Rolling element bearing fault diagnosis under slow speed operation using wavelet de-noising

Mishra, C.; Samantaray, Arun Kumar; Chakraborty, Goutam

doi:10.1016/j.measurement.2017.02.033

Cited by 108 publications

(62 citation statements)

References 31 publications

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“…The EMD method can decompose a signal into a sum of intrinsic mode functions (IMFs) according to the oscillatory nature of the signal [4]. On the other hand, the WT decomposes a signal into several low-frequency components and high-frequency components, which can show features of hidden failures [5][6][7][8]. From signal decomposition methods, such as those above, different features can be calculated, such as energy entropy [9], permutation entropy [10], kurtosis value [11,12], relative energy [13], The remaining sections of this article are as follows.…”

Section: Introductionmentioning

confidence: 99%

Stationary Wavelet Singular Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis

Rodríguez

Cabrera

Lagos

et al. 2017

Entropy

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Abstract:The behavioural diagnostics of bearings play an essential role in the management of several rotation machine systems. However, current diagnostic methods do not deliver satisfactory results with respect to failures in variable speed rotational phenomena. In this paper, we consider the Shannon entropy as an important fault signature pattern. To compute the entropy, we propose combining stationary wavelet transform and singular value decomposition. The resulting feature extraction method, that we call stationary wavelet singular entropy (SWSE), aims to improve the accuracy of the diagnostics of bearing failure by finding a small number of high-quality fault signature patterns. The features extracted by the SWSE are then passed on to a kernel extreme learning machine (KELM) classifier. The proposed SWSE-KELM algorithm is evaluated using two bearing vibration signal databases obtained from Case Western Reserve University. We compare our SWSE feature extraction method to other well-known methods in the literature such as stationary wavelet packet singular entropy (SWPSE) and decimated wavelet packet singular entropy (DWPSE). The experimental results show that the SWSE-KELM consistently outperforms both the SWPSE-KELM and DWPSE-KELM methods. Further, our SWSE method requires fewer features than the other two evaluated methods, which makes our SWSE-KELM algorithm simpler and faster.

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Section: Introductionmentioning

confidence: 99%

Stationary Wavelet Singular Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis

Rodríguez

Cabrera

Lagos

et al. 2017

Entropy

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show abstract

“…Therefore, the major concern in bearing fault feature extraction is to determine which signal processing tools and algorithms to use to distinguish and diagnose early stage fault characteristics. Up to now, various fault diagnosis techniques have been proposed attempting to address the above challenges, such as wavelet/wavelet-packet transform [4], local mean decomposition (LMD) and its extension [5], minimum entropy deconvolution (MED) and its extension [6,7] and artificial intelligence (AI) algorithms such as artificial neural network (ANN) and fuzzy algorithm [8][9][10], Hilbert envelope spectrum [11], energy and entropy methods [12][13][14], higher order statistical techniques [15][16][17][18], to mention just a few. Unfortunately, some potential drawbacks and severe shortcomings related to the common techniques still remained unresolved.…”

Section: Introductionmentioning

confidence: 99%

Incipient Fault Diagnosis of Rolling Bearings Based on Impulse-Step Impact Dictionary and Re-Weighted Minimizing Nonconvex Penalty Lq Regular Technique

Liang

2017

Entropy

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Abstract:The periodical transient impulses caused by localized faults are sensitive and important characteristic information for rotating machinery fault diagnosis. However, it is very difficult to accurately extract transient impulses at the incipient fault stage because the fault impulse features are rather weak and always corrupted by heavy background noise. In this paper, a new transient impulse extraction methodology is proposed based on impulse-step dictionary and re-weighted minimizing nonconvex penalty Lq regular (R-WMNPLq, q = 0.5) for the incipient fault diagnosis of rolling bearings. Prior to the sparse representation, the original vibration signal is preprocessed by the variational mode decomposition (VMD) technique. Due to the physical mechanism of periodic double impacts, including step-like and impulse-like impacts, an impulse-step impact dictionary atom could be designed to match the natural waveform structure of vibration signals. On the other hand, the traditional sparse reconstruction approaches such as orthogonal matching pursuit (OMP), L1-norm regularization treat all vibration signal values equally and thus ignore the fact that the vibration peak value may have more useful information about periodical transient impulses and should be preserved at a larger weight value. Therefore, penalty and smoothing parameters are introduced on the reconstructed model to guarantee the reasonable distribution consistence of peak vibration values. Lastly, the proposed technique is applied to accelerated lifetime testing of rolling bearings, where it achieves a more noticeable and higher diagnostic accuracy compared with OMP, L1-norm regularization and traditional spectral Kurtogram (SK) method.

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“…If a https://doi.org/10.24846/v26i3y201704 failure exists, then the PE of a subset of selected IMFs is computed and used as the input of a SVM in order to classify the type of the failure as well as its severity. Moreover, WT can decompose a signal into several independent frequency subbands and show features of hidden failures [1,15,18,24]. In [8] authors combine WT and EMD to create a new time-frequency analysis method namely empirical wavelet transform (EWT).…”

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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Rolling element bearing fault diagnosis under slow speed operation using wavelet de-noising

Cited by 108 publications

References 31 publications

Stationary Wavelet Singular Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis

Stationary Wavelet Singular Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis

Incipient Fault Diagnosis of Rolling Bearings Based on Impulse-Step Impact Dictionary and Re-Weighted Minimizing Nonconvex Penalty Lq Regular Technique

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

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