Application of Optimal Morlet Wavelet Filter for Bearing Fault Diagnosis

Morsy, Mohamed El; Achtenova, Gabriela

doi:10.4271/2015-01-2178

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…The Morlet wavelet is a wavelet composed of a complex exponential Gaussian window (envelope). Morsy et al [111] apply the optimal Morlet wavelet filter to preprocess the signal, and envelope detection was used to identify bearing failures at early stage. Yiakopoulos [112] proposed a feature extraction method combining the morphological analysis and complex shifted Morlet wavelets for bearing fault diagnosis.…”

Section: Wavelet Transform Methodsmentioning

confidence: 99%

“…Fan et al [95] Wavelet transform He et al [96] Wavelet transform Cui et al [97] Wavelet transform + time-frequency analysis + blind source Separation theory Morsy et al [111] Morlet wavelet Filter + envelope detection Yiakopoulos [112] Morphological + Complex Shifted Morlet Wavelets. Cui et al [98] High-frequency characteristics + self-adaptive wavelet de-noising Wang et al [114] Complex Morlet wavelet coefficients + sparsity measurement Tse et al [109] Wavelet transform + envelope analysis Wang et al [99] Adaptive wavelet stripping algorithm Morsy et al [113] Maximum Kurtosis + Morlet wavelet Combet et al [100] Wavelet bicoherence Moumene et al [101] Wavelets multiresolution analysis + the high-frequency resonance Fan et al [105] Discrete wavelet transform Karuppaiah et al [108] HAAR wavelet Rahman et al [106] Discrete wavelet transform Rangel-Magdaleno et al [107] Discrete wavelet transform + motor current signature analysis Chen et al [102] Adaptive redundant multiwavelet packet He et al [103] Adaptive multiwavelet Yang et al [110] EMD + autocorrelation de-noising + wavelet package decomposition Li et al [104] Intrinsic character-scale decomposition + tunable Q-factor wavelet transform.…”

Section: Authors Methodologiesmentioning

confidence: 99%

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A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Wei

et al. 2019

Entropy

169

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Rotating machinery is widely applied in various types of industrial applications. As a promising field for reliability of modern industrial systems, early fault diagnosis (EFD) techniques have attracted increasing attention from both academia and industry. EFD is critical to provide appropriate information for taking necessary maintenance actions and thereby prevent severe failures and reduce financial losses. A massive amounts of research work has been conducted in last two decades to develop EFD techniques. This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings. The main purpose of this paper is to serve as a guidemap for researchers in the field of early fault diagnosis. After a brief introduction of early fault diagnosis techniques, the applications of EFD of rotating machine are reviewed in two aspects: fault frequency-based methods and artificial intelligence-based methods. Finally, a summary and some new research prospects are discussed.

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Section: Wavelet Transform Methodsmentioning

confidence: 99%

Section: Authors Methodologiesmentioning

confidence: 99%

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Wei

et al. 2019

Entropy

169

View full text Add to dashboard Cite

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“…Jiang et al 22 applied a new denoising method based on the adaptive Morlet wavelet and singular value decomposition to extract the feature of wind turbine vibration signals. El Morsy and Achtenova 23 utilized the optimal Morlet wavelet filter and envelope detection to eliminate the background noise associated with interference vibrations. The Morlet wavelet parameters were optimized based on the maximum Kurtosis rule.…”

Section: Introductionmentioning

confidence: 99%

A fast filtering method based on adaptive impulsive wavelet for the gear fault diagnosis

Gao

Jia

2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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The useful fault features applied for the fault diagnosis are usually overwhelmed by noise and other interference factors in rotation machinery. The impulses masked in vibration signals can represent the faults of gears or bearings in a gearbox. The key to finding impulsive components is to identify the modeling parameters (such as damping ratio, central frequency) of a transient (Morlet wavelet, Laplace wavelet), which can be used as an adaptive filter to denoise the vibration signal. However, its engineering application is limited by the time-consuming computation. In order to tackle this issue, a fast algorithm based on an adaptive impulsive wavelet is proposed to filter the fault signal so that the fault characteristic frequency can be identified. Firstly, a correlation coefficient maximum criterion is employed to find one of the optimal parameters of the impulsive wavelet. Then, the other parameter is optimized by the minimum Shannon wavelet entropy criterion. Finally, the impulsive wavelet filter with optimal parameters is applied to extract the fault characteristic frequency. Simulation signals are applied to verify the efficiency of the proposed approach, and comparison analysis is conducted as well. Further, the proposed method is applied to detect the gear fault of a gearbox. The experimental results show that the proposed method is effective with high efficiency.

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“…Liu proposed a intelligent method for fault diagnosis of subway vehicle bogie [5]. EL Morsy used the optimal morlet wavelet filtering and envelope detection to analyze the experimental signals of the inner ring failure of rolling bearing [6]. Uddin S proposed a K-NN classification algorithm which can improve performance in bearing fault diagnosis [7].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Variation Process of Bearing Vibration Based on Non-ordered Gray Relation (Part II: Experiment)

XIA¹,

CHEN²,

Cheng³

et al. 2018

dtcse

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Abstract. In order to analyze the process of bearing vibration variation, the original vibration data of four kinds of damage diameters are grouped, and the intrinsic data sequence is selected after grouping. Then, the non-ordered gray relation between data sequence of different groups and the intrinsic data sequence is established and the gray confidence level is obtained. The gray confidence level is compared with the set threshold of 90%. Then the variation of the bearing vibration is evaluated. The correctness and feasibility of the proposed method are verified by comparing the assessment with the actual situation.

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Application of Optimal Morlet Wavelet Filter for Bearing Fault Diagnosis

Cited by 10 publications

References 20 publications

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

A fast filtering method based on adaptive impulsive wavelet for the gear fault diagnosis

Evaluation of the Variation Process of Bearing Vibration Based on Non-ordered Gray Relation (Part II: Experiment)

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