Optimal Wavelet Selection for the Size Estimation of Manufacturing Defects of Tapered Roller Bearings with Vibration Measurement using Shannon Entropy Criteria

Deák, Krisztián; Mankovits, Tamás; Kocsis, Imre

doi:10.5545/sv-jme.2016.3989

Cited by 20 publications

(15 citation statements)

References 25 publications

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“…WPD can not only decompose the low-frequency part of the signal, but also decompose the high-frequency part. It is defined as follows [16]:…”

Section: Improvement Of Spectral Subtractionmentioning

confidence: 99%

Feature Enhancement Method for Drilling Vibration Signal by Using Wavelet Packet Multi-band Spectral Subtraction

Zhou¹,

Li²,

Liu³

2019

SV-JME

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To address the difficulty in extracting the features of vibration signals under intense background noise, a new method is proposed based on wavelet packet multi-band spectral subtraction to intensify vibration signal features in drilling processing. First, it is assumed that the spindle vibration signal of machine idling and the vibration signal caused when the tool cuts the workpiece are independent of each other, and the machine's idling signal is perceived as the 'additive noise' of the monitoring signal in light of the spectral subtraction principles. Secondly, in line with the characteristics of vibration signals in the drilling process, the 'additive noise' and monitoring signal are split into multiple frequency bands via wavelet packet decomposition. Eventually, spectral subtraction is performed independently in each band, and the vibration signals are reconstructed. The simulations and experimental results indicate that the new method should effectively eliminate the impact of environmental noise on the process of feature extraction to intensify the features of the monitoring signal. Highlights• A new method is proposed based on wavelet packet multi-band spectral subtraction to intensify vibration signal features in drilling processing. • We have addressed the difficulty in extracting the weak features of vibration signals under intense background noise. • Compared with the traditional threshold de-noising method, the problem of threshold selection can be avoided.

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“…WPD can not only decompose the low-frequency part of the signal, but also decompose the high-frequency part. It is defined as follows [16]:…”

Section: Improvement Of Spectral Subtractionmentioning

confidence: 99%

Feature Enhancement Method for Drilling Vibration Signal by Using Wavelet Packet Multi-band Spectral Subtraction

Zhou¹,

Li²,

Liu³

2019

SV-JME

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“…Yan and Gao used the Shannon entropy in relation to the signal energy of the wavelet coefficients to also determine which mother wavelet can separate the faulty bearing vibrations at a high signal energy level, with what they called the energy‐to‐entropy ratio . This selection method has further been used in wavelet transform analysis of bearing faults . No consensus of a singular choice of mother wavelet to use in bearing vibration monitoring from these studies has emerged, with the Morlet, Meyer, Shannon and Daubechies wavelets all being proposed.…”

Section: Introductionmentioning

confidence: 99%

“…21 This selection method has further been used in wavelet transform analysis of bearing faults. [22][23][24] No consensus of a singular choice of mother wavelet to use in bearing vibration monitoring from these studies has emerged, with the Morlet, Meyer, Shannon and Daubechies wavelets all being proposed. Also, of the studies referenced all have been performed using either test rig data or a precisely controlled test machine, in the form of a lathe.…”

Section: Introductionmentioning

confidence: 99%

Mother wavelet selection in the discrete wavelet transform for condition monitoring of wind turbine drivetrain bearings

et al. 2019

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Although the discrete wavelet transform has been used for diagnosing bearing faults for two decades, most work in this field has been done with test rig data. Since field data starts to be made more available, there is a need to shift into application studies. The choice of mother wavelet, ie, the predefined shape used to analyse the signal, has previously been investigated with simulated and test rig data without consensus of optimal choice in literature. Common between these investigations is the use of the wavelet coefficients' Shannon entropy to find which mother wavelet can yield the most useful features for condition monitoring. This study attempts to find the optimal mother wavelet selection using the discrete wavelet transform. Datasets from wind turbine gearbox accelerometers, consisting of enveloped vibration measurements monitoring both healthy and faulty bearings, have been analysed. The bearing fault frequencies' excitation level has been analysed with 130 different mother wavelets, yielding a definitive measure on their performance. Also, the applicability of Shannon entropy as a ranking method of mother wavelets has been investigated. The results show the discrete wavelet transforms ability to identify faults regardless of mother wavelet used, with the excitation level varying no more than 4%. By analysing the Shannon entropy, broad predictions to the excitation level could be drawn within the mother wavelet families but no direct correlation to the main results. Also, the high computational effort of high order Symlet wavelets, without increased performance, makes them unsuitable.

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“…At present, the time-frequency and multiresolution characteristics of wavelet transform are mainly utilized for signal denoising. A denoising method based on wavelets has been widely applied in engineering, especially wavelet threshold denoising methods [11] to [13].…”

Section: Introductionmentioning

confidence: 99%

A Fault Feature Extraction Method for Gearbox with Composite Gear Train Based on EEMD and Translation-Invariant Multiwavelets Neighboring Coefficients

Pang¹,

Cheng²,

Zhao-jian³

et al. 2018

SV-JME

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Although gearboxes with composite gear trains have been widely used in industrial production, it remains difficult to extract their fault signal features due to relatively complex vibration signals. This paper proposes an effective fault feature extraction method based on ensemble empirical mode decomposition (EEMD) and translation-invariant multiwavelet neighbouring coefficients, through which a clear envelope spectrum of gearbox vibration signals can be obtained. Compared with EEMD denoising or translation-invariant multiwavelet denoising using neighbouring coefficients alone, the method combining both of the denoising approaches can not only effectively suppress the signal noise but also fully retain the fault feature information. The presented method was further experimentally verified using a test rig for a gearbox with a composite gear train. Fault diagnosis was conducted with a single fault, such as snaggletooth and abrasion, as well as mixed faults at different locations. The results have shown that this method can effectively extract the fault features and improve the fault detection rate of a gearbox with a composite gear train. Highlights• This paper proposes a fault feature extraction method based on EEMD and translation-invariant multiwavelet neighbouring coefficients. • This method can be used for gearboxes with composite gear trains. • This method was verified in a test rig with a composite gear train. • The results showed this method is suitable not only for single fault but also for mixed faults.

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Optimal Wavelet Selection for the Size Estimation of Manufacturing Defects of Tapered Roller Bearings with Vibration Measurement using Shannon Entropy Criteria

Cited by 20 publications

References 25 publications

Feature Enhancement Method for Drilling Vibration Signal by Using Wavelet Packet Multi-band Spectral Subtraction

Feature Enhancement Method for Drilling Vibration Signal by Using Wavelet Packet Multi-band Spectral Subtraction

Mother wavelet selection in the discrete wavelet transform for condition monitoring of wind turbine drivetrain bearings

A Fault Feature Extraction Method for Gearbox with Composite Gear Train Based on EEMD and Translation-Invariant Multiwavelets Neighboring Coefficients

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