A fault diagnosis method combined with LMD, sample entropy and energy ratio for roller bearings

Han, Minghong; Pan, Jun

doi:10.1016/j.measurement.2015.08.019

Cited by 157 publications

(79 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Recently, the gain controllable clipped histogram equalization (GC-CHE) [9] method performs clipped histogram equalization for preserving the brightness of an image. In Demerol's method of contrast enhancement the singular value decomposition matrix [10][11] was applied to the low level sub band. This method distorting the image details at low and high intensity regions.…”

Section: Proposed Algorithm For Image Enhancementmentioning

confidence: 99%

An Image Enhancement Framework for Fault Diagnosis and Feature Extraction

2017

IJSR

View full text Add to dashboard Cite

Image enhancement refers to accentuation, or sharpening, of image features such as edges, boundaries, or contrast to make a graphic display more useful for display and analysis. The enhancement process does not increase the inherent information content in the data. But it does increase the dynamic range of the chosen features so that they can be detected easily. The greatest difficulty in image enhancement is quantifying the criterion for enhancement. Therefore, a large number of image enhancement techniques are empirical and require interactive procedures to obtain satisfactory results. Many image enhancement techniques are based on spatial operations performed on local neighborhoods of input pixels. Often, the image is convolved with a finite impulse response filter called spatial mask. This study will highlight various image enhancement techniques along with their benchmark results.Image enhancement technologies have attracted much attention during the diagnosis process. The principle objective of image enhancement techniques is to process an input image so that the resultant image is more suitable than the original image for specific application. Traditional global histogram equalization usually causes excessive contrast. Enhancement while local histogram equalization may cause block effect. To overcome these problems, a new method for image contrast enhancement is developed. The novelty of the proposed method is that the weighted average of the histogram equalized, gamma corrected and the original image are combined to obtained the enhanced processed image .The proposed algorithm not only achieve contrast enhancement but also preserves the brightness level. Experimental results show that the proposed algorithm has good performance on enhancing contrast and visibility for a majority of images.

show abstract

“…In 2015, a fault diagnosis method combined with LMD, sample entropy(SampEn) and energy ratio for roller bearings was presented by Minghong Han [6]. SampEn was an improvement of approximate entropy(ApEn), since ApEn shows apparent disadvantages like the heavy dependence on data length and lack of relative consistency.…”

Section: Introductionmentioning

confidence: 99%

Incipient loose detection of hoops for pipeline based on ensemble empirical mode decomposition and multi-scale entropy and extreme learning machine

Wei

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

A fault diagnosis method combined with LMD, sample entropy and energy ratio for roller bearings

Cited by 157 publications

References 19 publications

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

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

An Image Enhancement Framework for Fault Diagnosis and Feature Extraction

Incipient loose detection of hoops for pipeline based on ensemble empirical mode decomposition and multi-scale entropy and extreme learning machine

Contact Info

Product

Resources

About