Detrended Fluctuation Analysis and Hough Transform Based Self-Adaptation Double-Scale Feature Extraction of Gear Vibration Signals

Wang, Jiaqing; Xiao, Han; Lv, Yong; Wang, Tao; Zhang, Xu

doi:10.1155/2016/3409897

Cited by 9 publications

(11 citation statements)

References 22 publications

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“…The specified peak frequencies of both the real part and imaginary part of admittance were extracted as the feature parameter for the bolt preload. In order to obtain a more accurate specified peak frequency, a polynomial fit was performed and the peak value of the fitting curve was used as the relevant feature frequency [41,42].…”

Section: The Experimental Results and Analysismentioning

confidence: 99%

Bolt Looseness Detection Based on Piezoelectric Impedance Frequency Shift

et al. 2016

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Abstract:In this paper, a piezoelectric impedance frequency shift method is developed to estimate the bolt preload for the detection of bolt looseness in engineering structures. An experimental device that allows the precision control of the axial preload force on a bolt is designed and fabricated. A universal testing machine is used to preload accurately on the bolt in the experiments. Under different bolt preload conditions, the impedance analyzer measures the admittance (inverse of the impedance) signal of the PZT (Lead ZirconateTitanate) patches which are bonded on the bolt head. Firstly, a wide frequency band is swept to find a sensitive frequency band of the piezoelectric admittance with the imaginary part. Then in the sensitive frequency band, a specified peak frequency of the admittance signature is chosen to investigate the frequency shift with different bolt preloads. The relationship between the specified frequency shift and the bolt preload is established. The experimental results show that the specified peak frequency decreases as the bolt preload increases for both M16 and M12 bolts, and the frequency shift has a linear relationship with the preload on the bolt. The frequencies of the real and imaginary parts of the admittance signature have the same results. Therefore, the bolt preload can be determined by measuring the specified frequency shift and this method has a good application prospect.

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Section: The Experimental Results and Analysismentioning

confidence: 99%

Bolt Looseness Detection Based on Piezoelectric Impedance Frequency Shift

et al. 2016

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“…In our research, the detrended order is one, which has been veri ed by experiment test and has better performance in [32]. e range of window sizes is from 8 to 512 sample points.…”

Section: Analysis and Comparison Of Vmd-dfa And Sts-dfamentioning

confidence: 89%

“…Furthermore, it avoids the spurious detection of correlations which are artifacts of nonstationary time series [19]. us, DFA method is applied into many fields, such as climate [20], heart rate dynamic [21][22][23], and mechanical engineering [24][25][26][27][28][29][30][31][32]. Lin and Chen claimed the valuable crossover properties of the scale exponents corresponding to different time scales in double logarithmic chart [26].…”

Section: Introductionmentioning

confidence: 99%

“…In our research, DFA is used to extract fault feature rather than evaluate the number of mode components of VMD, and then the main purpose in our work is the fault characteristics extraction, which is different from previous research. Wang et al [32] proposed a method which combined the scale exponent with intercept in DFA double logarithmic map which used the small time scale to classify the fault pattern of gears. We term this method as small time scale DFA (STS-DFA).…”

Section: Introductionmentioning

confidence: 99%

“…We term this method as small time scale DFA (STS-DFA). STS-DFA shows better performance of fault pattern recognization because of the fractal features of small time scale, which represents the local fluctuation as well as high-frequency component [32]. However, the characteristics of different fault modes have partial overlap because fluctuations corresponding to large time scales may affect local fluctuations corresponding to small time scales, which reduce accuracy of fault identification.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Detrended Fluctuation Analysis Method for Gear Fault Diagnosis Based on Variational Mode Decomposition

Xiao

2018

Shock and Vibration

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The vibration signal of heavy gearbox has the nonlinear and nonstationary characteristic, which makes the gear fault diagnosis difficult. Moreover, the useful fault information is mainly focused on the high-frequency components of the raw signal, which also affects the fault feature extraction from vibration signal. For this reason, a novel signal processing method based on variational mode decomposition (VMD) and detrended fluctuation analysis (DFA) is proposed to diagnose the gear faults of heavy gearbox. Since high-frequency component contains more fault information, the raw vibration signal is decomposed several mode components by VMD, which can remove the low-frequency component to retain the high-frequency component. Moreover, the most sensitive mode component is selected in these high-frequency components by a maximal indicator, which is composed of kurtosis and correlation coefficient. The most sensitive mode component is calculated by DFA to obtain bi-logarithmic map, and the sliding windowing algorithm is employed to capture turning point of the bi-logarithmic map, thus extracting the fault feature of small time scale to identify gear faults. The effectiveness of the proposed method for fault diagnosis is validated by experimental data analysis, and the comparison results demonstrate that the recognition rate of gear faults condition have marked improvement by proposed method than the DFA of small time scale (STS-DFA) and EMD-DFA.

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Multisensor signal denoising based on matching synchrosqueezing wavelet transform for mechanical fault condition assessment

Huang

et al. 2018

Meas. Sci. Technol.

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Since it is difficult to obtain the accurate running status of mechanical equipment with only one sensor, multisensor measurement technology has attracted extensive attention. In the field of mechanical fault diagnosis and condition assessment based on vibration signal analysis, multisensor signal denoising has emerged as an important tool to improve the reliability of the measurement result. A reassignment technique termed the synchrosqueezing wavelet transform (SWT) has obvious superiority in slow time-varying signal representation and denoising for fault diagnosis applications. The SWT uses the time–frequency reassignment scheme, which can provide signal properties in 2D domains (time and frequency). However, when the measured signal contains strong noise components and fast varying instantaneous frequency, the performance of SWT-based analysis still depends on the accuracy of instantaneous frequency estimation. In this paper, a matching synchrosqueezing wavelet transform (MSWT) is investigated as a potential candidate to replace the conventional synchrosqueezing transform for the applications of denoising and fault feature extraction. The improved technology utilizes the comprehensive instantaneous frequency estimation by chirp rate estimation to achieve a highly concentrated time–frequency representation so that the signal resolution can be significantly improved. To exploit inter-channel dependencies, the multisensor denoising strategy is performed by using a modulated multivariate oscillation model to partition the time–frequency domain; then, the common characteristics of the multivariate data can be effectively identified. Furthermore, a modified universal threshold is utilized to remove noise components, while the signal components of interest can be retained. Thus, a novel MSWT-based multisensor signal denoising algorithm is proposed in this paper. The validity of this method is verified by numerical simulation, and experiments including a rolling bearing system and a gear system. The results show that the proposed multisensor matching synchronous squeezing wavelet transform (MMSWT) is superior to existing methods.

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Detrended Fluctuation Analysis and Hough Transform Based Self-Adaptation Double-Scale Feature Extraction of Gear Vibration Signals

Cited by 9 publications

References 22 publications

Bolt Looseness Detection Based on Piezoelectric Impedance Frequency Shift

Bolt Looseness Detection Based on Piezoelectric Impedance Frequency Shift

A Novel Detrended Fluctuation Analysis Method for Gear Fault Diagnosis Based on Variational Mode Decomposition

Multisensor signal denoising based on matching synchrosqueezing wavelet transform for mechanical fault condition assessment

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