scope has a specific importance. In this paper, we discuss about rolling bearings defects detection with using of normalized Hilbert huang approach.

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A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM

Cited by 289 publications

References 20 publications

Extreme Learning Machine Based on Stationary Wavelet Singular Values for Bearing Failure Diagnosis

Extreme Learning Machine Based on Stationary Wavelet Singular Values for Bearing Failure Diagnosis

Fast EEMD Based AM-Correntropy Matrix and Its Application on Roller Bearing Fault Diagnosis

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