Detection and diagnosis of surface wear failure in a spur geared system using EEMD based vibration signalanalysis

Amarnath, M.; Krishna, I. R. Praveen

doi:10.1016/j.triboint.2013.01.001

Cited by 47 publications

(21 citation statements)

References 18 publications

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“…In [27,28], an improved version of the EMD is presented for fault diagnosis and modal parameter estimation, respectively. The EMD-based analysis for vibrations signals in other fields has been also reported in literature [29][30][31][32]. Despite the adaptive advantages of the EMD-based methods, some issues such as mode mixing, noise, and the lack of physical meaning for the decomposed modes can compromise their effectiveness [33].…”

Section: Related Literaturementioning

confidence: 95%

Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach

et al. 2020

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Transformers are vital and indispensable elements in electrical systems, and therefore, their correct operation is fundamental; despite being robust electrical machines, they are susceptible to present different types of faults during their service life. Although there are different faults, the fault of short-circuited turns (SCTs) has attracted the interest of many researchers around the world since the windings in a transformer are one of the most vulnerable parts. In this regard, several works in literature have analyzed the vibration signals that generate a transformer as a source of information to carry out fault diagnosis; however this analysis is not an easy task since the information associated with the fault is embedded in high level noise. This problem becomes more difficult when low levels of fault severity are considered. In this work, as the main contribution, the nonlinear mode decomposition (NMD) method is investigated as a potential signal processing technique to extract features from vibration signals, and thus, detect SCTs in transformers, even in early stages, i.e., low levels of fault severity. Also, the instantaneous root mean square (RMS) value computed using the Hilbert transform is proposed as a fault indicator, demonstrating to be sensitive to fault severity. Finally, a fuzzy logic system is developed for automatic fault diagnosis. To test the proposal, a modified transformer representing diverse levels of SCTs is used. These levels consist of 0 (healthy condition), 5, 10, 15, 20, and 25 SCTs. Results demonstrate the capability of the proposal to extract features from vibration signals and perform automatic fault diagnosis.

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Section: Related Literaturementioning

confidence: 95%

Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach

et al. 2020

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“…The traditional methods-such as fast Fourier transform, power spectrum and cepstrum analysis [7]-are primarily based on stationary signals, and have difficulty in analysing nonlinear phenomenon and in suppressing noise. Therefore, many researchers have investigated alternative signal analysis methods such as smoothed pseudo-Wigner Ville distribution [8], time synchronous averaging (TSA) [2,9], cyclo-stationary analysis [10], empiric mode decomposition (EMD) [11], ensemble empirical mode decomposition (EEMD) [12], wavelet analysis [13], and combined methods [1] to analyse vibration signals for a more accurate feature extraction. Although these efforts have shown promising results, they may be still lacking because these signal analysis methods possess limited noise reduction capability.…”

Section: Introductionmentioning

confidence: 99%

Gear Wear Process Monitoring Using a Sideband Estimator Based on Modulation Signal Bispectrum

Zhang

et al. 2017

Applied Sciences

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Abstract:As one of the most common gear failure modes, tooth wear can produce nonlinear modulation sidebands in the vibration frequency spectrum. However, limited research has been reported in monitoring the gear wear based on vibration due to the lack of tools which can effectively extract the small sidebands. In order to accurately monitor gear wear progression in a timely fashion, this paper presents a gear wear condition monitoring approach based on vibration signal analysis using the modulation signal bispectrum-based sideband estimator (MSB-SE) method. The vibration signals are collected using a run-to-failure test of gearbox under an accelerated test process. MSB analysis was performed on the vibration signals to extract the sideband information. Using a combination of the peak value of MSB-SE and the coherence of MSB-SE, the overall information of gear transmission system can be obtained. Based on the amplitude of MSB-SE peaks, a dimensionless indicator is proposed to assess the effects of gear tooth wear. The results demonstrated that the proposed indicator can be used to accurately and reliably monitor gear tooth wear and evaluate the wear severity.

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“…However, wavelet or harmonic wavelet de-noising is essentially a band-pass filter based applied on a Fourier transform with a time-variable window and is not self-adaptive in nature due to the fact different mother wavelets should be predefined for sub-bands. To address this challenge, empirical mode decomposition (EMD) and ensemble EMD (EEMD) were proposed and have shown their superiority over the wavelet and other traditional methods in many fault diagnosis applications, such as bearing fault diagnosis [10,11], gear fault diagnosis [12,13] and rotor fault diagnosis [14,15]. However, the EMD and EEMD still have some shortcomings, such as end effects and mixing problems [16,17] that still must be solved.…”

Section: Introductionmentioning

confidence: 99%

Incipient Fault Feature Extraction for Rotating Machinery Based on Improved AR-Minimum Entropy Deconvolution Combined with Variational Mode Decomposition Approach

Liang

2017

Entropy

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Aiming at the issue of extracting the incipient single-fault and multi-fault of rotating machinery from the nonlinear and non-stationary vibration signals with a strong background noise, a new fault diagnosis method based on improved autoregressive-Minimum entropy deconvolution (improved AR-MED) and variational mode decomposition (VMD) is proposed. Due to the complexity of rotating machinery systems, the periodic transient impulses of single-fault and multiple-faults always emerge in the acquired vibration signals. The improved autoregressive minimum entropy deconvolution (AR-MED) technique can effectively deconvolve the influence of the background noise, which aims to enhance the peak value of the multiple transient impulses. Nevertheless, the envelope spectrum of simulation and experimental in this work shows that there are many interference components exist on both left and right of fault characteristic frequencies when the background noise is strong. To overcome this shortcoming, the VMD is thus applied to adaptively decompose the filtered output vibration signal into a number of quasi-orthogonal intrinsic modes so as to better detect the single-and multiple-faults via those sub-band signals. The experimental and engineering application results demonstrate that the proposed method dramatically sharpens the fault characteristic frequencies (FCFs) from the impacts of bearing outer race and gearbox faults compared to the traditional methods, which show a significant improvement in early incipient faults of rotating machinery.

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Detection and diagnosis of surface wear failure in a spur geared system using EEMD based vibration signalanalysis

Cited by 47 publications

References 18 publications

Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach

Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach

Gear Wear Process Monitoring Using a Sideband Estimator Based on Modulation Signal Bispectrum

Incipient Fault Feature Extraction for Rotating Machinery Based on Improved AR-Minimum Entropy Deconvolution Combined with Variational Mode Decomposition Approach

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