Determination of Vibration Sources by Using STFT

Dekýš, Vladimír; Kalman, Peter G.; Hanák, Pavel; Novák, Pavel; Stankovičová, Zuzana

doi:10.1016/j.proeng.2017.02.251

Cited by 16 publications

(7 citation statements)

References 8 publications

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“…Mathematical methods make it possible to obtain relevant results and information from measured data by applying generated algorithms of a given methodology using computer software. The output is a graphical representation of vibration signals in the time or frequency domain with the possible detection of adverse vibration phenomena [2,5,10].…”

Section: Mathematical Methods Used In Processingmentioning

confidence: 99%

“…The Short-term Fourier transformation is a time-frequency analytical tool for nonstationary signals. The transformation provides information about the signal f(t) and its spectrum F(ω) in the time-frequency window [2]. The principle of this method is to multiply the f(t) signal to be analysed by a certain type of symmetric window function (so-called window) ω*(t-τ) of constant length and then calculate the Fourier transform of the individual signal segments f).…”

Section: Short-time Fourier Transform (Stft)mentioning

confidence: 99%

See 1 more Smart Citation

Vibrodiagnostics of Rolling Ball Bearings Connected With Processing, Result´s Comparison and Prediction of Service Life

Šulka¹,

Sapietová²,

Bárnik³

2020

SJSUT.ST

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This article deals with vibration analysis and the processing of the vibration signal obtained during the experimental measurement on a rotary shaft at start-up, constant speed and run-out, with rolling ball bearings. The next step of the research is the introduction and application of mathematical and numerical methods for the processing of input and measured data using diagnostic and mathematical software. The principle of measurement and processing was to compare the results of oscillation of an undamaged bearing with several types of damaged bearings with corresponding deformations in the orbits/raceways. The purpose of this research and comparison was to achieve optimal results (in terms of amplitude values, the occurrence of eigenvalue vibrations and the resonance phenomenon) of individual bearings with corresponding deformations in the raceways and to obtain calibration diagrams as a tool for possible prediction of bearings in operation.

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Section: Mathematical Methods Used In Processingmentioning

confidence: 99%

Section: Short-time Fourier Transform (Stft)mentioning

confidence: 99%

Vibrodiagnostics of Rolling Ball Bearings Connected With Processing, Result´s Comparison and Prediction of Service Life

Šulka¹,

Sapietová²,

Bárnik³

2020

SJSUT.ST

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“…However, WT is limited by fixed wavelet basis functions and suffers from energy leakage in the signal decomposition process. Vladimir et al [17] used STFT to perform a dynamic analysis to identify the source of vibrations and their interactions in the determination of the contribution of vibration source in a mechanical system. Yu et al [18] analysed the nonlinear dynamic characteristics of ship rolling in waves using WT.…”

Section: Introductionmentioning

confidence: 99%

A new time-frequency analysis method based on single mode function decomposition for offshore wind turbines

et al. 2020

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The Hilbert-Huang transform (HHT) has been widely applied and recognised as a powerful time-frequency analysis method for nonlinear and non-stationary signals in numerous engineering fields. One of its major challenges is that the HHT is frequently subject to mode mixing in the processing of practical signals such as those of offshore wind turbines, as the frequencies of offshore wind turbines are typically close and contaminated by noise. To address this issue, this paper proposes a new time-frequency analysis method based on single mode function (SMF) decomposition to overcome the mode mixing problem in the structural health monitoring (SHM) of offshore wind turbines. In this approach, the structural vibration signal is first decomposed into a set of window components using complex exponential decomposition. A state-space model is introduced in the signal decomposition to improve the numerical stability of the decomposition, and then a novel window-alignment strategy, named energy gridding, is proposed and the signals are constructed in the corresponding gridding. Furthermore, energy recollection is implemented in each gridding, and the reassembling of these components yields an SMF that is comparable to the intrinsic mode function (IMF) of the HHT, but with a significant improvement in terms of mode mixing. Four case studies are conducted to evaluate the performance of the proposed method. The first case attempts to detect three different frequencies in a simulated time-invariant signal. The second case attempts to test a synthesised signal with segmental time-varying frequencies (each segment contains three different frequencies components). The analysis results in these two cases indicate that mode mixing can be reduced by the proposed method. Furthermore, a synthesised signal with slowly varying frequencies is used. These analysis results demonstrate the effective suppression of non-relevant frequency components using SMF decomposition. In the third case, the experimental data from vortex-induced vibration (VIV) experiments sponsored by the Norwegian Deepwater Programme (NDP) are used to evaluate the proposed SMF decomposition for vibration mode identification. In the final case, field data acquired from an offshore wind turbine foundation 1 and offshore wind turbine are analysed. The mode identification results obtained using SMF decomposition are compared with those produced by the HHT. The comparison demonstrates superior performance of the proposed method in identifying the vibration modes of the VIV experimental and field data.

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“…Moreover, the meshing vibrations of multiple pairs of gear in planetary gearbox are coupled with each other, which makes the vibration response signal obtained from the gearbox housing non-stationary and nonlinear [5], [6]. Time-frequency analysis methods such as Wigner-Ville distribution (WVD) [7], short time Fourier transform (STFT) [8], and discrete wavelet transform (DWT) [9] are powerful tools for analyzing nonstationary and non-linear signals. However, these methods have their own limitations.…”

Section: Introductionmentioning

confidence: 99%

Early Fault Diagnosis for Planetary Gearbox Based on Adaptive Parameter Optimized VMD and Singular Kurtosis Difference Spectrum

Wang

Huang

2019

IEEE Access

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Variational mode decomposition (VMD) is widely used in the condition monitoring and fault diagnosis of rotary machinery for its unique advantages. An adaptive parameter optimized VMD (APOVMD) is proposed in order to adaptively determine the suitable decomposed parameters and further enhance its performance. The traditional singular value decomposition (SVD) method cannot effectively select the reconstructed order, which often leads to unsatisfactory results for signal reconstruction. Thus, a singular kurtosis difference spectrum method is proposed to accurately determine the effective reconstructed order for signal noise reduction. In addition, because the fault signal of the planetary gearbox at the early fault stage is weak and susceptible to ambient noise and other signal interference, the fault feature information is difficult to extract. To address this issue, a novel method for early fault feature extraction of planetary gearbox based on APOVMD and singular kurtosis difference spectrum is proposed in this paper. First, the APOVMD is applied to decompose the planetary gearbox vibration signal into a series of band-limited intrinsic mode functions adaptively and non-recursively. Second, the sensitive component is selected from the IMFS according to the cosine similarity index. Third, the Hankel matrix is constructed for the sensitive component in the phase space and decomposed by SVD. Here, the effective reconstructed order is automatically selected by the singular kurtosis difference spectrum method for noise reduction. Finally, the Hilbert envelope spectrum analysis is carried out on the reconstructed signal, and the fault characteristic frequency information of planetary gearbox can be accurately extracted from the envelope spectrum to realize the fault identification and location. The results of simulation studies and actual experimental data analysis demonstrate that the proposed method has superior ability to extract the early weak fault characteristics of the planetary gearbox compared with the VMD-SVD and EEMD-SVD methods, and the validity and feasibility of the presented method are proved. INDEX TERMS Planetary gearbox, adaptive parameter optimized VMD, singular kurtosis difference spectrum, cosine similarity, early fault diagnosis.

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Determination of Vibration Sources by Using STFT

Cited by 16 publications

References 8 publications

Vibrodiagnostics of Rolling Ball Bearings Connected With Processing, Result´s Comparison and Prediction of Service Life

Vibrodiagnostics of Rolling Ball Bearings Connected With Processing, Result´s Comparison and Prediction of Service Life

A new time-frequency analysis method based on single mode function decomposition for offshore wind turbines

Early Fault Diagnosis for Planetary Gearbox Based on Adaptive Parameter Optimized VMD and Singular Kurtosis Difference Spectrum

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